DIVERSIDAD FUNCIONAL Y DIVERSIDAD FILOGENÉTICA EN LOS...

TESIS DOCTORAL

ELIZABETH GUSMÁN MONTALVÁN

DIVERSIDAD FUNCIONAL Y DIVERSIDAD

FILOGENÉTICA EN LOS BOSQUES SECOS DEL SUR

DEL ECUADOR.

UNIVERSIDAD POLITÉCNICA DE MADRID

E.T.S.I. AGRONÓMICA, AGROALIMENTARIA Y DE BIOSISTEMAS

DEPARTAMENTO DE BIOTECNOLOGÍA-BIOLOGÍA VEGETAL

TESIS DOCTORAL

DIVERSIDAD FUNCIONAL Y DIVERSIDAD FILOGENÉTICA EN LOS BOSQUES

SECOS DEL SUR DEL ECUADOR.

Autor: Elizabeth del Carmen Gusmán Montalván1.

Directores: Dr. Marcelino De la Cruz Rot2. Doctor en Ciencias Biológicas

Dr. Adrián Escudero Alcantara2. Doctor en Ciencias Biológicas

1Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja. Ecuador.

2Área de Biodiversidad y Conservación. Departamento de Biología y Geología, ESCET,

Universidad Rey Juan Carlos.

Madrid, 2015

I

Marcelino de la Cruz Rot, y Adrián Escudero Alcántara, Profesores Titulares del

Departamento de Biología y Geología de la Universidad Rey Juan Carlos

CERTIFICAN:

Que los trabajos de investigación desarrollados en la memoria de la tesis doctoral

“Diversidad funcional y Diversidad filogenética en los Bosques Secos del Sur del

Ecuador”, son aptos para ser presentados por Elizabeth Gusmán Montalván ante el tribunal

que en su día se consigne, para aspirar al Grado de Doctor por la Universidad Politécnica de

Madrid.

Vo.Bo. Director de Tesis VoBo Co-Director de Tesis

Dr. Marcelino de la Cruz Rot Dr. Adrián Escudero Alcántara

III

Tribunal nombrado por el Mgfco. y Exmo. Sr. Rector de la Universidad Politécnica de

Madrid, el día ………………. de ……………………………………………….2015.

Presidente: ……………………………………………………………………………………

Secretario: ……………………………………………………………………………………

Vocal: …………………………………………………………………………………………

Vocal: …………………………………………………………………………………………

Vocal: …………………………………………………………………………………………

Suplente: ……………………………………………………………………………………...

Suplente: ……………………………………………………………………………………...

Realizado el acto de defensa y lectura de Tesis el día………….……de

……………………...de 2015 en la E.T.S.I. Ingenieros Agrónomos.

Calificación …………………………………………………………………………………...

……………………………..

EL PRESIDENTE

………………………………..

LOS VOCALES

……………………………….

EL SECRETARIO

V

A mis Padres y hermanos por ser la razón de mi vida.

RESUMEN ........................................................................................................... 1

ABSTRACT ....................................................................................................... 7

INTRODUCCIÓN GENERAL........................................................................ 11

2.1. REGLAS DE ENSAMBLAJE EN LA COMUNIDAD ......................... 13

2.2. OBJETIVOS ............................................................................................ 20

2.3. ÁREA DE ESTUDIO Y METODOLOGÍA GENERAL ....................... 23

CAPÍTULO 1 ..................................................................................................... 36

Environmental variation mediates habitat filtering and limiting similarity in a

Dry Neotropical forest. .................................................................................... 36

CAPÍTULO 2 ..................................................................................................... 66

Linking functional and phylogenetic diversities with assembly processes in a

dry neo tropical forest ...................................................................................... 66

CAPÍTULO 3 ..................................................................................................... 95

¿Actúan los mecanismos de ensamblaje a diferentes escalas espaciales en un

Bosque Seco? ................................................................................................... 95

CAPÍTULO 4 ................................................................................................... 121

Mechanisms of community assemblage at level taxonomic, functional and

phylogenetic in a scale spatial......................................................................... 121

CONCLUSIONES GENERALES ................................................................. 147

AGRADECIMIENTOS .................................................................................. 153

ÍNDICE

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RESUMEN

Valle de Bursera graveolens (palo santo) en época lluviosa (Zapotillo-Ecuador)

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El concepto tradicional de reglas de ensamblaje refleja la idea de que las especies no co-

ocurren al azar sino que están restringidos en su co-ocurrencia por la competencia

interespecífica o por un filtrado ambiental. En está tesis abordé la importancia de los

procesos que determinan el ensamble de la comunidad en la estructuración de los Bosques

Secos en el Sur del Ecuador.

Este estudio se realizó en la región biogeográfica Tumbesina, donde se encuentra la mayor

concentración de bosques secos tropicales bien conservados del sur de Ecuador, y que

constituyen una de las áreas de endemismo más importantes del mundo. El clima se

caracteriza por una estación seca que va desde mayo a diciembre y una estación lluviosa de

enero a abril, su temperatura anual varía entre 20°C y 26°C y una precipitación promedio

anual entre 300 y 700 mm.

Mi primer tema fue orientado a evaluar si la distribución de los rasgos funcionales a nivel

comunitario es compatible con la existencia de un filtro ambiental (filtrado del hábitat) o con

la existencia de un proceso de limitación de la semejanza funcional impuesta por la

competencia inter-específica entre 58 especies de plantas leñosas repartidas en 109 parcelas

(10x50m). Para ello, se analizó la distribución de los valores de cinco rasgos funcionales

(altura máxima, densidad de la madera, área foliar específica, tamaño de la hoja y de masa de

la semilla), resumida mediante varios estadísticos (rango, varianza, kurtosis y la desviación

estándar de la distribución de distancias funcionales a la especies más próxima) y se comparó

con la distribución esperada bajo un modelo nulo con ausencia de competencia. Los

resultados obtenidos apoyan que tanto el filtrado ambiental como la limitación a la semejanza

afectan el ensamble de las comunidades vegetales de los bosques secos Tumbesinos.

Un segundo tema fue identificar si la diversidad funcional está condicionada por los

gradientes ambientales, y en concreto si disminuye en los ambientes más estresantes a causa

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del filtrado ambiental, y si por el contrario aumenta en los ambientes más benignos donde la

competencia se vuelve más importante, teniendo en cuenta las posibles modificaciones a este

patrón general a causa de las interacciones de facilitación. Para abordar este estudio

analizamos tanto las variaciones en la diversidad funcional (respecto a los de los cinco rasgos

funcionales empleados en el primer capítulo de la tesis) como las variaciones de diversidad

filogenética a lo largo de un gradiente de estrés climático en los bosques tumbesinos, y se

contrastaron frente a las diversidades esperadas bajo un modelo de ensamblaje

completamente aleatorio de la comunidad.

Los análisis mostraron que tan sólo la diversidad de tamaños foliares siguió el patrón de

variación esperado, disminuyendo a medida que aumentó el estrés abiótico mientras que ni el

resto de rasgos funcionales ni la diversidad funcional multivariada ni la diversidad

filogenética mostraron una variación significativa a lo largo del gradiente ambiental.

Un tercer tema fue evaluar si los procesos que organizan la estructura funcional de la

comunidad operan a diferentes escalas espaciales. Para ello cartografié todos los árboles y

arbustos de más de 5 cm de diámetro en una parcela de 9 Ha de bosque seco y caractericé

funcionalmente todas las especies. Dicha parcela fue dividida en subparcelas de diferente

tamaño, obteniéndose subparcelas a seis escalas espaciales distintas. Los resultados muestran

agregación de estrategias funcionales semejantes a escalas pequeñas, lo que sugiere la

existencia bien de filtros ambientales actuando a escala fina o bien de procesos competitivos

que igualan la estrategia óptima a dichas escalas.

Finalmente con la misma información de la parcela permanente de 9 Ha. Nos propusimos

evaluar el efecto y comportamiento de las especies respecto a la organización de la

diversidad taxonómica, funcional y filogenética. Para ello utilicé tres funciones sumario

espaciales: ISAR- para el nivel taxonómico, IFDAR para el nivel funcional y IPSVAR para

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el nivel filogenética y las contrastamos frente a modelos nulos que describen la distribución

espacial de las especies individuales. Los resultados mostraron que en todas las escalas

espaciales consideradas para ISAR, IFDAR y IPSVAR, la mayoría de las especies se

comportaron como neutras, es decir, que están rodeados por la riqueza de diversidad

semejante a la esperada. Sin embargo, algunas especies aparecieron como acumuladoras de

diversidad funcional y filogenética, lo que sugiere su implicación en procesos competitivos

de limitación de la semejanza. Una pequeña proporción de las especies apareció como

repelente de la diversidad funcional y filogenética, lo que sugiere su implicación en un

proceso de filtrado de hábitat. En este estudio pone de relieve cómo el análisis de las

dimensiones alternativas de la biodiversidad, como la diversidad funcional y filogenética,

puede ayudarnos a entender la co-ocurrencia de especies en diversos ensambles de

comunidad.

Todos los resultados de este estudio aportan nuevas evidencias de los procesos de ensamblaje

de la comunidad de los Bosques Estacionalmente secos y como las variables ambientales y la

competencia juegan un papel importante en la estructuración de la comunidad.

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ABSTRACT

The traditional concept of the rules assembly for species communities reflects the idea that

species do not co-occur at random but are restricted in their co-occurrence by interspecific

competition or an environmental filter. In this thesis, I addressed the importance of the se

processes in the assembly of plant communities in the dry forests of southern Ecuador.

This study was conducted in the biogeographic region of Tumbesina has the largest

concentration of well-conserved tropical dry forests of southern Ecuador, and is recognized

as one of the most important areas of endemism in the world. The climate is characterized by

a dry season from May to December and a rainy season from January to April. The annual

temperature varies between 20 ° C and 26 ° C and an average annual rainfall between 300

and 700 mm.

I first assessed whether the distribution of functional traits at the level of the community is

compatible with the existence of an environmental filter (imposed by habitat) or the existence

of a limitation on functional similarity imposed by interspecific competition. This analysis

was conducted for 58 species of woody plants spread over 109 plots of 10 x 50 m.

Specifically, I compared the distribution of values of five functional traits (maximum height,

wood density, specific leaf area, leaf size and mass of the seed), via selected statistical

properties (range, variance, kurtosis and analyzed the standard deviation of the distribution of

the closest functional species) distances and compared with a expected distribution under a

null model of no competition. The results support that both environmental filtering and a

limitation on trait similarity affect the assembly of plant communities in dry forests

Tumbesina.

8

My second chapter evaluated whether variation in functional diversity is conditioned by

environmental gradients. In particular, I tested whether it decreases in the most stressful

environments because of environmental filters, or if, on the contrary, functional diversity is

greater in more benign environments where competition becomes more important

(notwithstanding possible changes to this general pattern due to facilitation). To address this

theme I analyzed changes in both the functional diversity (maximum height, wood density,

specific leaf area, leaf size and mass of the seed) and the phylogenetic diversity, along a

gradient of climatic stress in Tumbes forests. The observed patterns of variation were

contrasted against the diversity expected under a completely random null model of

community assembly.

Only the diversity of leaf sizes followed the hypothesis decreasing in as trait variation abiotic

stress increased, while the other functional traits multivariate functional diversity and

phylogenetic diversity no showed significant variation along the environmental gradient.

The third theme assess whether the processes that organize the functional structure of the

community operate at different spatial scales. To do this I mapped all the trees and shrubs of

more than 5 cm in diameter within a plot of 9 hectares of dry forest and functionally

classified each species. The plot was divided into subplots of different sizes, obtaining

subplots of six different spatial scales. I found aggregation of similar functional strategies at

small scales, which may indicate the existence of environmental filters or competitive

processes that correspond to the optimal strategy for these fine scales.

Finally, with the same information from the permanent plot of 9 ha, I evaluated the effect and

behavior of individual species on the organization of the taxonomic, functional and

phylogenetic diversity. The analysis comprised three spatial summary functions: ISAR- for

taxonomic level analysis, IFDAR for functional level analysis, and IPSVAR for phylogenetic

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level analysis, in each case the pattern of diversity was contrasted against null models that

randomly reallocate describe the spatial distribution of individual species and their traits. For

all spatial scales considering ISAR, IFDAR and IPSVAR, most species behaved as neutral,

i.e. they are surrounded by the diversity of other traits similar to that expected under a null

model. However, some species appeared as accumulator of functional and phylogenetic

diversity, suggesting that they may play a role in competitive processes that limiting

similarity. A small proportion of the species appeared as repellent of functional and

phylogenetic diversity, suggesting their involvement in a process of habitat filtering. These

analysis highlights that the analysis of alternative dimensions of biodiversity, such as

functional and phylogenetic diversity, can help us understand the co-occurrence of species in

the assembly of biotic communities.

All results of this study provide further evidence of the processes of assembly of the

community of the seasonally dry forests as environmental variables and competition play an

important role in structuring the community.

Diversidad funcional y filogenética en los Bosques Secos del Sur del Ecuador

11

INTRODUCCIÓN GENERAL

Valle de Bursera graveolens (palo santo) en época seca (Zapotillo-Ecuador)


12


13

2.1. REGLAS DE ENSAMBLAJE EN LA COMUNIDAD

La identificación de los factores que determinan la distribución de las especies y los

mecanismos que regulan su organización en ensambles de la comunidad son un objetivo

central en la ecología vegetal (Crawley 1986), los científicos han buscado reglas o normas

generales que expliquen la distribución de las especies en todas las regiones biogeográficas

(Götzenberger et al 2012). La teoría de biogeografía de islas fue una de las primeras teorías

ecológicas basadas en la idea de que el conjunto de especies en las comunidades están

controladas por normas ecológicas (MacArthur and Wilson 1967). Sin embargo, el término

"regla o norma de ensamblaje" fue presentado por primera vez por Jared Diamond (1975) que

llegó a la conclusión de que las interacciones inter-específicas, y en particular la

competencia, conducen a patrones de co-ocurrencia no aleatoria de las especies, desde

entonces el concepto de estás normas de ensamblaje han sido un tema central en la ecología y

se aplica a una variedad de fenómenos y organismos (Weiher and Keddy 1995).

Según Keddy (1992) un enfoque dominante para estudiar el ensamble de la comunidad es el

análisis de cómo los rasgos funcionales se distribuyen, generalmente los rasgos funcionales

(o traits en inglés) son características morfológicas, fisiológicas y/o fenológicas medibles de

un organismo, y son consideradas funcionales si tiene un impacto en el crecimiento,

reproducción y supervivencia del individuo (Lavorel and Garnier 2002; Cornelissen et ál.

2003; McGill et al. 2006; Violle et al. 2007). Dentro de este enfoque se han propuesto dos

mecanismos principales para explicar el papel de los rasgos funcionales de las plantas,

primero Filtrado de hábitat- selecciona un conjunto de especies con rasgos funcionales

similares este proceso de filtrado conduce a una escasa dispersión en los valores de los rasgos

dentro de las comunidades, es decir, estos filtros abióticos llevan a una distribución de


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caracteres convergentes o de 'baja dispersión' (Weiher et al. 1995; Cornwell et al. 2006;

Grime 2006).

Segundo el Límite de similitud o diferenciación de nicho impide que las especies que

coexisten sean ecológicamente demasiado similares y conduce a un exceso de dispersión de

los valores de los rasgos dentro de las comunidades (MacArthur y Levin 1967; Weiher y

Keddy 1.995; Grime 2006). Se espera que la competencia pueda crear una distribución

divergente o 'sobre dispersión' de los valores del rasgo entre las especies localmente

concurrentes (MacArthur and Levin 1967).

De acuerdo con una está línea de razonamiento, se espera que los patrones más fuertes de

filtrado en los rasgos funcionales se de en condiciones muy duras (por ejemplo frío o seco),

debido a la fuerte filtrado abiótico (Weiher and Keddy 1995; Cornwell et al. 2006), mientras

que la alta divergencia del rasgo se espera en los hábitats más competitivos y productivos de

acuerdo a la hipótesis de limite de similitud (MacArthur and Levin 1967; Wilson and Stubbs

2012).

Entre la gran cantidad de rasgos medibles sobre un individuo, los de interés para la ecología

funcional deben cumplir al menos cuatro condiciones (Lavorel et al., 2007). Ellos deben: (i)

tener alguna relación con la función de la planta; (ii) ser relativamente fáciles de observar y

rápidos de cuantificar, (iii) ser medibles utilizando protocolos estandarizados; y (iv) tener una

clasificación coherente (Garnier et al, 2001a; Cornelissen et al, 2003a; Mokany et al. 2008).

La mayoría de los rasgos medidos en esta tesis están directamente relacionados con procesos

en el crecimiento, funcionamiento de la planta (McGill et al 2006; Lavorel et al., 2007; Violle

et al 2007) o de su entorno (Lavorel and Garnier 2002).


15

Debido a los vínculos directos entre los rasgos y el funcionamiento de los organismos, la

distribución de rasgos ofrecen una visión de gran alcance en cómo las comunidades se

ensamblan y cómo influyen en los procesos de los ecosistemas (Cornwell y Ackerly 2009,

Díaz et al. 2001). Dado que los rasgos fundamentales que determinan el ensamble de la

comunidad y los procesos de los ecosistemas pueden ser compartidos entre las comunidades

de todo el mundo (Díaz et al. 2004). Entre los más utilizados en los estudios del ensamble de

comunidades tenemos la altura máxima, la densidad de la madera, tamaño de la hoja, área

foliar específica y masa seca de la semilla.

Desde una perspectiva basada en el rasgo, una comunidad puede estar caracterizada por la

distribución de los rasgos funcionales de los individuos que la componen (Ackerly 2003).

Debido a los vínculos directos entre los rasgos y el funcionamiento de los organismos, la

distribución de caracteres ofrecen una visión a gran alcance en ¿cómo las comunidades se

ensamblan y cómo influyen en los procesos de los ecosistemas? (Cornwell y Ackerly 2009,

Díaz et al. 2001). Dado que los rasgos fundamentales que determinan el ensamble de la

comunidad y procesos de los ecosistemas pueden ser compartidos entre las comunidades de

todo el mundo (Díaz et al. 2004), el conocimiento obtenido a través de los enfoques basados

en los rasgos puede ayudar a entender y (McGill et al. 2006), de mejor manera la ecología de

las comunidades.

Enfoques basados en el rasgo en ecología de comunidades vegetales han cobrado impulso en

los últimos 15 años, y se han hecho muchos progresos como el desarrollo de listas de rasgos

funcionales claves que definen aspectos independientes de las estrategias de las plantas

(Weiher et al. 1999), la medición de los rasgos de un gran número de especies y la

compilación de bases de datos de rasgos globales (Kattge et al. 2011), que documentan los

cambios en la distribución de caracteres funcionales a través de gradientes ambientales en el


16

espacio y el tiempo (Fonseca et al. 2000; Wright et al. 2004; Garnier et al. 2004; Kraft et al.

2008).

Aproximaciones actuales en Bosque Seco

A pesar del interés de los estudios basados en los rasgos funcionales en la ecología, la

mayoría de los estos hacen hincapié en los patrones de rasgos en los ecosistemas de bosques

húmedos templados o tropicales. Existiendo pocos estudios relacionados con los patrones de

diversidad con un enfoque en rasgos funcionales en los Bosques tropicales estacionalmente

secos (BTES) u otros ecosistemas limitados por la disponibilidad agua (Chaturvedi et al.

2011, Hulshof et al 2013). La disponibilidad de agua sin lugar a dudas es uno de los factores

limitantes más importantes en los BTES, siendo crítica para el establecimiento, supervivencia

y desarrollo de las plantas (Ruthemberg 1980), condicionando tanto los gradientes espaciales

(Balvanera et al. 2011, Espinosa et al. 2011), procesos ecológicos básicos y las interacciones

bióticas que se establecen en cada bosque (Martínez-Yrizar et al. 1992; Mooney et al. 1993).

Además de la disponibilidad absoluta, la estacionalidad y la variación interanual de la

precipitación marcan la dinámica de las comunidades vegetales y la estructura florística en

los estos bosques secos (Blain and Kellman 1991; Murphy and Lugo 1995; Sampaio 1995).

El éxito de las especies en ocupar diferentes nichos dependerá de la capacidad para tolerar el

estrés hídrico y competir por el agua durante la sequía (Engelbrecht and Kursar 2003).

Mi investigación se oriento en identificar los procesos de ensamblaje de la comunidad

basados en rasgos funcionales en los Bosques Secos del Sur del Ecuador, con el fin principal

de Entender los diferentes procesos que intervienen en la estructuración de la comunidad de

Bosque Seco y conocer como estos procesos influyen en la diversidad funcional y

filogenética de estos bosques.


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El primer capítulo fue orientado a la teoría del ensamble de la comunidad que considera que

la distribución de los valores de los rasgos funcionales dentro de una comunidad están

condicionados por un “Filtrado” dado por las condiciones ambientales que restringe el rango

de estrategias viables en una comunidad, y que puede también estar dado por un reparto de

micro-sitios y recursos, que condiciona un limite, restringiendo la semejanza funcional de las

especies coexistentes (Cornwell y Ackerly 2009). Nosotros nos hemos propuesto responder si

la distribución de los valores de los rasgos funcionales en los Bosques Secos del Sur del

Ecuador puede tomarse como evidencia de un rango limitado (filtrado por el hábitat) y/o de

un espaciado regular (limite a la semejanza) predicho por la teoría del ensamblaje de las

comunidades, mediante el empleo de modelos nulos e identificando cual es la variable

ambiental predominante con la variación de los rasgos funcionales.

En el segundo capítulo nos hemos centrado en el estudio de la relación entre la diversidad

funcional y filogenética basado en la suposición razonable de que la diversificación evolutiva

ha generado la diversificación de rasgos (Webb 2000). Especies relacionadas (es decir, un

mismo género) son funcionalmente y ecológicamente más similares que especies alejadas

(Webb 2002). Si el filtrado de hábitat es el proceso comunitario las especies deberían ser

filogenéticamente mas relacionadas de lo esperado por azar (Webb et al. 2002; Cavender-

Bares et al. 2004; Losos 2008). Sin embargo, si la divergencia del rasgo es debido a los

procesos de limite de similitud entre las plantas, esto conduce a la predicción de una

estructura filogenético más dispersas, es decir especies que interactúan deben ser menos

relacionado filogenéticamente de lo esperado por azar (Webb et al 2002; Cavender-Bares et

al. 2004; Kraft et al. 2007). Se espera que la importancia de estos procesos opuestos, filtrado

de hábitat y el limitante de similitud, varíen a lo largo de gradientes ambientales, afectando el

conjunto de la comunidad. (Freschet et al. 2011; Violle et al 2012).


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Con base en estos supuestos, una expectativa generalizada es que la diversidad funcional

disminuye bajo fuerte estrés biótico y aumenta en los habitas más leves (por ejemplo Weither

y Keddy 1995), y que filogenéticamente estén mas agrupado bajo fuerte estrés (por ejemplo

Webb 2000). Aunque estos dos son uno de los principales procesos de la asamblea de la

comunidad hay otros procesos que también puede afectar a la diversidad funcional de la

comunidad y la estructura filogenética (Valiente-Banuet and Verdú 2007; Mayfield and

Levine 2010; Weiher et al. 2011) Por un parte tenemos la facilitación a menudo permite la

coexistencia de especies (Callaway et al. 2002; Brooker et al. 2008; Butterfield 2009) con

diferentes rasgos funcionales (Callaway 2007) y / o filogenéticamente distantes (Valiente-

Banuet et al. 2006) aumento la diversidad funcional y la dispersión filogenético. Y por otra la

competencia puede aumentar la similitud de los rasgos entre las especies ocasionando que

pueden competir relativamente igual (Chesson 2000). Este proceso puede reducir la

diversidad funcional (Chesson 2000; Spasojevic and Suding 2012) y aumentar la dispersión

de las especies en la filogenia.

Bajo estas premisas hemos explorado la diversidad de los bosques tropicales secos del sur de

Ecuador a lo largo de un gradiente ambiental. Tenemos la intención de determinar si la

facilitación modifica la respuesta esperada de ambos procesos mediante la adición de

especies filogenéticamente distantes y funcionalmente a la comunidad a nivel de estrés.

El tercer capítulo fue orientado a cuantificar la dispersión de los rasgo funcionales a lo largo

de una escala espacial a nivel funcional y filogenético. Swenson and Enquist (2009) aplicaron

ambos enfoques en un Bosque Seco Neotropical y encontraron que la combinación del

estudio la diversidad funcional y filogenética y la escala espacial permite un mejor

entendimiento en la estructuración de la comunidad (Kraft y Ackerly 2010). La flexibilidad

del rasgo y los análisis filogenéticos para cuantificar patrones en múltiples escalas espaciales


19

los hace ideales para abordar la cuestión de la dependencia de la escala en la ecología de la

comunidad (por ejemplo, Cavender-Bares et al. 2006; Swenson et al. 2006). Se ha

documentado que la diversidad de especies es promovida y mantenido por los procesos

ecológicos y evolutivos que operan en los rasgos de las especies a través del espacio y el

tiempo.

De modo semejante a lo que pretendemos con el capítulo 2, aplicaremos una aproximación

conjunta (funcional y filogenética) al estudio de una parcela de Bosque Seco de 9 Ha en la

que hemos cartografiado e identificado todos los individuos arbóreos y arbustivos. Esto nos

permitirá conocer como actúan los procesos de ensamblaje de la comunidad a escala espacial

fina y determinar que escalas espaciales resultan más importantes (Kraft y Ackerly 2010). En

este enfoque utilizamos rasgos funcionales, individuales y en combinación (Westoby 1998;

Westoby et al. 2002; Westoby and Wright 2006) cuantificamos la relación entre la dispersión

del rasgo frente a la riqueza de especies en diversas escalas espaciales.

En nuestro últimos capítulos nos proponemos analizar la estructuración de la comunidad bajo

tres dimensiones taxonómica, funcional y filogenética en una parcela de Bosque Seco de 9

hectáreas, con un enfoque espacialmente explícito. Recientemente, los ecologistas han pasado

de la medición de la diversidad como la riqueza de especies y la uniformidad, a la utilización

de rasgos funcionales y la filogenia de la especies para estimar la similitud ecológica de las

especies con el fin de probar las hipótesis del ensamble de la comunidad (Swenson 2013).

Se han desarrollado análisis estadístico novedosos para el estudio de la diversidad vegetal,

mediante el uso de patrones espaciales, como es el uso del ISAR (Su relación individual de

especies-área) propuesto por Wiegand et al. (2007), donde calcula la riqueza de especies en

los barrios locales alrededor de los individuos de una especie clave dentro de una comunidad.

Como consecuencia de ello una especie en particular pueden ser identificada como


20

acumulador o repelente de diversidad, o como neutral. A pesar del claro impacto del enfoque

desarrollado por Wiegand et al. 2007, es limitado, ya que trata a todas las especies

ecológicamente idéntico y evolutivamente independiente (CO Webb et al. 2002, Yang et al.

2013). Así, un objetivo clave en la ecología de la comunidad ha sido ampliar el enfoque

multi-escala de Wiegand et al. 2007) donde se incluya a otros ejes de la biodiversidad, como

el funcional (DF) y filogenético (PD). porque están convencidos de que pueden ser mejores

predictores de la productividad y de la estabilidad de la comunidad (Tilman et al. 1997,

Cadotte et al. 2009; Flynn et al. 2011; Helmus and Ives 2012). El objetivo de este capitulo

fue evaluar el comportamiento de las especies en las tres dimensiones en el caso de la

diversidad taxonómica usamos ISAR, IFDAR para el nivel funcional y IPSVAR para la

diversidad filogenética Nosotros probamos estos patrones en la parcela de 9 Ha de un bosque

seco neotropical ubicado en la Reserva Ecológica Arenillas (REA), este ecosistema es

especialmente interesante porque se somete periódicamente a un fuerte estrés abiótico, lo que

ha llevado a las especies desarrollan patrones y adaptaciones morfofuncionales. Algunos

estudios han demostrado que en este ecosistema, la facilitación es más fuerte en condiciones

de estrés y se reduce cuando las condiciones mejoran (por ejemplo, Espinosa et al. 2014,

2011).

2.2. OBJETIVOS

- Está tesis ha sido estructurada en cuatro capítulos que cubren aspectos como: la estructura

de la comunidad dado por las reglas de ensamblaje, la diversidad funcional y filogenética

medida por un gradiente de estrés, la relación dispersión funcional y filogenética medida

por la riqueza de especie a diferentes tamaños de escala espacial y finalmente la

diversidad de la comunidad en tres dimensiones (taxonómica, funcional y filogenética)

mediante un enfoque espacial.


21

El objetivo principal es:

- Identificar los procesos de ensamblaje de la comunidad basados en rasgos funcionales en

los Bosques Secos del Sur del Ecuador, un tipo de ecosistema de gran diversidad.

Los objetivos específicos son:

- Evaluar si la distribución de los rasgos funcionales a nivel comunitario es compatible

con la existencia del filtrado del hábitat o el límite de similitud en un bosque neotropical

seco.

Se considera que la distribución de los valores que pueden tomar los rasgos funcionales

dentro de una comunidad está condicionada por dos tipos principales de procesos de

ensamblaje. En primer lugar un “Filtrado por el hábitat” que restringe el rango de estrategias

viables en una comunidad .En segundo lugar el reparto de micro-sitios y recursos, que

condiciona un limite que restringe la semejanza funcional de las especies coexistentes. La

magnitud de ambos procesos puede depender de las condiciones de una localidad y puede

cambiar a lo largo de un gradiente abiótico.

- Identificar si existe vinculación de la diversidades funcional y filogenética con los

procesos de ensamblaje en bosque seco a través de un gradiente de estrés .

Hoy en día, algunas investigaciones en ecología implican además de los métodos basados en

el estudio de los rasgos funcionales otras aproximaciones como el estudio de la diversidad

funcional y filogenética en los proceso de la estructuran las comunidades, ya que

proporcionan mayor información de los diferentes procesos que se realizan en la

estructuración de la comunidad.

- Cuantificar la dispersión funcional y filogenética a lo largo de una escala espacial.


22

Es ampliamente reconocido que los patrones y procesos ecológicos son espacialmente

estructurado y dependiente de la escala, la comprensión de cómo se organiza la diversidad en

el espacio es un foco importante en la ecología de la comunidad. Análisis previos han

demostrado la utilizando de procesos estocásticos, filtros abióticos y bióticos, para cuantificar

la co-ocurrencia de las especies en el ensamble de las comunidades. Con fin de obtener una

visión más profunda de los mecanismos que promueven el ensamble de comunidades,

nosotros evaluamos si el rol de los rasgos funcionales de las plantas en la estructura de la

comunidad es dependiente de la escala espacial a la cual se evalúa.

- Analizar el conjunto de mecanismos de la comunidad a escala espacial local; mediante

un enfoque taxonómico, funcional y filogenética

Los ecologistas han pasado de medir la diversidad de especies solo con la riqueza y la

uniformidad, a la utilización de medidas que reflejan diferencias ecológicas como el uso de la

diversidad funcional y filogenética de las especies, debido a evidencias que apuntaban a

considerar los rasgos funcionales como mejores predictores de la diversidad. Nosotros

proponemos analizar la estructuración de la comunidad en una parcela de 9 Ha. de Bosque

Seco, a nivel taxonómico, funcional y filogenético, para conocer si en una comunidad hay

especies que favorecen la acumulación al o repulsión de la diversidad funcional, y a qué

escala espacial se ejercen estos procesos.


23

2.3. ÁREA DE ESTUDIO Y METODOLOGÍA GENERAL

Área de estudio

La presente tesis se llevó a cabo en los bosques estacionalmente secos del suroccidente de

Ecuador, ubicados en la región Pacífico Ecuatorial (Espinosa et al. 2012). Estos bosques

forman parte de la región biogeográfica Tumbesina, que abarca territorios del suroeste de

Ecuador y noroeste de Perú, en un rango altitudinal que va desde el nivel del mar hasta los

2000 m s.n.m., aproximadamente (Best and Kessler 1995; Espinosa et al. 2011). Estos

bosques son muy ricos en especies y refugio de una extraordinaria diversidad de endemismos

de muy diferentes grupos taxonómicos (Best and Kessler 1995). La región Tumbesina abarca

una gran variedad de climas, como resultado de la posición geográfica y la topografía variada

(Best y Kessler 1995). La precipitación es el factor climático más variable, y por ende, el más

importante para la definición de la vegetación a lo largo de la región (Best y Kessler 1995).

Esta región es reconocida como un centro de endemismo para diferentes taxones (Best y

Kessler 1995), así como por ser uno de los hotspots más amenazados del mundo (Dinerstein

et al. 1995, Espinosa et al. 2012). El clima se caracteriza por una estación seca que va de

mayo a noviembre y una estación lluviosa que se extiende desde diciembre a abril (Aguirre

and Kvist 2005). Para toda esta zona se estima una temperatura promedio anual entre 20° y

26°C y una precipitación promedio anual entre 300 y 700 mm (Aguirre y Kvist 2005).

En el capítulo 1 y 2 del presente trabajo abarcó la vegetación leñosa de toda el área de

bosques secos de la región suroccidente de Ecuador, en la provincia de Loja (cantones

Zapotillo, Macará, Celica; 120-2640 m s.n.m.) en uno de los remanentes más grandes y mejor

conservados de los bosques secos Tumbesinos (Aguirre y Kvist 2005), entre las latitudes

3°3’11’’ y 4°37’28’’ S, y entre las longitudes 79°14’37’’ y 80°25’46’’ O. Se registra una


24

temperatura media anual en la región de unos 24°C (rango: 10-33°C) y con una precipitación

media anual de 500 mm / año (Espinosa et al 20011).

Los capítulos 3 y 4 se desarrollaron dentro de la Reserva Ecológica Arenillas (REA),

localizada en el extremo sur del Ecuador, provincia de El Oro, entre las ciudades de Arenillas

y Huaquillas, y cubre un área de 131,7 Km2

(Decreto Ejecutivo No. 787) (03° 34’ 15,44’’S;

80° 08’ 46,15’’E, altitud 30 m). La REA fue incluida dentro del Patrimonio de Áreas

Naturales del Estado (PANE) en el año 2001 y ha estado protegida de actividades de

extracción por aproximadamente 60 años (BirdLife International 2013). Su clima se

caracteriza por una estación de lluvias de cuatro meses con una precipitación media anual de

515 mm a partir de enero a abril y de 152 mm durante la estación seca. La temperatura media

es de 25.2°C tienen una variación máxima de 3.4 º C entre los meses más fríos y más cálidos.

Metodología general

Para realizar los capítulos 1y 2 utilizamos las 109 parcelas (10x50m) previamente

establecidas en el sur del Ecuador en el área de estudio. Estas parcelas estuvieron establecidas

en 48 parches de bosque seco con un diseño estratificado, tratando de cubrir el rango total de

condiciones físicas del área (Espinosa et al. 2011).

Para cumplir con los objetivos planteados en los capítulos 3 y 4, se estableció una parcela

permanente de 9 ha en la Reserva Ecológica Arenillas (REA). Esta parcela se ubicó en una de

las zonas mejor conservadas de la REA, que posee una formación vegetal de transición entre

bosque tropical seco y matorral seco de tierras bajas. Todas las plantas leñosas con DAP >5

cm han sido inventariadas y georeferenciadas desde el año 2009.


25

Para colectar los rasgos funcionales dentro de nuestra investigación, tomamos en cuenta los

mas importantes en la literatura y seguimos los protocolos establecidos por Cornelissen et al

(2003) y Pérez-Harguindeguy et al. (2013).

La Altura máxima de la planta como se la conoce en varios estudios, se asocia con el vigor

competitivo, la fecundidad, y la tolerancia frente a condiciones de estrés. Altura de la planta

es la distancia más corta entre el límite superior de los principales tejidos fotosintéticos

(excluyendo inflorescencias) en una planta y el nivel del suelo, expresada en metros (Pérez-

Harguindeguy et al. 2013). En el caso de Densidad de la madera está estrechamente

vinculado al soporte mecánico, el transporte de agua y la capacidad de almacenamiento. En

consecuencia la densidad de la madera juega un papel central en las estrategias de historia de

vida de las especies. Las especies con un baja densidad cuenta con altas tasas de crecimiento

de diámetro y altura (Poorter 2008, Chave et al. 2009) mientras que las especies con una alta

densidad de la madera tiene tasas de crecimiento lento, pero sobreviven mejor a la sequía

(Alvarez and Kitajima 2007, Poorter and Markesteijn 2008).

El Tamaño de la hoja es importante para el equilibrio de la energía y el funcionamiento

hidráulico, hojas más pequeñas por lo general se encuentran en hábitats más secos y más

expuestas (Ackerly et al. 2002; Cornwell and Ackerly 2009). Se conoce como tamaño de la

hoja, al área de la superficie proyectada de un solo lado (envés) correspondiente a una sola

hoja, expresada en mm2 (Cornelissen et al. 2003). El tamaño de la hoja es importante para el

equilibrio de la energía y el funcionamiento hidráulico de la planta (Cornwell and Ackerly

2009). En el caso del Área foliar específica está definida como el área superficial de captura

de luz por unidad de biomasa seca, refleja un compromiso entre la captura y conservación de

los recursos (Poorter et al. 2009) tiene una fuerte relación positiva con la concentración de

nutrientes en la hoja, conductancia en los estomas, la tasa de fotosíntesis, el uso eficiente del


26

agua (Hoffmann et al., 2005), longevidad de la hoja, tasa relativa de crecimiento, densidad de

la madera, y la capacidad competitiva (Reich et al. 1997). Área foliar específica se cree que

variará con la fenología de la hoja, especies caducifolias tienen mayor área foliar específica,

especies de hoja perenne tienen valores más bajos de Área foliar específica (Reich et al.

1997). Finalmente la Masa seca de la semilla esta relacionada con todos los aspectos de la

ecología de las plantas, incluyendo la dispersión, establecimiento de plántulas y la

persistencia en el ecosistema (Westoby, 1998; Weiher et al.1999).


27

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35


36

CAPÍTULO 1

Environmental variation mediates habitat filtering and

limiting similarity in a Dry Neotropical forest.


37


38

Abstract

According to Community Assembly Theory (sensu Diamond) abiotic constraints and biotic

interactions combine to form assembly rules that determine which species from the local pool

will coexist. Although, this theory has generally been studied with data on taxonomic

diversity, it may be more informative to consider the functional traits of species. Two main

mechanisms have been proposed to explain the role of functional traits in the assembly of

plant communities; a habitat filter that restricts the range of viable strategies and second, a

partitioning of resources limits the similarity of traits amongst coexisting species.

We compare the distribution of values for five traits (maximum height, wood density,

specific leaf area, leaf size and seed mass) in 58 woody plant species from 109 dry forest

plots. Specifically, we examined the within-community mean, range, variance, kurtosis and

the standard deviation of this distribution of neighbor distances (sdND). The range and

variance of maximum height, specific leaf area and seed mass were smaller than would be

expected from a lottery model of community assembly in most plots. We also found evidence

of limiting similarity for leaf size in the kurtosis, and seed mass in sdND.

These results support the existence of both habitat filtering and a limiting similarity affect in

assembly of plant communities of Tumbesian dry forests. Habitat filtering is evident along

the precipitation gradient with functional trait configurations in drier sites of high specific

leaf size, high wood density, high maximum height, and low seed mass. We also confirmed

that the climate gradient modifies the strength of the limiting similarity with a shortage of the

allowed functional strategies and an increase of the effects of competition (a more even

distribution of trait values), for most traits, as the conditions become harder (i.e., drier).


39

Introduction

Detection of patterns in the ecosystems and inference of their causal processes were basic

topics in the development of community ecology [1,2]. One of the most impacting theoretical

contributions in this context was formulated by Diamond [3] as the Community Assembly

Theory. In a very synthetic way it points out that coexistence is possible due to differences in

resource use among species. According to this theory, the combined effect of abiotic

constraints (environmental conditions) and biotic interactions conform certain assembly rules

determining which species from the local pool will coexist at fine spatial and time scales in

the realized assemblages. Tests for this theory have traditionally focused on the taxonomic

identity and diversity, but a complementary and more informative approach is to consider the

functional features of species (functional traits) inhabiting these communities [4]. Plant

functional traits, i.e., any morphological, physiological or phenological feature that affects

plant growth, reproduction or survival, constitute a direct link between plant fitness and the

environment and are useful for answering relevant questions in ecology [5]. Indeed, plant

functional traits offer the best available approach for achieve a general predictive

understanding of communities [6] and investigate ecosystem functioning [7].

Two main mechanisms have been proposed to explain the role of plant functional traits in the

construction of realized assemblages: i) habitat filtering, which implies that the environment

selects (i.e., "filters") the range of suitable trait values within a community and, ii) limiting

similarity, which means that only species differing in their traits and therefore having

different resource requirements can thrive together, avoiding or minimizing inter–specific

competition [4]. Thus it is reasonable to test whether variation in the traits of coexisting

species is larger than expected by chance, with trait divergence being interpreted as evidence

of limiting similarity [8]. On the contrary, if habitat filtering were operating we would expect


40

a convergence of species trait values, i.e. less variation at the community level than expected

by chance [4].

Although the prevalence of these two mechanisms in natural communities is far from being

known, it has been hypothesized that the prevailing assembly mechanism in realized

assemblages could shift along environmental gradients from limiting similarity (and

functional divergence) at more productive and milder sites to habitat filtering (and

convergence) at the more stressful conditions [9]. In fact, the effect of environmental

gradients on the strength and direction of plant-plant interactions has been extensively

documented and has confirmed these expectations in many cases [10,11,12,13]. The shifts in

trait values across sites along and environmental gradient is usually related to species

turnover, and could be assessed by comparing community trait means [14,15].

Keeping this in mind, we have evaluated the role of habitat filtering and limiting similarity on

the assembly of neotropical Tumbesian dry forests, one of the less studied tropical forest

ecosystems in the world, despite evidences of extreme threats and forest losses are

accumulating quickly [16]. Understanding the mechanisms that determine community

assembly in these forests is critical for their proper management and conservation [17] and

also to complete the knowledge about the prevalence of these functional mechanisms in

tropical dry forests [18].

In previous works we found that both the composition and the structure of Tumbesian forests

greatly shift as climatic conditions (mainly annual precipitation) change along its distribution

area [19]. Our main objective here is to test whether the paradigm about the prevalence of

these assembly mechanisms remains valid in the Tumbesian forest, with e.g., limiting

similarity prevailing in the wetter and milder places occupied by the Tumbesian forest, close

to montane forests, and habitat filtering prevailing in the harsh full areas in contact with the


41

dry shrublands [20]. More specifically we intended to answer the following questions: (1) Is

there any evidence for a restricted distribution (habitat filtering) or even spacing (limitation

of similarity) of trait values in the Tumbesian forests? (2) Does the relative importance of

habitat similarity or habitat filtering with respect to different traits change across the climatic

gradient?

Methods

Natural history of the Tumbesian dry forests

Tumbesian dry forests extend on a narrow fringe territory comprising 87,000 km2 along the

Pacific coast from the southwestern tip of Ecuador, to the northwestern of Peru [21]. Our

study area covers nearly 2000 km2 in the province of Loja (Zapotillo, Macará and Celica

districts) in the southernmost tip of Ecuador, covering an altitude range from 120 to 2,600 m.

This territory encompasses one of the largest and best conserved remnants of the Tumbesian

dry forests in Ecuador [22,19]. We surveyed only well-conserved stands without evidences of

recent anthropogenic perturbations. All survey and collection was authorized under the

scientific investigation permit numbered 18-2012-IC-FLO-DPL-MA, issued by the

Ecuadorian Government Ministry of Environment. Average annual temperature in the region

is ca 24 ºC (range: 10–33 ºC). Mean annual precipitation is ca 500 mm/yr (range: 270–1284

mm/yr).

These forests are very rich in tree and shrub species [19] and are dominated by Bursera

graveolens, Acacia macracantha and Capparis scabrida. It is also the habitat of several

endangered species including Juglans neotropica, Siparuna eggersii [23,24].

Field data

We placed 109 rectangular plots (10 x 50 m) on 48 homogeneous forest stands following a

stratified sampling design based on the available preliminary landscape maps in the territory.


42

In each plot we counted the number of individuals of all standing perennial species see details

[19]. From the Worldclim database [25], we annotated for each plot the value of the mean

annual precipitation, as this variable has been shown to be one the main predictor of species

turnover and is correlated to the variation of community composition and structure in the area

[19].

Plant functional trait selection and measurements

We selected a set of plant functional traits, which are known for being tightly related to

important processes in trees and shrubs [26]. Leaf size (LS) plays a particularly important

role in carbon assimilation, water relations and energy balance [27]. Plant growth and

survival are strongly connected to specific leaf area (SLA, leaf area per unit mass). This trait

represents an "economic" axis of variation that ranges from species typical of resource-rich

habitats, which have a rapid tissue turnover and high metabolic rates and whose attributes

enhance the rapid acquisition of photosynthetic carbon (high SLA), to species tolerating

resource-limited habitats which tend to show opposite attributes [28]. SLA and LS decrease

along gradients of declining moisture and/or nutrient availability [27,29,30,31,32,33,34,35].

LS is important for energy balance and hydraulic functioning, with smaller leaves generally

found in drier and more exposed habitats [36]. WD is related to water efficiency and

transport [36]. Although the variation of this trait is considered to be orthogonal to the leaf

economic one [37], it is also related to growth and survival. Low WD is usually a surrogate

of high stem growth rates [38], whereas high WD are related to low growth rates and to the

development of thick cell walls [39], which makes stems more resistant to breakage [40] and

to fungi and pathogen attacks [41]. Hmax is related to access to light [42,43]. Higher plants

intercept on average more light and therefore have potentially faster growth rates [44]. SM is

a crucial feature for dispersion and regeneration and is related to the competition-colonization


43

trade-off [45]. Large seeds provide reserves for successful seedling establishment in poor

light or intense competitive conditions [46]. By contrast, small-seeded species produce more

seeds per unit of reproductive effort [47] and are better colonizers [48].

We selected all woody species appearing in at least 4 of the studied plots. Three traits (LS,

SLA and SM) were measured in the field and/or the laboratory, following standardized

protocols [26]. In the case Hmax were taken from the literature [49,50,51,52] and WD [S2]

For each species, we collected 10 leaves from each of 10 randomly chosen trees, covering all

the environmental range of each species in our data set. LS, was measured with the program

Image J [53] from scanned (300 dpi) fresh leafs. Leaf mass (for calculation of SLA) was

measured after 3 days drying at 60 ºC. Seeds were also collected from at least 10 fruits taken

from different individuals of each species. We also used seed accessions from the Germplasm

Bank of Universidad Técnica Particular de Loja. Seed mass was obtained after 48 hours

drying at 80 °C [26]. SM of ten species, which could not be collected in the field, were

approximated by the average SM of congeneric species recorded in the Kew Millennium

Seed database (Royal Botanic Gardens Kew [54].

Data analysis

We first calculated the Spearman rank correlation coefficients between trait values of species

and between mean species trait values by plot. For subsequent analyses, we log-transformed

Hmax, LS, SLA and MS values. WD showed a normal distribution and was not transformed.

The effects of habitat filtering can be seen as a reduction in the variety of successful trait

values that occur within a community [2,55,56]. To detect such a reduction, we measured the

range and the variance of trait values within each community. If habitat filtering is operating

on a specific plant trait, then the observed range of values for the plant trait should be lower


44

than expected if communities were assembled independently with respect to that trait. As the

range is susceptible to extreme values that could be due to mass effects [57] or measurement

error, we measured also the variance of trait values in these communities as a more efficient

metric of convergence [36]. In fact, these two metrics have been shown as good predictors of

habitat filtering [58]

In order to analyze the possible existence of a limiting similarity mechanism, we followed

three complementary approaches. First, we computed the kurtosis of the distribution of trait

values within plots, since it has been suggested that platykurtic distributions (i.e., low

kurtosis values) are expected when a process (i.e., limiting similarity) spreads species traits

within a realized community [36]. Secondly, we checked for large variances in the

distribution of trait values, as this is also a robust indicator of character divergence [58].

Additionally, we quantified the even distribution of trait values within plots. For this, within

each plot, we ordered species with respect to their trait values and computed differences in

trait values among neighbor species ("neighbor distances"). The standard deviation of this

distribution of neighbor distances (sdND) was used as a measure of even spacing. If a

limiting similarity process would be ruling the assembly process, sdND should be low or, in

any case, lower than the sdND of a completely random process [36].

Inference about the significance of the observed range, variance and kurtosis was based on a

lottery null model, with equal probability of assembling any of the recorded species in each

null community [36]. As we considered each of the surveyed plots as a community unit, we

conducted 9999 random draws for each plot with the same richness as the observed plot. We

compared the range, and kurtosis of observed trait values in the plot to the median of the

range and kurtosis of traits in the simulated communities with one-tailed Wilcoxon paired-

sample tests. In the case of variance we compared observed and expected values with two-


45

tailed tests. As the presence of a strong habitat filter effect could generate false positives

when testing for even spacing with lottery models (due to the existence of extreme trait

values in the species pool, [36], inference about the distribution of sdND's was based on a

two-step null model which accounts for possible habitat filtering [36]. In a first step, for each

plot, we identified among the 58 species those species whose niche breadth (relative to

annual precipitation) comprised the abiotic conditions found in the plot (i.e., "potential

community members").

Niche breadth was estimated as the range of annual precipitation values among the plots

where the species appeared in the whole study. In the second step, we conducted 9999

random draws with the same richness of each plot choosing among the set of potential

community members. We computed the sdND's of each random community and built a

distribution of sdND's under our null model. We then compared the observed sdND's with the

median of the randomized SND's with a one-tailed Wilcoxon paired-sample test.

In order to test whether species turnover along the gradient has any effect on the distribution

of traits, we also computed correlations between the mean value of each trait in each plot and

the values of the mean annual precipitation in each plot.

Finally, to test whether assembly mechanisms shift along the precipitation gradient, we

calculated Spearman rank correlations between the annual precipitation and the range,

variance, kurtosis and sdND's for each trait in each plot. All analyses including null models

testing were performed using R version 3.1.1 [59].

Result

We found 102 species (73 trees and 29 shrubs). Maximum richness per plot was 14 species,

with a median of 4 species per plot. We selected the 58 species (30 deciduous, 13 semi–


46

deciduous and 15 evergreens) that appeared in at least four of the studied plots (S1).

Plant functional traits showed appreciable variation between species. Hmax varied from 4 m

(Annona sp, LS ranged between 287.00 ± 60.22 cm2 in Piscidia carthaginensis and 8.46 ±

2.52 cm2 in Prosopis juliflora. SLA values varied between 46.63 cm

2/g and 2.9 cm

2/g in

Urera sp. and Fulcaldea laurifolia, respectively. WD ranged between 1.00 g.cm–3

(Tabebuia

chrysantha) and 0.19 g.cm–3

(Erythroxylum glaucum). Finally, SM ranged from 0.01 g in

Vernonanthura patens and Ficus insipida to 137.92 g in Geoffroea spinosa (S1).

We did not find intraspecific trait correlations except for the case of Hmax and LS (Table 1).

Table 1. Spearman rank correlation coefficients for 58 species trait values. Significant correlations (p

< 0.05) are shown in bold.

WD LS SLA SM

LS –0.23

SLA –0.10 0.10

SM 0.20 0.04 0.04

Hmax –0.03 0.30 (p=0.02) –0.01 0.20

WD – wood density; LS – leaf size; SLA – specific leaf area; SM – seed mass; Hmax – maximum height.

The mean trait values, per plot were significantly correlated for some traits. LS was positive

correlated with SLA and Hmax, and negative correlated with SM; whereas WD showed

positive correlation with SLA and SM. Finally SLA had a positive correlation with Hmax

(Table 2).


47

Table 2. Spearman rank correlation coefficients for mean trait values, in 109 plots. Significant

correlations (p < 0.05) are shown in bold.

WD LS SLA SM

LS 0.02

SLA 0.25(p=0.008) 0.33(p=0.001)

SM 0.36(p=0.001) –0.29(p=0.002) –0.06

Hmax 0.08 0.28(p=0.003) 0.26(p=0.006) 0.14

WD – wood density; LS – leaf size; SLA – specific leaf area; SM – seed mass; Hmax – maximum height

The range and variance of Hmax, SLA and SM were smaller than expected for the lottery

model of community assembly in most plots (Table 3, Figure 1).


48

0.0

0.5

1.0

1.5

2.0log(maximum height)

0.0

0.2

0.4

0.6

0.8

Wood density

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5log(Leaf size)

ran

ge

in

tra

it v

alu

es

2 4 6 8 10 12 14

0.0

0.5

1.0

1.5

2.0

2.5log(Specific leaf area)

Species richness

2 4 6 8 10 12 14

0

2

4

6

8

log(seed mass)

Species richness

Figure 1. The community–wide range of values of five traits at different levels of species richness.

Open circles show the observed values for 109 plots; solid squares show the mean of 9999 null model

trials at each of the corresponding levels of species richness. See Table 3 for a description of the traits

and a synthesis of results; note that data have been log–transformed except for wood density. Wood

density showed an approximately normal distribution and was not transformed.

☐ Observed values

Null expectation


49

Table 3: The range and variance of trait values within a site: deviations from the lottery model of

habitat filtering. Figures show the number of plots where the range or variance observed for the

distribution of values of each trait was smaller than the range or variance expected according to the

lottery null model.

Range Variance

Trait No. plots < Wilcoxon P No. plots < Wilcoxon P

expectation expectation

Hmax 62 out of 109 0.002 70 out of 109 <0.001

WD 34 out of 109 1 33 out of 109 <0.001

LS 59 out of 109 0.06 54 out of 109 0.65

SLA 77 out of 109 <0.001 70 out of 109 <0.001

SM 81 out of 109 <0.001 85 out of 109 <0.001

Hmax – maximum height; WD – wood density; LS – leaf size; SLA – specific leaf area; SM – seed mass. P – p-

value

On the other hand, the kurtosis of trait values within plots was significantly lower than

expected only for LS (Table 4). The standard deviation of the distribution of neighbor

distances among trait values within plots (sdND) was significantly lower than expected only

for SM (Table 4). Remarkably, the variance of WD was smaller than expected only in a few

plots, (Table 3).

Table 4: The kurtosis of trait values per plot, and the standard deviation of the distribution per plot of

"neighbor distances" (sdND), i.e., functional distances among neighbors in the list of species ordered

according to their trait values. Figures show the number of plots where the kurtosis or sdND observed

for the values of each trait was smaller than the kurtosis or sdND expected according to a lottery null

model (kurtosis) or to a two–step model accounting for habitat filtering (sdND).


50

Kurtosis sdND

Trait No. plots < Wilcoxon

P

No. plots < Wilcoxon

P expectation Expectation

Hmax 55 out of 109 0.72 66 out of 109 0.61

WD 53 out of 109 0.80 47 out of 109 0.95

LS 62 out of 109 0.05 52 out of 109 0.84

SLA 54 out of 109 0.39 50 out of 109 0.06

SM 40 out of 109 0.99 66 out of 109 0.01

Hmax – maximum height; WD – wood density; LS – leaf size; SLA – specific leaf area; SM – seed mass. P – p-

value

Average values (per plot) for four traits showed correlation with annual precipitation.

Average Hmax, WD and SLA decreased as annual precipitation increased, while SM increased

with precipitation (Table 5). In parallel, range and variance of Hmax, SM and SLA values per

plot increased with precipitation. Kurtosis of Hmax and SM were also positively correlated

with annual precipitation (Table 5). The sdND of LS and WD showed respectively positive

and negative correlations with annual precipitation (Table 5).

Table 5: Spearman rank correlation coefficients between plot value, range, variance, kurtosis,

standard deviation of nearest distance (sdND) and mean for plant functional traits and annual

precipitation. Bold values indicate correlations with p<0.05

Trait Range Variance Kurtosis sdND Mean

Hmax 0.38 (p=<0.001) 0.34(p=0.0002) 0.31(p=0.001) 0.01 –0.23(p=0.002)

WD 0.08 0.01 0.06 –0.28(p= 0.006) –0.47(p=0.001)

LS 0.18 0.05 0.03 0.30(p= 0.0004) –0.05

SLA 0.46(p=<0.001) 0.46(p=<0.001) 0.18 0.08 –0.32(p=0.0007)

SM 0.34(p=0.0002) 0.26(p=0.005) 0.35(p=0.0002) 0.12 0.38(p=0.001)

Hmax – maximum height; WD – wood density; LS – leaf size; SLA – specific leaf area; SM – seed mass.


51

Discussion

Although numerous functional trait-based studies have shown functional patterns from

temperate to Mediterranean and tropical moist forest ecosystems, there are relatively few

studies addressing patterns of functional trait diversity in tropical dry forests (TDFs) or other

extensive water-limited ecosystems [18,60,61,62].

The distributions of trait values within the Tumbesian TDFs were clearly nonrandom,

suggesting that the studied plant traits play an important role in determining the success or

failure of each species in each particular site. We did not find intra-specific trait correlations

(except for the case of Hmax and LS, Table 1), which implies that the potential response of

the studied traits to habitat filtering and competition could be considered independent.

We found signal of habitat filtering (smaller than expected range and variance of trait values)

for three traits: SLA, Hmax, and SM (Table 3). We found also evidence for limiting

similarity (lower kurtosis or lower sdND values than expected, respectively) for LS and SM,

and a lower variance for WD (Table 4). These results support the idea that both habitat

filtering and limiting similarity, which are considered sometimes as competing models of

community assembly, act simultaneously in the South Ecuadorian dry forests and,

sometimes, even act over the same trait (e.g., over SM).

The platykurtic distribution of LS and the trend towards large variance of WD within

communities are probably related to competition for water. Being water the main limiting

resource in the Tumbesian forests [19], a niche partition around water use efficiency, which

is closely related to LS [27,63, 64] and WD [65], would promote coexistence within the

community.

The even spacing of SM values could be interpreted under the same framework of niche

partition, i.e., as the result of the dispersion of different regeneration strategies associated to

microsite variability within plots (i.e., very fine spatial scale). This is congruent with the


52

known relationship between seed size and the competition-colonization trade-off [47,66] and

similar results have been found in other ecosystems, such as the Great Basin pine

communities [67] or the California Chaparral [36]

Another sign of the mediation of the environment in trait-based community assembly is the

variation of average community trait values through the environmental gradient. In fact,

environmental gradients can generate species turnover [68] caused by the gradient filtering

different species at different environmental conditions [36], which, in turn modify

community mean trait values along the gradient. We confirmed the existence of shifts in

mean community values along the precipitation gradient for most traits. For example,

community SLA increased as the climate became drier. Although this finding may seem

counterintuitive and contrary to what has been found in a number of studies [36,69,70], SLA

was not significantly related to climatic parameters in a global study reported by Ordoñez

[71]. The usual decline of SLA along gradients of decreasing moisture is usually mediated by

a transition from thin, large-leaved, deciduous plants to thick, small-leaved, perennial species

[36]. In the Tumbesian forests however, most species are deciduous [19,72], and must build a

new foliar system each wet season. As the duration of the wet season is related to annual

precipitation in the Tumbesian region [73], faster foliar development (and therefore higher

SLA, [74,75,76]) would be favored at the driest sites.

The variation of WD along the precipitation gradient was consistent with the lower stem-

growth rates expected in the drier sites. High WD are related to low growth rates and to the

development of thick cell walls [39], which additionally enable stems to resist embolism

while maintaining highly negative water potentials [77].

The average Hmax per plot increased with declining annual precipitation. This is surprising

as the dispersion of Hmax values within plots showed symptoms of habitat filtering (i.e.,

lower range and variance than expected according to null models), which suggest that drier


53

conditions favor the establishment of species with potentially larger height. We think that in

this case the convergence in trait values is not caused by habitat filtering but instead is the

result of the competitive processes for light [78]. In fact, larger Hmax is positively related to

faster growth rates in tropical trees [44], as access to light increases exponentially with height

in forest canopies [43]. This, in combination with their larger LS (LS is correlated with Hmax

at species level; Table 1) would make Tumbesian species with larger potential height grow

faster than the smaller ones. In the drier sites, where the growing season is shorter, a faster

growth rate (i.e. a larger potential height) should be a competitive advantage.

Average SM per plot decreased with declining annual precipitation. This is not surprising as

high SM provides a competitive advantage for seedlings in the most competitive extreme

(i.e., the humid extreme) of the precipitation gradient [47,79,80,81]. In fact, larger seeds

provide the emerging seedlings with larger reserves and make them succeed size-dependent

competition, and to recover easily from damage caused, e.g., by herbivory or falling debris

[82], processes more important in the most productive (i.e., humid) extreme of the gradient.

On the contrary, small seed species are better colonizers of sites where conditions for

establishment are ephemeral both in time and space [48,83].

Finally, the influence of the environment on the processes mediating community assembly

could be confirmed by assessing the existence of shifts in the distribution of trait values

within communities along the environmental gradient. We confirmed that the distributions of

some traits shifted also along the gradient of precipitation. For example, the range and

variance of SLA, Hmax and SM decreased with declining precipitation. This means a

shortage of the allowed functional strategies for these traits as the environmental conditions

become harder. This result is contrary to recent views that expect convergence to occur in

more productive sites [76,78,84], and instead agrees with the traditional niche differentiation

theory, which expects these convergences to occur in the more stressful sites [85]


54

The kurtosis of Hmax and SM decreased with declining precipitation, as did the sdND of LS.

This means that, within communities, the spread of values for these traits increased as

precipitation decreased. This result points to an increase of the effects of competition, as the

environmental conditions become harder [86,87,88].

In the case of wood density, we found that sdND increased as precipitation declined, which

means that the spread of WD values was less even in dry sites, i.e., that the effects of

competition (or limiting similarity) on this trait decrease as the climate becomes harder. This

shows that the selective pressures for this trait and those for Hmax and SM are operating

along different resource axes.

In conclusion, we confirmed the influence of both habitat filtering and limiting similarity on

the assembly of the Tumbesian forests. The existence of a neat trend for plot mean trait

values across the precipitation gradient is consistent with the idea of habitat filtering: within

each sampled habitat, only species inside of the viable range of trait values will be included.

The results of the correlation analysis suggest that in the drier sites, the successful functional

strategy consist of high SLA, high WD, high Hmax and low SM (Table 5). We confirmed

that the gradient also modifies the strength of the limiting similarity and the habitat filtering

processes, with a shortage of the allowed functional strategies (i.e., a decrease of the range of

trait values) and an increase of the effects of competition (a more even distribution of trait

values), for most traits, as the conditions become drier.


55

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APPENDIX 1.

S1 Table: Mean trait values for 58 species of tropical dry forest. The letters a,b,c, mean the base literature of each trait. Hmax – maximum height; WD –

wood density; LA – leaf area; SLA – specific leaf area; SM – seed mass.

Family Species aHmax (m) bWD (g. cm–3) LS (cm2 ) SLA(cm2. g–1) dSM (g)

Mimosaceae Acacia macracantha Humb. and Bonpl. ex Willd. 12 0.73 28.16±4.6 6.93 3.12

Verbenaceae Aegiphila sp. 8 0.66 66.37±13.4 7.21 1.46

Opiliaceae Agonandra excelsa Griseb. 8 14.44±4.8 13.75 63.23

Annonaceae Annona sp. 4 0.44 124.29±25.6 19.73 20.06

Apocynaceae Aspidosperma sp. 10 0.65 67.09±10 10.42 2.64

Asteraceae Barnadesia arborea Kunth 4 11.62±2.5 15.92 1.43

Nyctaginaceae Bougainvillea peruviana Bonpl. 5 0.56 20.51±7.1 17.09 0.87

Burseraceae Bursera graveolens (Kunth) Triana and Planch 10 0.32 129.97±28.9 7.44 2.28

Caesalpinaceae Caesalpinia glabrata Kunth, Karl (Carl) Segismundo 8 0.95 42.83±9.4 9.95 10.63

Bombacaceae Cavanillesia platanifolia (Bonpl.) Kunth 25 0.60 279.24± 18.09 73.45

Bombacaceae Ceiba trischistandra (A. Gray) Bakh. 22 0.32 123.21±24.5 11.86 8.29

Ulmaceae Celtis iguanaea (Jacq.) Sarg. 6 0.66 48.54±8.8 3.91 15.52

Fabaceae Centrolobium ochroxylum Rose ex Rudd 12 0.69 226.68± 5.52 23.2

Solanaceae Cestrum auriculatum L'Hérit. 5 84.84±15.9 9.45 0.21

Verbenaceae Citarexylum sp 6 0.64 51.94± 16.03 14.21


62

Polygonaceae Coccoloba ruiziana Lindau 6 0.61 48.04±10.9 20.36 1.37

Bixaceae Cochlospermum vitifolium (Willd.) Spreng. 15 0.22 128.08 8.25 1.43

Capparaceae Colicodendron scabridum (Kunth) Seem. 10 0.67 16.17±5.6 4.56 3.33

Boraginaceae Cordia alliodora (Ruiz and Pav.) Oken. 10 0.52 60.17±13.5 7.25 1.65

Boraginaceae Cordia lutea Lam. 7 0.52 52.51±6.5 7.46 10.55

Boraginaceae Cordia macrantha Chodat 15 0.52 140.15±35.0 7.99 3.13

Euphorbiaceae Croton sp. 8 0.46 128.84±26.8 4.43 0.31

Bombacaceae Eriotheca ruizii (K. Schum.) A. Robyns. 15 0.39 177.26±45.8 19.59 1.14

Erythroxylaceae Erythroxylum glaucum OE Schulz 5 0.19 8.97±1.3 12.81 1.16

Fabaceae Erythrina velutina Willd. 12 0.27 175.36± 10.44 13.17

Moraceae Ficus insipida Willd. 15 0.38 115.25±26.1 8.97 0.01

Asteraceae Fulcaldea laurifolia (Bonpl.) Poir. 8 0.46 21.64±45.8 2.9 0.2

Fabaceae Geoffroea spinosa Jacq. 10 0.67 30.23±12.5 11.76 137.92

Sterculiaceae Guazuma ulmifolia Lam. 10 0.51 51.82±12.1 11.97 0.36

Convolvulaceae Ipomoea wolcottiana Rose 8 0.30 125.41±25.14 16.48 4.76

Mimosaceae Leucaena trichodes (Jacq.) Benth. 10 0.65 40.14±13.9 17.84 1.53

Anacardiaceae Loxopterygium huasango Spruce ex Engl. 20 0.73 246.05±54.9 13.77 0.7

Fabaceae Machaerium millei Standl. 9 0.24 82.4±22.4 14.53 33.35

Moraceae Maclura tinctoria (L.) D. Don ex Steud. 15 0.79 22.14±4.5 16.65 1.92

Anacardiaceae Mauria heterophylla Kunth 10 0.31 246.18 16.43 NA

Mimosaceae Mimosa pigra L. 4 0.73 31.63±10 5.79 0.63


63

Myrtaceae Myrcia fallax (Rich.) DC. 5 0.82 29.74±6.0 4.43 26.09

Piperaceae Piper sp. 3 0.39 106.58±22.1 21.27 0.51

Fabaceae Piscidia carthagenensis Jacq. 15 0.8 287.2 15.77 3.23

Nyctaginaceae Pisonia aculeata L. 8 0.58 23.24±6.2 4.82 1.82

Mimosaceae Pithecellobium excelsum (Kunth) Mart. 5 0.52 21.99±6.9 15.93 8.27

Flacourtiaceae Prockia crucis P. Browne ex L. 4 0.58 46.31±9.5 22.48 NA

Mimosaceae Prosopis juliflora (Sw.) DC. 10 0.79 8.46±2.5 20.14 1.79

Myrtaceae Psidium guayava Raddi 5 0.71 38.32±10.4 5.97 0.37

Rubiaceae Randia armata (Sw.) DC. 5 0.67 27.34±6.6 15.71 1.33

Sapindaceae Sapindus saponaria L. 8 0.67 96.1±23.4 12.63 69.79

Caesalpinaceae Senna mollissima (Humb. and Bonpl. ex Willd.) H.S. Irwin

and Barneby

6 0.56 152.48 7.18 3.6

Caesalpinaceae Senna spectabilis (DC.) H.S. Irwin and Barneby 8 0.56 268.34 24.99 1.62

Rubiaceae Simira ecuadorensis (Standl.) Steyerm. 8 0.8 194.43±44.3 11.84 3.88

Solanaceae Solanum albidum Dunal 5 0.42 152.49±31.3 14.22 0.02

Styracaceae Styrax sp 6 0.38 84.2±11.9 15.89 10.74

Bignoniaceae Tabebuia chrysantha (Jacq.) G. Nicholson 15 1 213.85±52.2 19.84 1.23

Bignoniaceae Tecoma castaneifolia (D.Don) Melch. 6 0.79 41.85±6.7 5.95 0.18

Combretaceae Terminalia valverdeae AH Gentry 10 0.70 62.37 6.17 2.1

Polygonaceae Triplaris cumingiana Fisch. Y CA Mey. 9 0.52 194.1±56.1 3.98 3.95

Urticaceae Urera sp. 7 0.21 43.63±7.2 46.41 0.05


64

Asteraceae Vernonanthura patens (Kunth) H. Rob. 5 0.54 70.08± 9.27 0.01

Rhamnaceae Ziziphus thyrsiflora Benth. 8 0.88 34.89± 30.47 75.64

Symbol shows the averages of values the data for wood density and seed mass of the genus for each species

Symbol shows the averages of values the data for wood density and seed mass of the family for each species.

a Whaley OQ, Orellana A, Pérez E, Tenorio M, Quinteros F, et al. Plantas y Vegetación de Ica, Perú – Un recurso para su restauración y conservación. Royal Botanic

Gardens, Kew. 2010. a Aguirre Z, Cueva E, Merino E, Quizhpe W, Valverde A. Evaluación ecológica rápida de la vegetación en los bosques secos de La Ceiba y Cordillera Arañitas, provincia de

Loja, Ecuador. Pp. 15-35. En M.A. Vásquez, M. Larrea, L. Suárez and P. Ojeda (eds.). Biodiversidad en los Bosques Secos del Sur-Occidente de la Provincia de Loja.

EcoCiencia, Ministerio del Ambiente, Herbario LOJA y Proyecto Bosque Seco. 2001; Quito. Ecuador. aAguirre Z, Zofhre H. Especies forestales de los bosques secos del Ecuador. Guia dendrológica. 2012; 392.

bZanne AE., Lopez-Gonzalez G*, Coomes DA, Ilic J, Jansen S, et al. Global wood density database. 2009; Dryad. Identifier: http://hdl.handle.net/10255/dryad.235.

bChave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE. Towards a worldwide wood economics spectrum. Ecology letters. 2009; 12: 351–366.

c Royal Botanic Gardens Kew. Seed Information Database (SID). Versión 7.1. Available from. October 2013; http://data.kew.org/sid/


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66

CAPÍTULO 2

Linking functional and phylogenetic diversities with assembly

processes in a dry neo tropical forest


67


68

ABSTRACT

One of the most important challenges for ecologist is to understand the assemblage

mechanisms of plant communities. One compelling community assembly model predicts that

environmental filtering will be more important in structuring communities in stressful

environments, while competitive interactions will be more important in benign environments

These focus ignores the importance of other assembly processes such as equalizing fitness

processes and facilitation important in the community assembly.

We intended to determine whether facilitation under the highest level of stress modifies the

expected response for habitat filtering and limiting similarity by adding phylogenetically and

functionally distinct species to the community. In the present work we evaluated the

functional structure of five important plant functional traits along a climatic stress gradient.

We compared the observed trait distribution in the community with a null model that assumes

random community assembly.

Our results suggest evidences of habitat filtering in the values taken by most traits along the

stress gradient (measured as CWN), no evidence that functional diversity shifted along the

environmental gradient was found for any trait except for leaf size. The usual decline of LS at

along gradients of decreasing moisture is usually mediated by a transition from thin, large-

leaved, deciduous plants to thick, small-leaved, perennial species. Other factor is the

increased leaf nitrogen in arid environments, as an adaptation to prevent water loss by

allowing stomata to remain closed for longer periods of time


69

INTRODUCTION

One of the most important challenges for ecologist is to understand the assemblage

mechanisms of plant communities (Keddy 1992; Weiher and Keddy 1995; Chesson 2000;

Ackerly 2003; Götzenberger et al. 2012; Price et al. 2012). A promising approach for

disentangle these mechanisms is the use of functional and phylogenetic diversities (McGill et

al. 2006; Kraft et al. 2008; Mayfield and Levine 2010; Wiens et al. 2011; Paine et al. 2011;

Katabuchi et al. 2012; Shipley et al. 2012). A complete theoretical framework has been built

to describe how the environmental conditions affect the values taken by these two community

attributes during the assemblage of plant communities (Lavorel and Garnier, 2002; McGill et

al. 2006; Jeltsch et al. 2008).

Two potentially opposing processes can structure the distribution of functional diversity

along environmental gradients. Firstly, abiotic limitations increase the species functional

similarity, because the co-occurring species needs to be functionally adapted to the same

abiotic envelope (May et al. 2013). This process is known as "habitat filtering" (Cornwell and

Ackerly, 2009). Secondly, under more productive conditions prevalence of competition

would predict a limit in the similarity of coexisting species (MacArthur and Levins 1967;

Pacala and Tilman 1994). Since that species with similar functional traits may have a

substantial overlap niche, it is expected that co-occurring species show divergence of traits

(MacArthur and Levins 1967; Stubbs and Wilson 2004; Kraft et al. 2008). This process is

known as “limiting similarity” (Cornwell and Ackerly 2009). Thus, while the habitat filtering

reduces the functional diversity (Weiher and Keddy 1995; Weiher et al. 2011), the limiting

similarity increases the functional diversity among coexisting species (Cornwell and Ackerly

2009; Weiher et al. 2011). If these niche and functional differences matter they should shift


70

along gradients simply because their prevalence appears associated to stressful conditions

(Bernard-Verdier et al. 2012)

The relationship between functional and phylogenetic diversity is rooted in the reasonable

assumption that evolutionary diversification has generated traits diversification, which in turn

may result in greater niche complementarity and similarity associated to relatedness (Webb

2000). Thus, related species (i.e. same genus) are functionally and ecologically more similar

than distantly related species (Webb et al. 2002). This framework allows inferring the

potential mechanisms that underlie phylogenetic structure of a community when compared

with null expectation generated by drawing species at random from a regional pool of

potential colonists (Webb 2000; Cornwell et al. 2006). In the same way that functional

diversity, the phylogenetic diversity can be affected for “habitat filtering” and “limiting

similarity” (i.e. competitive exclusion). If the habitat filtering is the assembly community

process and plants exhibit evolutionary niche conservatism, then closely related species with

similar traits should coexist within a community more commonly than expected and the

species would be clustered in the phylogeny (Webb et al. 2002; Cavender-Bares et al. 2004;

Losos 2008). Alternatively, if competitive exclusion is a primary driver of community

composition, this should limit coexistence of closely related species with similar niches, and

species would be overdispersed in the phylogeny (Webb et al. 2002; Cavender-Bares et al.

2004; Kraft et al. 2007).

It is expected that the importance of these opposing processes, habitat filtering and limiting

similarity, vary along environmental gradients, affecting the community assemblage.

(Freschet et al. 2011; Violle et al. 2012). Based on these assumptions, a widespread

expectation is that functional diversity decreases under strong biotic stress and it increases in

milder habitats (e.g. Weither and Keddy 1995; Spasojevic and Suding 2012) and that


71

phylogenetic diversity is clustered under strong biotic stress and shows overdispersion in

mesic habitats (e.g. Webb et al. 2002; Swenson and Enquist 2009).

Although these two are among the main processes in the community assembly there other

processes that can also affect the functional diversity of community and phylogenetic

structure (Valiente-Banuet and Verdú 2007, Mayfield and Levine 2010; Weiher et al. 2011).

Even if niche differentiation prevents coexisting of similar species (i.e. limiting similarity)

competition may increases similarity among species (Chesson 2000). Under some conditions

species with similar traits may compete relatively equally, thus excluding competitors with

unfit traits (i.e. equalizing fitness processes Chesson 2000; Grime 2006). This process may

reduce functional diversity (Chesson 2000, Spasojevic and Suding 2012) and increase species

dispersion in the phylogeny. On the other hand, in stressful environments, facilitation often

allows the coexistence of species (Callaway et al 2002; Brooker et al. 2008; Butterfield 2009)

with different functional traits (Callaway 2007) and/or phylogenetically distant (Valiente-

Banuet et al. 2006) increasing the functional and phylogenetic diversity.

Under these limitations to know when and where both diversities run in parallel remains as a

challenge. In order to build a more general framework for disentangling the signatures of

habitat filtering and limiting similarity in community assembly left on functional and

phylogenetic diversities we have explored the diversities of dry tropical forests of southern

Ecuador along a large environmental gradient. Some works carried out in this ecosystem

have recognized the prevalence of facilitation and have shown that the strength of this

interaction increases with stress (Espinosa et al. 2011; 2013). We intended to determine

whether facilitation under the highest level of stress modifies the expected response for

habitat filtering and limiting similarity by adding phylogenetically and functionally distinct

species to the community.


72

In the present work we evaluated the functional structure of five important plant functional

traits along a climatic stress gradient. We compared the observed trait distribution in the

community with a null model that assumes random community assembly. Based on this

approach, we want to answer the following specific questions: i) does functional and

phylogenetic diversity shift along abiotic stress gradients from lower values at the stressful

conditions to higher values at the more benign habitats?., and ii) does facilitation increase

functional and phylogenetic diversity in stressful environments?

Methods

Natural history of the Tumbesian dry forest

Tumbesian dry forests extend on a narrow fringe territory comprising 87,000 km2 along the

Pacific coast from the southwestern tip of Ecuador, to the northwestern of Peru (Dinerstein et

al. 1995). Our study area covers nearly 2000 km2 in the province of Loja (Zapotillo, Macará

and Celica districts) in the southernmost fringe of Ecuador, covering an altitude range from

120 to 2600 m. This territory encompasses one of the largest and best conserved remnants of

the Tumbesian dry forests in Ecuador (Aguirre and Kvist 2005; Espinosa et al. 2011). We

surveyed only well-conserved stands without evidences of recent anthropogenic

perturbations. Average annual temperature in the region is ca 24 ºC (range: 10–33 ºC). Mean

annual precipitation is ca 500 mm/yr (range: 270–1284 mm/yr) in average.

These forests are very rich in tree and shrub species (Espinosa et al. 2011) and are dominated

by Bursera graveolens, Acacia macracantha and Capparis scabrida. It is also the habitat of

several endangered tree species including Juglans neotropica, Siparuna eggersii (Joergensen

and León-Yánez 1999; Valencia et al. 2000).


73

Field data

We placed 109 rectangular plots (10 x 50 m) on 48 homogeneous forest stands following a

stratified sampling design based on the available preliminary landscape maps in the territory.

In each plot we counted the number of individuals of all standing perennial species (see

details in Espinosa et al. 2011). From the Worldclim database (Hijmans et al. 2005), we

annotated for each plot the value of the mean annual precipitation, as this variable has been

shown to be one the main predictor of species turnover and is correlated to the variation of

community composition and structure in the area (Espinosa et al. 2011).

Plant functional trait selection and measurements

We selected a set of plant functional traits, which are known for being tightly related to

important processes in trees and shrubs. Leaf size (LS) plays a particularly important role in

carbon assimilation, water relations and energy balance (Ackerly et al. 2002). Plant growth

and survival are strongly connected to specific leaf area (SLA, leaf area per unit mass). This

trait represents an "economic" axis of variation that ranges from species typical of resource-

rich habitats, which have a rapid tissue turnover and high metabolic rates and whose

attributes enhance the rapid acquisition of photosynthetic carbon (high SLA), to species

tolerating resource-limited habitats which tend to show opposite attributes (Poorter and

Bongers 2006). SLA and LS decrease along gradients of declining moisture and/or nutrient

availability (Schimper et al. 1903; Hamann 1979; Dolph and Dilcher 1980; Givnish 1984;

Skarpe 1996; Cunningham et al. 1999; Fonseca et al. 2000; Ackerly et al. 2002).

LS is important for energy balance and hydraulic functioning, with smaller leaves generally

found in drier and more exposed habitats (Cornwell and Ackerly 2009). Wood density (WD)

is related to water efficiency and transport (Cornwell and Ackerly 2009). Although the


74

variation of this trait is considered to be orthogonal to the leaf economic one (Ackerly 2004),

it is also related to growth and survival. Low WD is usually a surrogate of high stem growth

rates (King et al. 2006), whereas high WD are related to low growth rates and to the

development of thick cell walls (Castro-Díez et al. 1998), which makes stems more resistant

to breakage (van Gelder et al. 2006) and to fungi and pathogen attacks (Augspurger 1984).

Height maximum (Hmax) is related to access to light (Westoby 1998; Poorter et al. 2005).

Higher plants intercept on average more light and therefore have potentially faster growth

rates (Poorter et al. 2008). Seed mass (SM) is a crucial feature for dispersion and regeneration

and is related to the competition-colonization trade-off (Coomes and Grubb 2003). Large

seeds provide reserves for successful seedling establishment in poor light or intense

competitive conditions (Kitajima 2002). By contrast, small-seeded species produce more

seeds per unit of reproductive effort (Moles and Westoby 2006) and are better colonizers

(Dalling et al. 1998).

We selected all woody species appearing in at least 4 of the studied plots. Three traits (LS,

SLA and SM) were measured in the field and/or the laboratory, following standardized

protocols (Cornelissen et al. 2003). The rest (wood density and maximum height) were taken

from the literature.

For each species, we collected 10 leaves from each of 10 randomly chosen trees, covering all

the environmental range of each species in our data set. LS was measured with the program

Image J (Kraft et al. 2010) from scanned (300 dpi) fresh leafs. Leaf mass (for calculation of

SLA) was measured after 3 days drying at 60 ºC. Seeds were also collected from at least 10

fruits taken from different individuals of each species. We also used seed accessions from the

Germplasm Bank of Universidad Técnica Particular de Loja. SM was obtained after 48 hours

drying at 80 °C (Cornelissen et al. 2003). SM of ten species, which could not be collected in


75

the field, were approximated by the average SM of congeneric species recorded in the Kew

Millennium Seed database (Royal Botanic Gardens Kew 2013).

STATISTICAL ANALYSIS

We conducted a principal components analysis (PCA) with all the recorded climatic and soil

variables using function rda of package vegan (Oksanen et al. 2013. The first PCA axis (PC1)

accounted for 51 % of the environmental variation and was highly correlated to variation in

climatic conditions, so we use plot scores in this axis as a measure of the mean environmental

gradient in subsequent analyses.

Species richness and Simpson’s diversity index was measured in each plot.

To summarize the functional composition of each plot, we used community-weighted means

of trait values (CWM, Garnier et al. 2004). In addition, we computed functional dispersion

(FDis, Laliberté and Legendre 2010) as a measure of functional diversity, using function

fdisp in the package FD (Laliberté and Legendre 2010). We computed CWM and FDis both

for each individual trait and for all the traits in combination. In addition, to describe

variability among communities in functional diversity, we classified the plots in 9 groups of

environmentally similar characteristics and calculated the coefficient of variation of FDis

(CVFDis) or each individual trait and for all the traits in combination. To make the

classification, we calculated the Euclidean distance among plots on the basis of their PC1

scores and computed a hierarchical clustering (Ward's minimum variance method) with the

function hclust of package stats (R Core et al. 2013). We partitioned the hierarchical

clustering into nine groups (varying in size from 9 to 15 plots; Spasojevic and Sunding 2012).

To calculate phylogenetic diversity, we first generated a phylogenetic tree for the 58-recorded

taxa using the program Phylomatic and the megatree version R20130707 (Webb and


76

Donoghue 2005). Branch lengths were calibrated according to Wikström et al. (2001) using

the bladj algorithm of Phylocom (Webb et al. 2008). We then calculated phylogenetic

diversity for each plot as the mean pairwise distance (MPD) among co-occurring species with

function mpd in package picante (kembel 2010). As is usual in this kind of studies (e.g.,

Kembel and Hubbell, 2006; Kraft and Ackerly 2010, Spasojevic and Sunding 2012), to

quantify overall clustering of taxa on the phylogenetic tree, we computed the standardized

version of MPD, net relatedness index (NRI), comparing the observed MPD for each plot

with the expectations (mean and standard deviation) of a null model of community assembly.

A NRI of zero indicates no difference between observed and null values, values greater than

zero indicate phylogenetic overdispersion and values less than zero indicate phylogenetic

underdispersion (Webb et al. 2002).

Inference about FDis and NRI was based on the values of FDis and MPD computed for

communities simulated according to a null model based on GLM with fixed abundance and

richness

To determine how the measured variables (e.g. species diversity, mean Bray–Curtis

dissimilarity trait CWM's, difference in FDis, NRI and CVFDis) changed along the

environmental gradient, we fitted OLS regressions, testing both linear and quadratic

responses to plot scores in the PC1.We selected the best-fit using Akaike Information Criteria

(Crawley 2007). All computations were made in the R environment (R Core Team 2014).

RESULTS

Environmental gradient

The first principal component axis (PC1) accounted for 51 % of the variation in the

environmental data. Variables related to water availability such as altitude, precipitation in


77

the driest month, annual precipitation, soil moisture and also others like pH and slope were

negatively correlated with the PC1 score. Variation in soil organic matter, total nitrogen and

organic carbon were mostly related to PC2, which accounted for an additional 19 % of

environmental variation (Appendix 1).

Species richness

We recorded 58 species in the 109 plots. The most abundant species were Simira

ecuadorensis (Rubiaceae), Tabebuia chrysantha (Bignoniaceae), Eriotheca ruizii

(Malvaceae) and Caesalpinia glabrata (Fabaceae), which appeared in >40 percent of the

plots. On the other hand, most species (54%) appeared in <5 percent of the plots. Maximum

richness per plot was 14 species, with a median of 4 species per plot.

Functional and phylogenetic diversity

We did not find intraspecific trait correlations except for the case of Hmax and LS (r = 0.3, p =

0.02).

Communities at the stressful end of the abiotic gradient (higher PC1 scores) tended to be

significantly higher in stature, with higher wood density. LS and SLA were largest at medium

environmental conditions and decreased to wards both ends of the environmental gradient

(quadratic relationship). Seed mass did not varied along the environmental gradient (Fig. 1).


78

-1.0 -0.5 0.0 0.5 1.0

6

8

10

12

14

Maximum height (m)

-1.0 -0.5 0.0 0.5 1.0

0.4

0.5

0.6

0.7

0.8

Wood density (g/cm3)

-1.0 -0.5 0.0 0.5 1.0

50

100

150

200

Leaf size

Co

mm

unity w

eih

ted m

ea

n

-1.0 -0.5 0.0 0.5 1.0

5

10

15

20

Specific leaf area

Less stressful More stressful

Enviromental gradiente

-1.0 -0.5 0.0 0.5 1.0

0

10

20

30

40

50Seed mass



Figure.1. Community-weighted mean (CWM) trait values along the stress–resource environmental gradient

(principal components analysis component 1. Community- weighted Maximum height (m) (a: F11,107 = 6.88, R2

= 0.05, P = 0.01), Wood density (g. cm–3) (e: F1,107 = 19.81, R

2 = 0.15, P<0.001), leaf size (cm

2) (b: F2,106= 5.96,

R2 = 0.08, P = 0.004) Specific leaf area (cm

2. g–

1) (d: F1,106 = 6.83, R

2 = 0.10, P = 0.002).

For all traits, most communities showed functional diversity concordant with the expectations

of the null model of community assembly considered. In some cases, however, some plots

showed overdispersion or underdispersion for some traits. The trait for which most plots

deviated from the expected null FDis was wood density, for which 9% and 15 % of plots


79

(n=10 and n=16) showed respectively over- and under-dispersion. Maximum height was the

trait for which there were the least deviations (only six over-dispersed and one under-

dispersed plots) (Fig. 2).

-1.0 -0.5 0.0 0.5 1.0

-0.05

0.00

0.05

0.10

Maximum height

-1.0 -0.5 0.0 0.5 1.0

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

Wood density

-1.0 -0.5 0.0 0.5 1.0

-0.2

-0.1

0.0

0.1

Leaf size

Fu

nctio

na

l d

ive

rsity

FD

IS o

bs -

Fd

is n

ull

-1.0 -0.5 0.0 0.5 1.0

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08Specific leaf area



-1.0 -0.5 0.0 0.5 1.0

0.0

0.1

0.2

0.3

Seed Mass



Figure. 2. Functional diversity (FDis) for individual traits along a stress–resource environmental gradient

(principal components analysis component. We represent for each plot the difference between observed

(FDisobs) and the expected (FDisnull) functional diversity according to null model based on GLM with fixed

abundance and richness of community. The dashed line represents the expected difference whereas values larger


80

and smaller than 0 indicate respectively over- and under-dispersion of FDis in respect to the null model.1).

Functional diversity significantly increased with increasing water stress only for leaf area (a: F1,106 = 3.87, R2 =

0.03, P < 0.05). Black symbols represent communities where functional diversity significantly differed (p <

0.05) from the null expectation; white symbols represent communities where the observed functional diversity

was not significantly different from the null expectation.

When FDis was computed from all traits combined, only five plots showed overdispersion

and eleven underdispersion (Fig. 3). The deviations from the expected FDisNULL occurred

uniformly along the whole environmental gradient for al traits except for leaf size, for which

functional diversity decreased towards the stressful end of the environmental gradient (Fig.2).

As in the case of the combined FDis, there were more underdispersed (14) than overdispersed

(5) cases among the plots that deviated from the expected phylogenetic diversity (as

estimated by NRI; Fig 3). Similarly, these deviations occurred along the whole environmental

gradient.

Fig. 3. Multivariate functional diversity (FDis) (principal components analysis component 1) and phylogenetic

diversity [net relatedness index (NRI)] did not change along the stress–resource environmental gradient than

null model. Black points represent plots where functional or phylogenetic diversity were significantly different

from the null model. The dashed line represents null functional diversity where negatives values indicate greater

functional diversity than null and positive values indicate less functional diversity than null.

-1.0 -0.5 0.0 0.5 1.0

-0.10

-0.05

0.00

0.05

Functional diversity


Fu

nct

iona

l div

ers

ity

FD

IS o

bs

- F

dis

null

-1.0 -0.5 0.0 0.5 1.0

-2

-1

0

1

2

3

4

5Phylogenetic diversity


NR

I


81

Functional diversity of all traits was highly variable among communities (CVFDis F2,7 = 10.3,

R2 = 0.67, P = 0.008) at the stressful end of the environmental gradient. Variability among

communities diminished (lower CVFDis) towards the less stressful end of the gradient for SLA

(F2,7 = 7.49, R2 = 0.59, P = 0.01). CVFDis did not change over the environmental gradient for

leaf area (F1,9 = 4.14, R2 = 0.24, P = 0.07), wood density (F1,9 = 0.99, R

2 = <-0.001, P = 0.42),

maximum height (F2,8 = 7.49, R2 = 1.39, P = 0.30) and seed mass (F2,8 = 0.22, R

2 = -0.18, P =

0.80).

DISCUSSION

The study of shifting patterns in functional and phylogenetic diversity along environmental

gradients has been used to understand the mechanisms ruling community assembly and in

particular, the role of habitat filtering and limiting similarity (e.g. Spasojevic and Suding

2011; May et al. 2013; Kraft and Ackerly 2010). Robust evidence shows that both functional

diversity and phylogenetic dispersion are usually low under strong abiotic stress, and that

they increase in regions with mesic conditions and more competitive interactions (e.g.

Weiher and Keddy 1995; Corwell and Ackerly 2009). However, some processes such as

facilitation can modify this pattern due to the fact that positive interactions tend to occur

between phylogenetic and functional distant species (Valiente-Banuet and Verdú 2007)

increasing the functional diversity and phylogenetic dispersion and therefore shifting the

expected pattern under the more general theoretical framework. In our tropical dry forest we

found evidences of the more general pattern only with respect to the diversity of leaf size,

which decreased as abiotic stress increased.

On the other hand, when multivariate functional and phylogenetic diversity were evaluated

they did not showed significant variation along the environmental gradient..


82

Changes in community weighted traits.

Interesantly, although functional and phylogenetic diversity showed no variation, the CWM's

for most traits were affected by the environmental gradient. CWM quantifies the dominant

trait values in a community and is consequently closely related to the ‘‘mass ratio

hypothesis’’ (Grime, 1998), which propose that ecosystem processes are mainly determined

by the functional traits of dominant species in a community (i.e. functional identity). The sign

of the CWM-environment relationship for some traits (e.g., Hmax and WD) is the same to the

sign found for the relationship between the unweighted community averages and annual

precipitation in another study (Gusmán et al. in revision) and points to habitat filtering as the

main process governing community assembly around these traits. In the case of SLA, we

found that the CWM decreased with favorable environmental conditions, as in the

unweighted analysis, but was better described by a quadratic relationship, i.e., CWM for SLA

were higher for intermediate environmental conditions and decreased towards both the

stressful and the favorable extremes of the gradient. The same relationship was found for the

CWM of leaf size. In our analyses, the CWM for seed mass was not significantly related to

environmental variation, but excluding two-outlier plots from the analysis, we found the

same relationship that we have reported to the unweighted case. The changes in the CWM

could be due stress tolerance of plants as response to habitat filtering (Weiher and Keddy

1995; Cornwell and Ackerly 2009; Lebrija-Trejos et al. 2010) and/or the equalizing fitness

processes (Spasojevic and Suding 2011).

In the stressfull habitats, leaf size and SLA are reduced probably as a response to low water

availability (Hulshof et al. 2013), i.e, as a consequence of habitat filtering. Although the

reduction of LS and SLA in more benign environmental conditions could be attributed to

some competitive process of equalizing fitness favoring convergence towards hypothetically


83

more competitive small LS and SLA values, this should be accompanied by a decrease in

functional diversity of these traits, which does not happen (in fact there is a significant

increase of FDis for leaf size towards the more benign end of the environmental gradient). A

more plausible explanation for this decrease of CWM values for LS and SLA at the less

stressful in the case of leaf area our results showed effects of habitat filtering, the functional

diversity was low in the stressful habitats and the mean leaf area was smaller in drier sites, a

result consistent with other studies at local and regional scales (Parsons 1976; Wilf et al.

1998; Fonseca et al. 2000). The functional significance of leaf area is not entirely clear

(Corwell and Ackerly 2009). Leaf area can influence the thermal conductance of the leaf.

High leaf area serve to keep leaves with low temperature through evaporative cooling, this

mechanism is efficient under high water availability (Corwell and Ackerly 2009). At dry

sites, where limited soil water may lead to stomatal closure on a daily basis, closed stomata

and large leaves could lead to high and potentially damaging leaf temperatures (Givnish and

Vermeij 1976).

Changes in functional and phylogenetic diversity.

Functional diversity is regarded as key to understanding ecosystem processes and their

response to environmental stress (Norberg et al. 2001; Suding et al. 2008; Cadotte et al. 2009;

Flynn et al. 2011). Although we found evidences for habitat filtering in the average values

taken by most traits along the stress gradient (measured as CWN), no evidence that functional

diversity shifted along the environmental gradient was found for any trait except for leaf size.

The prevalence of environmental filtering in stressed communities should cause decreased

functional diversity (Mouchet et al. 2010) and phylogenetic clustering (this one in case that

traits related to abiotic stress are phylogenetically conserved, as is usually assumed (Webb et

al. 2002, Kembel 2009). This hypothesis ignores facilitation or assumes that its effects will be


84

minor compared with the influence of environmental filtering in stressed communities

(Mason et al. 2013). However, there exists evidence about the dominant role played by in

structuring communities in stressful habitats (McAuliffe 1988; Tirado and Pugnaire 2005;

Valiente-Banuet et al. 2006). Espinosa et al. (2013, 2015) showed that in the interandean dry

shrublands and tropical dry forest facilitation shifts along environmental gradients, with an

increase of its effects towards more stressful conditions. Facilitation often allows the

coexistence of species (Callaway et al 2002; Brooker et al. 2008; Butterfield 2009) with

different functional traits (Callaway 2007) and/or phylogenetically distant (Valiente-Banuet

et al. 2006), which can increase phylogenetic dispersion and the functional diversity in the

stressful end of the gradient.

CONCLUSIONS

The assumption that habitat filtering reduced the functional diversity (Weiher and Keddy

1995; Cornwell and Ackerly 2009) and phylogenetic dispersion (Webb et al. 2002; Cavender-

Bares et al. 2004; Kraft et al. 2007) in stressful environments, and that limiting similarity

prevents coexisting of too similar species by competition, in more productive environments

(MacArthur and Levins 1967; Chesson 2000) ignores the importance of other alternative

mechanisms (i.e. facilitation, functional redundancy) operating on multiple niche axes. We

found evidences of habitat filtering in the values taken by most traits along the stress gradient

(measured as CWN), no evidence that functional diversity shifted along the environmental

gradient was found for any trait except for leaf size. The usual decline of LS at along

gradients of decreasing moisture is usually mediated by a transition from thin, large-leaved,

deciduous plants to thick, small-leaved, perennial species (Corwell and Ackerly 2009). Other

factor is the increased leaf nitrogen in arid environments, as an adaptation to prevent water


85

loss by allowing stomata to remain closed for longer periods of time (Wright et al. 2001,

2005).


86

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Appendix 1. Principal components analysis of environmental variables.


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CAPÍTULO 3

¿Actúan los mecanismos de ensamblaje a diferentes

escalas espaciales en un Bosque Seco?


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RESUMEN

Una aproximación que ha resultado ser eficaz para cuantificar la influencia de los procesos

estocásticos y deterministas en el ensamble de comunidades ha sido el estudio de la

diversidad funcional y filogenética en un contexto espacial. Los rasgos funcionales y la

estructura filogenética de la comunidad pueden ser modificados tanto por condicionantes

bióticos como abióticos. Nosotros evaluamos 1) si la dispersión de los rasgos funcionales de

las plantas en la estructuración de la comunidad es dependiente de la escala espacial, 2) Los

factores deterministas modifican la dispersión funcional dependiendo de la escala espacial?.

3) la dispersión de los rasgos funcionales es un reflejo de la dispersión filogenética.

Examinamos la dispersión funcional y filogenética en una parcela de 9 hectáreas en un

Bosque Seco ubicado en el Suroeste de la Costa Ecuatoriana, dividida en seis escalas

espaciales. Hemos encontrado que a escalas espaciales pequeñas, las especies que coexisten

son funcionalmente mas agrupadas, además existe una relación positiva entre la dispersión

funcional y la diversidad filogenética en escalas espaciales mayores, este resultado nos

indique existe una conservación de los rasgos funcionales en este tipo de bosque.

Nuestros hallazgos sugieren que la co-ocurrencia de especies fue impulsada por el filtrado de

hábitat a escalas pequeñas donde hubo mayor agrupación de lo esperado por el azar. Es decir

las condiciones abióticas locales permite la coexistencia de un sólo subconjunto funcional en

la piscina de especie.


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INTRODUCCIÓN

La importancia relativa de los procesos deterministas y estocásticos en el ensamble de las

comunidades sigue siendo un tema central en la ecología de comunidades (Gotzenberger et

al. 2012). Algunos autores argumentan que una aproximación efectiva para cuantificar la

influencia de estos procesos en el ensamble de comunidades es el análisis de la diversidad

funcional y filogenética en un contexto espacial (Losos 1996, Webb 2000, Cavender-Bares et

al. 2004; Milne 1992, Enquist y Niklas 2001).

Los rasgos funcionales y la filogenia de las especies deberían ser aleatorios en la comunidad,

en un contexto de un modelo estocástico, siendo la dispersión el factor limitante en la

estructura de la comunidad (Hubbell 2001). Por otro lado, modelos deterministas afirman la

importancia en la diferenciación ecológica y evolutiva de las especies en la comunidad. De

esta forma la estructura de la comunidad debe ser no-aleatoria con respecto a los rasgos

funcionales y a su filogenia (por ejemplo, Swenson y Enquist 2009; Kraft y Ackerly 2010;

Swenson et al 2012; 2013).

Los rasgos funcionales y la estructura filogenética de la comunidad pueden ser modificados

tanto por condicionantes bióticos como abióticos. Mientras las interacciones bióticas (como

la exclusión competitiva o la facilitación) conducen a la sobre-dispersión de los rasgos

funcionales, las interacciones abióticas (por ejemplo el filtrado ambiental) conducen a la baja

dispersión o agrupación de los rasgos funcionales en la comunidad (Swenson y Enquist

2009). Bajo este mismo enfoque cuando analizamos la diversidad filogenética de una

comunidad se espera que las especies estrechamente relacionadas puedan ser ecológica y

funcionalmente más similares a especies alejadas filogenéticamente (Swenson and Enquist

2009). Por ejemplo, si los rasgos funcionales se conservan filogenéticamente, las especies co-

ocurrentes estarán más relacionadas de lo esperado por el azar (Webb et al. 2002). Sin


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embargo, se ha demostrado que algunos rasgos ecológicos no se conservan filogenéticamente

(Cavender-Bares et al. 2004), lo que podría conducir a un desacople entre la diversidad

funcional y filogenética.

Se ha documentado que el filtrado de hábitat y el límite de similitud pueden ocurrir

simultáneamente en una comunidad de plantas, pero su importancia relativa por lo general

varía en diferentes escalas espaciales (Goetzenberger et al 2012; Adler et al 2013; May et al

2013). Por ejemplo en bosques tropicales el filtrado de hábitat parece ser más importante a

escalas más grandes, por lo que se producen gradientes ambientales y topográficas

predecibles (Shipley et al. 2012), por otra parte, el límite de similitud, es a menudo más

importante a escalas más pequeñas donde las interacciones competitivas entre los vecinos

aumentan en frecuencia e intensidad (Kraft y Ackerly 2010, Cavender-Bares et al 2006;

Swenson et al. 2007, 2012).

Con fin de obtener una visión más profunda de los mecanismos que promueven el ensamble

de las comunidades, nosotros evaluamos si el rol de los rasgos funcionales de las plantas en

la estructura de la comunidad es dependiente de la escala espacial a la cual se evalúa.

Específicamente nos interesa entender (1) ¿Cómo influye la escala espacial en la relación

entre la diversidad funcional y la riqueza de especies?; Si la diversidad funcional es un reflejo

de la diversidad taxonómica nosotros esperamos que la correlación sea significativa y

positiva entre estas dos medidas y que se torne negativa en escalas espaciales más grandes.

(2) ¿Factores deterministas modifican la dispersión funcional dependiendo de la escala

espacial?. Esperamos que el ensamble de las comunidades de bosque seco sea diferente a un

ensamble aleatorio de las especies del territorio, como consecuencia de filtrados bióticos o

abióticos. Así, a escalas cortas esperamos una mayor dispersión de caracteres como efecto de

interacciones bióticas y con menor dispersión a escalas mayores como resultado de un


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filtrado del ambiente. Finalmente, queremos entender. (3) ¿En qué medida la dispersión de

rasgos funcionales coinciden con la dispersión filogenética?. Si los caracteres funcionales

evaluados son conservados, bajo el criterio de conservacionismo filogenético (Fitter y Peat

1994, Ackerly y Reich 1999), la diversidad filogenética debería estar positivamente

relacionada con la diversidad funcional y estas a su vez con la riqueza taxonómica.

MÉTODOS

Área de Estudio

La zona de estudio se encuentra en el Reserva Ecológica Arenillas (REA). La REA se

encuentra en el suroeste de Ecuador, en la provincia de El Oro, entre los cantones de

Arenillas y Huaquillas y cubre 131,7 Km2

(Decreto Ejecutivo No. 787) con una altitud que va

de 0 a 300 m. Esta zona pertenece a la región biogeográfica Tumbesina, una de las zonas más

importantes de endemismo y a su vez se constituye en uno de los ecosistemas más

amenazados (Best y Kessler 1995). El clima se caracteriza por una temporada de lluvias

(enero a abril), con una precipitación media de 515 mm y sólo 152 mm de promedio durante

la estación seca (mayo-diciembre) (estación meteorológica Huaquillas por un período de 45

años de registro (1969-2014). La temperatura media es de 25.2 °C tiene una variación

máxima de 3.4 ºC entre los meses más fríos y más cálidos. La temperatura más baja se da

durante la estación seca.

Dentro de una zona muy bien conservada en el centro de la REA conocida como 'Pintag

Nuevo' instalamos una parcela permanente en 2009. Esta área se caracteriza por una

formación de transición entre los bosques secos caducifolios y matorrales secos de tierras

bajas. Las especies de árboles más conspicuos de la zona son; Tabebuia chrysantha y

Tabebuia bilbergii (Bignoniaceae), junto con otras especies como Colicodendron scabridum


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(Capparaceae) y Croton sp. (Euphorbiaceae). De enero de 2010 a mayo del 2011 delimitamos

una parcela cuadrada de 9 hectáreas, subdivida en 225 subparcelas de 20x20 metros (400 m2).

Todos los individuos arbóreos con un diámetro a la altura del pecho (DAP) igual o mayor a

5cm fueron registrados. Cada individuo fue identificado y determinada su posición espacial.

Rasgos funcionales en plantas

Seleccionamos un conjunto de rasgos funcionales que están estrechamente relacionados con

procesos importantes en las plantas como el crecimiento, reproducción, y fisiología. La

Altura máxima de la planta está relacionada con el acceso a la luz (Westoby 1998; Poorter et

al 2005). Las plantas más altas interceptan en promedio más luz y por lo tanto tienen tasas de

crecimiento potencialmente más rápidas (Poorter et al. 2008). La Densidad de la madera se

encuentra relacionada con la eficiencia y el transporte del agua (Cornwell y Ackerly 2009) y

con el crecimiento y la supervivencia del individuo. Baja densidad de la madera suele estar

relacionada con altas tasas de crecimiento del tallo (King et al., 2005), mientras que altas

densidades están relacionados con bajas tasas de crecimiento (Castro-Díez et al.1998).

Adicionalmente, la altas densidades de la madera generan paredes celulares gruesas (Castro-

Díez et al. 1998) mejorando la resistencia de los tallos a la rotura (van Gelder et al. 2006) y a

los ataques de patógenos (Augspurger 1984). El tamaño de la hoja desempeña un papel

particularmente importante en la asimilación de carbón, relaciones hídricas y el balance

energético de la planta (Ackerly et al., 2002). El crecimiento de las plantas y la supervivencia

están fuertemente conectados con el área foliar específica (SLA, siglas en ingles). Esta

característica representa un eje "económico" de variación que va desde las especies típicas de

los hábitats ricos en recursos, con altas tasas metabólicas y cuyos atributos mejoran la rápida

adquisición de carbono fotosintético (alta SLA), a las especies que toleran hábitats y recursos

limitados que tienden a mostrar atributos opuestos (Walters y Reich 1999; Poorter y Bongers


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2006). La Masa seca de la semilla (MSS, siglas en ingles) es una característica crucial para la

dispersión y la regeneración (Coomes y Grubb 2003). Las semillas grandes proporcionan

reservas para el éxito de establecimiento de plántulas en condiciones de bajos recursos

(Kitajima 2002). Por el contrario, las especies con semillas pequeñas son mejores

colonizadoras (Dalling et al. 1998).

Los rasgos se midieron en el campo y/o el laboratorio, siguiendo los protocolos

estandarizados propuestos por Pérez-Harguindeguy et al. (2013). Para cada especie

inventariada se recogieron al azar 10 hojas de 50 individuos de cada especie de árboles y

arbustos. El área foliar se midió con el programa Image J (Abramoff et al. 2004; Kraft and

Ackerly. 2010). El área foliar específica se calculó como el área de lámina de la hoja dividida

por la masa seca de la hoja (48 horas de secado a 80 ºC), para las especies de hojas

compuestas se incluyó el raquis. Las hojas fueron recolectadas en la temporada de

crecimiento. Para calcular la densidad de la madera se recogieron ramas secundarias de

árboles y arbustos. El volumen de madera fresca se determinó con el método de

desplazamiento de agua que consiste en secar las muestras a 80 °C, hasta que las muestras

mantengan un peso constante, finalmente se realiza el calculó la densidad de la madera (d =

m/v: donde m= masa seca y v= al volumen). Para las especies que no fue posible calcular la

densidad de la madera directamente, se utilizó la base de datos de Chave et al. (2009). Para la

obtención de los datos de masa de las semillas recogimos muestras de al menos 10 individuos

de cada especie. Las semillas fueron secadas durante 48 horas a 80 °C, luego de lo cual

fueron pesadas según lo propuesto por Cornelissen et al (2003). La masa seca de la semilla de

algunas especies que no pudieron ser recogidas en el campo, se aproximó su valor en base a

congéneres registrados en la base de datos del Jardín Botánico de Kew. (2014).


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Análisis estadístico

Modelos espaciales por especie

Empleamos la función K de Ripley para caracterizar el patrón espacial de cada especie

(Ripley 1976; Illian et al. 2008). Nuestro ajuste de modelo se basa en minimizar una medida

de discrepancia entre la función observada K y la función K (también conocido como

"contraste mínimo" o "mínimos cuadrados" de estimación), siguiendo la ponderación

presentada en Wiegand et al. 2012. Nosotros calculamos el patrón espacial de cada especie

con el patrón esperado en base a cuatro modelos (Poisson homogéneo (HPP), Poisson

inhomogéneo (IPP), Poisson agrupado homogéneo (HPCP) y Poisson agrupado

inhomogéneo (IPCP)) (Shen et al. 2009, Lin et al. 2011). Una descripción detallada de los

cuatro modelos considerados en este estudio se encuentra en el Apéndice 1.

Para seleccionar el modelo que mejor se ajustó a cada especie usamos el ajuste de bondad de

u (gof-u.)(Diggle 2003; Loosmore y Ford, 2006; Pescador et al 2014).

Diversidad en escalas espaciales

Para examinar la dispersión de los rasgos funcionales dividimos a la REA en 5 escalas

espaciales (20, 50, 500, 2000 y 10000 m2). Dentro de cada cuadrante se cuantificó la riqueza

de especies (SR siglas en ingles), dispersión funcional (FDis siglas en ingles) que es la

distancia media de las especies individuales al centroide de todas las especies en la

comunidad en un espacio funcional (Laliberté et al 2010), el FDis es una medida de

diversidad funcional. Finalmente la diversidad filogenética mediante el índice de variabilidad

filogenética de especies (PSV siglas en ingles ). El PSV resume el grado en que las especies

están relacionadas filogenéticamente en una comunidad (Helmus et al 2007).

En cada escala espacial se realizaron correlaciones de Sperman´s rho entre la dispersión


104

funcional de la comunidad y la riqueza de especies. Las correlaciones fueron realizadas para

los rasgos individualmente y para la diversidad del conjunto de datos. Adicionalmente, se

evaluó la correlación entre la diversidad funcional, filogenética y la riqueza de especies. Para

calcular la significancia de la correlación usamos una prueba de Wilcoxon.

Con el fin de determinar si el cambio de la dispersión funcional dentro de los cuadrantes es

diferente de lo esperado por el azar, generamos 1000 conjuntos de comunidades simuladas en

cada escala espacial. Utilizamos el modelo espacial con mejor ajuste para cada especie

(Apéndice 2) para construir los modelos nulos. Para cada uno de estos conjuntos calculamos

la dispersión funcional generando una distribución nula. Esta distribución nula se utilizó para

calcular el efecto estandarizado del tamaño de la siguiente manera:

Ztd = (TDobs - TDnull) / TDsdnull

donde TDnull es el valor medio de la distribución nula de la dispersión de rasgos y TSDsdnull es

la desviación estándar de la distribución nula (Gotelli y Graves, 1996). Nosotros también

testamos si la mediana de Ztd para todos los cuadrantes REA era diferente de una expectativa

nula de cero mediante una prueba de Wilcoxon.

Todos cálculos se efectuaron utilizando el entorno R (R Core Team 2013) y los paquetes

“spatstat” (Baddeley y Turner 2005), “ecespa” (de la Cruz 2008a), “FD” (Laliberté et al.

2014), “picante”(Kembel y Ackerly 2011).

RESULTADOS

Inicialmente encontramos una relación positiva entre la dispersión funcional de los rasgos y

la riqueza de especies en escalas espaciales cortas (0-500m2) y una relación negativa a

escalas espaciales mayores. En el caso de Masa seca de la semilla la relación fue positiva en


105

todas las escalas espaciales (Fig. 1). Nosotros encontramos que existió una relación positiva

significativa en escalas cortas (0-100m2) para casi todos los rasgos funcionales excepto para

altura máxima. En el caso de Masa seca de la semilla fue significativo en casi todas las

escalas espaciales (Fig.1).

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Spe

arm

an

's r

ho

FDIs

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Spe

arm

an

's r

ho

FDIs.Height

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Spe

arm

an

's r

ho

FDIs.Wood.Density

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Sp

earm

an

's r

ho

FDIs.Leaf.area

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Sp

earm

an

's r

ho

FDIs.SLA

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Sp

earm

an

's r

ho

FDIs.MSS

20 50 200 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

area

Spe

arm

an

's r

ho

PSVs

Figura 1. Correlaciones de Spearman´s rho entre la dispersión de los rasgos funcionales de la

comunidad en cada escala espacial. Círculos negros indican correlaciones significativas (P ≤ 0.05).

Área en m2

Nosotros observamos que la desviación de la dispersión funcional y diversidad filogenética

de la comunidad fueron significativos a escalas espaciales cortas (0-40 m2), encontrando un

mayor agrupamiento con respecto a lo esperada por el azar (Fig.2). Para la riqueza de

especies encontramos que la desviación fue significativo en escalas entre 0-1000 m2. En el

caso de la diversidad filogenética encontramos que a escalas espaciales de 100m2

la

desviación es mas dispersa con respecto a lo esperada por el azar (Fig.2).


106

En general todas la desviaciones de la dispersión de los rasgos funcionales fueron

significativos en escalas espaciales cortas (0-40m2). En el caso de densidad de la madera y

tamaño de la hoja fueron significativos también a escalas espaciales de 40-100m2, para SLA

y MSS a escalas de 40-500m2

(Fig.2).

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

FDIs

area

med

ian

Z-s

core

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

PSVs

area

med

ian

Z-s

core

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

SR

area

med

ian

Z-s

core

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

FDIs.Height

area

me

dia

n Z

-sco

re

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

FDIs.Wood.Density

area

me

dia

n Z

-sco

re

20 50 100 500 2000 10000-1

.0-0

.50

.00

.51

.0

FDIs.Leaf.area

area

me

dia

n Z

-sco

re

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

FDIs.SLA

area

med

ian

Z-s

core

20 50 100 500 2000 10000

-1.0

-0.5

0.0

0.5

1.0

FDIs.MSS

area

med

ian

Z-s

core

Figura 2. Desviación de las comunidades al modelo nulo a diferentes escalas espaciales. Los valores

por encima de la línea indican desviaciones positivas (dispersión de rasgos) y los valores por debajo

de la línea desviaciones negativas (agrupamiento de rasgos). Círculos negros indican desviaciones

significativas del modelo nulo mediante una prueba de Wilcoxon. (P ≤ 0.05), área en m2

Finalmente encontramos que existió una relación positiva significativa entre la dispersión

funcional de la comunidad y la riqueza de especies en escalas espaciales cortas (0-100m2) y

negativa significativa a escalas espaciales mayores (Fig. 3). En cuanto a la relación entre la

dispersión funcional de la comunidad y la diversidad filogenética fue positiva significativa a


107

escalas mayores (Fig. 3). En el caso de la relación entre la diversidad filogenética de la

comunidad y la riqueza no fue significativa en ninguna escala espacial (Fig. 3).

20 50 100 200 500 1000 5000

-1.0

-0.5

0.0

0.5

1.0

area

Sp

ea

rma

n's

rh

o

SR vs FDis

20 50 100 200 500 1000 5000

-1.0

-0.5

0.0

0.5

1.0

area

Sp

ea

rma

n's

rh

o

FDis vs PSV

Figura 3. Correlaciones entre dispersión funcional y filogenética a diferentes escalas espaciales.

Círculos negros indican correlaciones significativas mediante una prueba de Wilcoxon (P ≤ 0.05).

Área en m2

DISCUSIÓN

Nosotros encontramos que la dispersión funcional de los rasgos individuales y para su

conjunto en escalas cortas muestran agrupación funcional (Fig. 2), sugiriendo que las

condiciones abióticas locales (filtrado de hábitat) permiten la coexistencia de un sólo

subconjunto funcional en la piscina de especies (Webb et al., 2002). La filtración del medio

ambiente ocurra cuando las especies comparten tolerancias similares (Cavender-Bares et al.

2004, 2006), es decir ocupan el mismo tipo de entornos (Donoghue 2008), como por ejemplo

variabilidad en las condiciones climáticas, edáficas o topológicos, que sufren algunos

comunidades, y por lo tanto, las especies concurrentes deben ser funcionalmente más

similares.

En el caso de la diversidad filogenética encontramos que las especies fueron mas similares


108

filogenéticamente en escalas cortas. Es decir que las especies estrechamente emparentadas

comparten rasgos similares (Kraft et al. 2007; Losos et al. 2008), mientras que las especies

relacionadas lejanamente difieren en sus rasgos funcionales (Webb et al. 2002; Swenson and

Enquist 2009). Sin embargo este patrón puede cambiar a escalas espaciales mayores donde la

diversidad filogenética es mas dispersa, es decir que las especies que se encuentran en estos

sitios son mas alejadas filogenéticamente produciéndose un efecto de limite de similitud

donde las especies que se encuentran en estas escalas deben ser menos similares.

Como observamos la dispersión funcional se correlaciona con la diversidad filogenética, esta

correlación depende de los rasgos considerados en el estudio y del nivel de conservadurismo

filogenético (Srivastava et al. 2012; Mouquet et al. 2012; Losos et al. 2008). Desde una

perspectiva de la comunidad, hay estudios teóricos que indican que un aumento de la

diversidad filogenética en una comunidad aumenta el potencial evolutivo de adaptarse a los

cambios ambientales (Mouquet et al. 2012; Sgro, et al. 2011). Esta relación entre la

dispersión funcional y la diversidad filogenética se basa en la suposición razonable de que la

diversificación evolutiva ha generado la diversificación del rasgo, que a su vez puede dar

lugar a una mayor complementariedad de nicho (Cadotte et al. 2008).

Conclusión

En las 9-ha de la REA, nuestros resultados demuestran que funcionalmente las especies son

mas similares en escalas espaciales pequeñas, indicando la importancia de filtrados abióticos.

Es decir las condiciones abióticas locales permite la coexistencia de sólo un subconjunto

funcional en la piscina de especie. Los resultados también apoyan la hipótesis de que las

especies se encuentran filogenéticamente mas agrupadas a escalas pequeñas es decir son

filogenéticamente mas similares y que a escalas superiores este comportamiento cambia

siendo mas distinta de los esperado por el azar.


109

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Apéndice 1: Detalles matemáticos de los modelos ajustados

1. Proceso homogéneo de Poisson (HPP)

En este proceso se define por su intensidad λ que normalmente se aproxima como λ = n / A, es decir,

la relación entre el número de puntos n y el área A de la ventana de observación. Su parámetro

denota la intensidad del patrón y describe el número medio de los puntos localizados en una unidad de

superficie, es decir, la densidad media de árboles por unidad de área para cada especie (Plotkin et al.

2000, Shen et al. 2009). K(r) es el número esperado de puntos en un círculo de radio r alrededor de

un punto arbitrario. La función K para un patrón de puntos homogéneo es estimada como:

donde A y n son el área y el número de puntos en la parcela respectivamente, wij es un factor de

corrección de borde, y la función indicador I. El HPP tiene una intensidad constante a lo largo del área

de estudio y la distribución de los puntos es independiente, por lo que el valor esperado es K(r) = πr2

(Illian et al. 2008).

2. Proceso de Poisson cluster (PCP)

Procesos Poisson clúster puntos no-independientes (agrupados) en un proceso de dos pasos. Primero,

se genera un HPP de puntos “padres” con intensidad. Luego cada “padre” produce un número

aleatorio de “descendientes” que se distribuyen de acuerdo a una ley de Poisson, con media =/,

donde representa la intensidad de los descendientes; y sus ubicaciones son independientes,

isotrópica y normalmente distribuidos alrededor de cada árbol padre, con media cero y desviación

estándar σ (Stoyan y Stoyan 1994). La función K esperada para un PCP es

Los parámetros y son usualmente ajustados comparando la función K empírica con la función K

teórica, mediante el método de mínimo contraste (Stoyan y Stoyan 1994, Diggle 2003). De esta


115

forma, la agregación del patrón es cuantificada por (número medio de grupos), y el tamaño medio

de los grupos está dado por . El PCP considera la distribución de descendientes como una función

de dispersión limitada y asume homogeneidad, sin embargo las características que conlleva esta

condición podrían no cumplirse por muchas especies debido a la heterogeneidad ambiental y

asociación de hábitat (Gunatilleke et al. 2006, Wiegand et al. 2007b, Morlon et al. 2008).

Proceso Poisson no homogéneos (IPP).

El IPP supone la formación del patrón en dos etapas, primero se genera un patrón de Poisson

homogéneo, luego los puntos del patrón son retenidos con una probabilidad proporcional a la

superficie (x), la cual describe la heterogeneidad ambiental. En ausencia de valores de variables

ambientales, esa superficie es estimada con una función kernel. Este proceso puede ser utilizado para

examinar los efectos de interacción entre la densidad de árboles y los factores ambientales (Shen et al.

2009). Este proceso se estima como:

donde x={x1,…,xn}A, w es el factor de corrección de borde (Ripley 1977, Baddeley 1999).

El valor esperado de la función K heterogénea para un IPP es . La función de intensidad

(x) puede ser estimada en diferentes formas. Aquí utilizamos el método basado en “kernel

smoothing” (Baddeley 2010), que estima la intensidad en la ubicación u como

,

donde k(u) es una función kernel arbitraria y e(u) es término de corrección de borde. Utilizamos

kernel Gaussiano bidimensional como función de suavizamiento.

Proceso de Poisson cluster heterogéneo (IPCP)

Como una alternativa al IPP, es considerado también una extensión del HPCP, donde la distribución


116

de los puntos además de ser agrupada es heterogénea. Waagepetersen (2007) considera un proceso

cluster X=XcY donde Xc son los cluster de los “descendientes” asociados a los puntos “padres” c en

un proceso de Poisson estacionario Y con intensidad > 0. La función de intensidad de X tiene forma

log-lineal, y el valor esperado para la función K heterogénea tiene la misma forma que para el HPCP.

Este proceso es adecuado para evaluar el efecto conjunto de heterogeneidad de hábitat y dispersión

limitada (Diggle 2003, Illian et al. 2008), de forma similar al HPCP, excepto por el número de

descendientes por padre, el cual ya no es una constante, sino que debe ser estimado a través de una

función de intensidad espacialmente heterogénea (Waagepetersen 2007, Yi-Ching Lin et al. 2011).

Los modelos fueron equipados con código basado en la spatstat paquetes R (Baddeley y Turner 2005)

y ecespa (De la Cruz, 2008). Proporcionamos este código y ejemplos de su uso en un nuevo paquete

de R (selectspm) disponible en los servidores CRAN habituales (cran.r-project.org). Tanto las

funciones-K homogéneos y no homogéneos se estimaron utilizando la corrección borde isotrópica de

Ripley (Ripley, 1978). Todas las funciones se estimaron hasta 75 m (es decir, r max = 75 m), con

pasos de 1 m.


117

Appendice 2:

Tabla.1: valores de ajuste de bondad de u (gof-u) de los diferentes modelos adaptados a cada especie

IPCP: Proceso de agrupación de Poisson no homogénea; IPP: Procesos de Poisson no homogéneo

Species Best model value-u

1. Armatocereus. cartwrightianus IPCP 94235537

2. Bursera.graveolens IPCP 5847679

3. Caesalpinia.glabrata IPCP 31586555

4. Coccoloba.ruiziana IPCP 65851604

5. Colicodendron.scabridum IPCP 983903.6

6. Cordia.lutea IPCP 506813760

7. Croton rivinifolius IPCP 12895271

8. Erythrina.velutina IPCP 107762648

9. Erythroxylum.glaucum IPCP 4196608

10. Geoffroea.spinosa IPCP 6905221

11. Leucaena.trichodes IPCP 12515970

12. Malphigia.emarginata IPCP 5186331

13. Piptadenia.flava IPCP 15047050

14. Tabebuia.chrysantha IPCP 13088950

15. Achatocarpus.pubescens IPP 94235537

16. Chloroleucon.mangense IPP 8431424

17. Cochlospermum.vitifolium IPP 991187.5

18. Cynophalla.mollis IPP 1461603

19. Eriotheca.ruizii IPP 3137833

20. Jacquinia.sprucei IPP 56464279

21. Pisonia.aculeata IPP 72624176

22. Pithecellobium.excelsum IPP 11102231

23. Tabebuia.billbergii IPP 965215.2


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121

CAPÍTULO 4

Mechanisms of community assemblage at level

taxonomic, functional and phylogenetic in a scale

spatial


122


123

ABSTRACT

Recently, ecologists have moved from measuring diversity as the species richness and

evenness, to using measures that reflect ecological differences as use of the functional and

phylogenetic diversity of species, because several evidences that pointed to consider

functional traits as better predictors of diversity.

We are interesting in know the individual species–area relationships (ISAR) to measure to

species richness in local neighborhoods around the individuals of a focal species within a

community. Individual Functional Diversity-Area Relationship (IFDAR) to measure scale-

dependent, local functional diversity structures around individual species, and individual

Phylogenetic Species Variability Area Relationship (IPSVAR) to measure of the

phylogenetic distance between species. We have used these tools in a dry forest in south of

Ecuador.

Our results showed that at all the spatial scales considered for ISAR, IFDAR and IPSVAR,

most species behaved as ‘neutral’, i.e. they are surrounded by the expected richness. Some

species in IFDAR (39%), IPSVAR (17%), were accumulator the functional and phylogenetic

diversity, suggesting the existence of a process of limiting similarity, i.e. the species are more

functionally different than expected. A small proportion of species was repeller of functional

and phylogenetic diversity than expected, suggesting the existence of an additional process

habitat filtering.

In this study highlights how analyzing alternative dimensions of biodiversity, such as

functional and phylogenetic diversity, may help us understand the co-occurrence of species in

diverse assemblages. These results suggest that some species showed a different behavior to

the plant-plant interactions, where the abiotic interactions and environment are important in

the community assembly at functional and phylogenetic levels.


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INTRODUCTION

Historically, species richness has been the most common metric used to characterize local

and regional biodiversity of ecosystem. However, a more accurate assessment of biodiversity

can be achieved by studying simultaneous the three diversity, taxonomic, functional and

phylogenetic (Swenson 2011). For example, use of only the taxonomic diversity for

conservation or management could mask losses of functionality of ecosystem (Díaz and

Cabido 2001). Functional diversity is the variety of life-history traits presented by an

assemblage of organisms (Mayfield et al. 2006), and is critical to the maintenance of

ecosystem processes (Hooper and Vitousek 1997; Tilman and Downing 1994; Tilman et al.

1997). Thus, functional diversity adds an important dimension to the conventional

characterization of biodiversity (Faith 2015). Phylogenetic diversity describes the total

phylogenetic distance among species in a community (Faith 1992) and provides a measure of

future biodiversity, identifying phylogenetic patterns in community composition can generate

hypotheses about the abiotic and biotic factors structuring communities (Helmus et al. 2007).

During the past decade ecologists have increasingly utilized phylogenetic and functional

traits information to estimate the ecological similarity of species in order to test mechanistic

community assembly hypotheses (Swenson 2013), because several evidences pointed to

consider functional traits to be better predictors of diversity (Tilman et al. 1997; Cadotte et al.

2009; Flynn et al. 2011; Helmus and Ives 2012). A common idea in the study of ecology is

that taxonomic, functional and phylogenetic diversity are positively correlated, that is,

accounting for one of these components also accounts for the others. However, recent studies

(Cadotte et al. 2013; Pavoine and Bonsall 2011; Pavoine et al. 2013) suggested that these

components might also represent independent aspects of community structure (Dainese et al.

2015).


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Have developed newfangled statistical analysis for the plant diversity study, by using spatial

patterns, as is the use of ISAR (His Individual Species–Area relationship) that computes

species richness in local neighborhoods around the individuals of a focal species within a

community. As a consequence a particular species can be identified as an accumulator or

repeller of diversity, or as neutral. Although repelling can be interpreted as the expectation of

a prevalence of “competition” between species, and facilitation in the case of accumulation,

this must be done with caution and only after partialling out the possible existence of other

mechanisms, which can give also similar spatial signals (Espinosa et al.2015). Both

accumulators and repellers have been taken as evidence for non-neutral or niche- based

processes influencing the distribution and diversity of tree species in forest communities.

Conversely if the species diversity around a target individual does not deviate significantly

from that expected, this is taken as evidence for neutrality (Wiegand et al. 2007). The

proportion of these plant types and abundance within a community could shed light on the

mechanisms ruling coexistence in mega-diverse communities. For example, interspecific

aggregations of plants are often interpreted as a result of positive plant-plant interactions that

are generally considered to play a prominent role in semiarid plant community dynamics

because of harsh climate and scarce resources in these environments (Schlesinger et al. 1990;

Kéfi et al. 2007; Pugnaire et al. 2011; Espinosa et al. 2014).

Despite the clear impact of the approach developed by Wiegand et al. (2007). It has been

expanded this vision of spatial processes occurring in the community assembly, using tools

such measure of functional and phylogenetic diversity as the IFDAR (Individual functional-

diversity–area relationship) and IPSVAR (Individual Phylogenetic Species Variability Area

Relationship).

The IFDAR quantifies the functional diversity and the IPSVAR the phylogenetic diversity of

species within circular areas around a target individual of a species. The IPSVAR is then


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compared with the expected random ratio taking into account the phylogenetic diversity of

species observed in the neighborhood, and IFDAR is compared with the expected random

ratio taking into account the traits diversity of species observed. The simultaneous

comparison of these functions and null models will give new insights of the process and

timing implied in the organization of the forests (Helmus and Ives 2012).

The aim of this study is to evaluate the species behavior in the three levels of diversity using

ISAR- for the level taxonomic, IFDAR for the level functional and IPSVAR for the level

phylogenetic. We test these patterns in the Neotropical dry forest, this ecosystem is especially

interesting because it is periodically subjected to a strong abiotic stress, which has led to

species develop patterns and morphofunctional adaptations. Some studies have showed that

in this ecosystem, the facilitation is stronger under stress conditions and it reduced when

conditions improve (e.g. Espinosa et al. 2014, 2011), additionally, to local scales Espinosa et

al. (2015) found interesting evidence that to local scales exist different processes acting such

as facilitation and habitat association. We are interested in disentangle the mechanisms of

community assemblage at local spatial scale. Specifically, we are interesting in know 1) if

plant-plant interactions define structure in our dry forest community and if it is mediated by

habitat filtering or limiting similarity, or both, and 2) to what extent the environmental

conditions define the structure of community. When plant-plant interactions dominate, we

expected that species would present accumulator or repeller behavior at the taxonomic level.

Accumulator behavior at taxonomic level means positive plant-plant interaction; if these

interactions are mediated by limiting similarity at the same scale, we expected that the

species would present the same behavior at functional and phylogenetic levels. Conversely if

species show repeller behavior at taxonomic level, and this behavior is mediated by habitat

filtering, we expect repeller behavior at functional and phylogenetic levels.

On the other hand, if environmental conditions rather than plant-plant interactions define the


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community assembly, we expect a repeller pattern at functional and phylogenetic level if

habitat filtering is predominant, but an accumulator pattern if the limiting similarity is

predominant.

Methods

Study Site

The study was conducted in the Arenillas Ecological Reserve (REA, from its Spanish name),

located in southwestern Ecuador (03º34′15.44′′S; 80º08′46.15′′E, 30 m a.s.l.), in El Oro

province, between the towns of Arenillas and Huaquillas. This reserve covers 131,7 Km2

(Decreto Ejecutivo No. 787), with an altitude ranging from 0 to 300 m. The climate is

characterized by a rainy season with an average annual rainfall of 515 mm from January to

April (wet season) and only 152 mm on average during the eight-month dry season (weather

station Huaquillas for a record period of 45 years (1969-2014)). The average temperature is

21-25 °C with a maximum variation of 3.4 ºC in the coldest and warmest reaching the lowest

temperature during the dry season. These dry forests are considered the most threatened

ecosystems in Ecuador (Sierra 1999; Gentry 1977; Espinosa et al. 2015).

In the center of the REA in 2009, a permanent plot of 9 hectares was set up and marked all

trees and shrubs greater than 5 cm and identified. During the dry season (July to September

2010 and 2011) were mapped using a total station, Leica TS02-5 Power model with less than

5 cm accuracy.

Functional trait collection

The following functional traits were collected for 38 species between trees and shrubs that

growing in the permanent plot the traits were: maximum height (Hmax) (is the distance

between the top of photosynthetic tissue and the ground), leaf area (in square centimeters),

specific leaf area (SLA, in square centimeters of fresh leaf area per gram of dry leaf mass),


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wood density (in grams per square centimeter) and seed mass (in grams). The traits were

measured following standardized protocols (Pérez-Harguindeguy et al. 2013). For each

species, we randomly collected 10 leaves from each of 50 individuals. Leaf area was

measured with the program Image J (Abramoff et al. 2004; Kraft et al. 2010). Specific leaf

area was calculated, as the fresh leaf area divided by the leaf dry mass (48 hours drying at 80

ºC), for species of compound leaves the rachis was included. They were collected during the

phenologic peak. To estimate wood density secondary branches of three individuals were

collected with size in circumference ranging from 10 to 20 cm, fresh wood volume was

determined with the water displacement method (Chave 2006), after which samples were

oven-dried at 80ºC and weighed for species that we could not calculate the wood density, was

used the database of Chave et al (2009). Seed mass was recorded of the Kew Millennium

Seed database (Royal Botanic Gardens Kew 2014).

Phylogeny construction

A phylogenetic tree was constructed for these 38 species in the plot, using Phylomatic (Webb

and Donoghue 2005). Phylomatic utilizes the Angiosperm Phylogeny Group III (APG III

2010). Taxonomic relationships (family, genus and species) are pasted onto this backbone to

estimate the phylogenetic tree. Branch lengths were estimated for each tree using the BLADJ

algorithm implemented in Phylocom (Webb et al. 2008), to adjust the length of the branches

of the phylogeny with known ages of fossils of plants (Wikström et al. 2001). The ''ape”

library was used to import and manipulate the phylogeny, as required in R (Paradis 200, R

Development Core Team 2009). Our metric used for of phylogenetic analysis was

phylogenetic species variability (PSV) this measure of phylogenetic species variability

summarizes the degree to which species in a community are phylogenetically related

(Helmus et al. 2007)


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Spatial pattern analysis

To select appropriate null models for each species, we followed an approach conceptually

similar to the pattern reconstruction strategy of Wiegand et al. (2013) and implemented in

Chacón et al. (2013). We first fitted a battery of different spatial null models for each species:

(1) a homogeneous Poisson process (HPP) with constant intensity λ equal to the density of

the observed pattern; (2) a homogeneous Poisson cluster process (HPCP) with constant

intensity λ and parameters σ and ρ fitted by minimum contrast (Diggle 2003); (3)

heterogeneous Poisson processes (IPP) with an intensity function λ (x,y) estimated with a

Gaussian kernel (Wiegand et al. 2007) with 13 different σ values (bandwidths) from σ = 15 to

σ= 75 m with a sequence of 5 m; and, finally, (4) heterogeneous Poisson cluster processes

(IPCP) (Waagepetersen 2007) with sigma-values similar to IPP. A detailed description of the

four models of processes considered in this study is located in Appendix 1, of Chapter 3 of

the thesis. We performed 199 simulations for each null model. To avoid difficulties due to

small sample size we only calculated functions for species with at least 10 individuals.

Best model for each species was selected with the goodness-of-fit u statistic (Diggle 2003;

Loosmore and Ford, 2006; Pescador et al. 2014).

Taxonomic, Functional and phylogenetic diversities

We analyzed our community in three dimensions: Taxonomic using the ISAR, functional by

IFDAR, and phylogenetic by IPSVAR.

For taxonomic diversity first, we computed ISAR for ‘adult’ individuals with DBH 10 cm,

which in our ecosystem is the dominant life stage (Linares-Palomino and Ponce-Alvarez

2009); these functions summarize how community richness is organized around individual

species once they reach maturity (Wiegand et al. 2007).

IFDAR allows the calculation of functional diversity around individual species to be


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evaluated against the expectations of some assumed ecological processes and corresponding

null models, for example, if competitive effects were ruling the structure of a community, we

would expect an increase (i.e., accumulation) of FD around individual species. On the

contrary, if only habitat filtering is affecting the assembly process, we should find that most

species have small values of FD in their neighborhoods. Even more if several processes were

simultaneously ruling the community assembly, IFDAR could detect there differential

effects, at least when these are occurring at contrasting spatial scales (De la Cruz in prep).

IFDAR is able to detect both positive and negative deviations of functional diversity from the

expectations of a heterogeneous null model.

IPSVAR is then compared with the expected random ratio taking into account the

phylogenetic diversity of species observed in the neighborhood, closely related species might

have similar tolerances to similar environmental stressors and thus be more likely to occur

within the same community than with less related species (e.g., Webb 2000). Conversely,

closely related species might share the same resource requirements, and therefore

competition could prevent similar species in the same community (Elton 1946).

The goodness of fit of the models were evaluated in three ranges of radius 0 to 20 m; 21 to 40

m and 41 m- 60 m, to test for differences between the observed pattern and the null model

of ISAR, IFDAR and IPSVAR

All calculations were done using the R statistical software, version 3.1.0 (R Development

Core Team, 2014). The ISAR analyses were implemented using the statistical package

‘‘idar’’ (De la Cruz, in prep.). The randomization process was simulated using the packages

‘‘spatstat’ (Baddeley and Turner 2005), ecespa (De la Cruz, 2008) and “picante” (Kembel et

al. 2010).


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Results

We tagged and georeferenced 9479 individuals with DBH greater than 5 cm in the 9-ha plot.

We found 38 species, 19 of trees and 19 of shrubs. We selected 23 species (14 trees, 9

shrubs) with more than 10 individuals, belonging to 14 families. (Appendix A).

The best fit for spatial patterns were homogeneous Poisson cluster for fourteen species, and

homogeneous Poisson processes for nine species (Appendice 1). In all cases, the best models

included intensity surfaces estimated from their own patterns with Gaussian kernels.

Taxonomic, Functional and phylogenetic diversities

Considering the ranges of radius r= 0–60 m, the most prevalent behavior for the 23 species

was neutral for ISAR with 57%, (13 species); IFDAR 60 % (14 species); and 70 % (16

species) for IPSVAR (Fig. 1).

0 20 40 60

02

04

060

80

100

ISAR

Radius r of circle (m)

Pe

rce

nta

ge

of ca

ses

neutrals

accumulators

repellers

0 20 40 60

02

04

060

80

100

IFDAR


Pe

rce

nta

ge

of ca

ses

neutrals

accumulators

repellers

0 20 40 60

02

04

060

80

100

IPSVAR


Pe

rce

nta

ge

of ca

ses

neutrals

accumulators

repellers

Figure1: Proportion of significant accumulator, repeller and neutral species for ISAR (Individual

Species-Area Relationships), IFDAR (Individual Functional-Diversity–Area Relationship) and

IPSVAR (Individual Phylogenetic Species Variability Area Relationship) in the REA, in a radius of

0-60 m.

For ISAR (taxonomic diversity) six species were accumulators (26%) in all spatial scales

(r=0-60 m), and Chloroleucon mangense was the only repeller of diversity in all-spatial


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scales. In IFDAR (functional diversity) one species were accumulators (Jacquinia sprucei)

and one repeller (Armatocereus sp) of diversity (4%) in all-spatial scales (r=0-60 m). With

respect IPSVAR only was accumulator and repeller in certain spatial scales (Table 2).


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Table 2. p-values of the GoF u test for three spatial ranges: 0–20, 21–40 and 41–60 m. Grey background: species that behaved as accumulators in the

evaluated range. Black background: species that behaved as repellers in the evaluated range. Neutral behavior is showed as white cells and n.c. indicates that

ISAR, IFDAR and IPSVAR, were not calculated.

Species ISAR IFDAR IPSVAR

R0-20 R21-40 R41-60 R0-20 R21-40 R41-60 R1-20 R21-40 R41-60

Achatocarpus pubescens 0.91 0.94 0.16 0.94 0.02 0.05 0.56 0.52 0.64

Armatocereus sp. 0.16 0.41 0.5 0.01 0.01 0.01 0.15 0.04 0.04

Bursera graveolens 0.24 0.07 0.1 0.56 0.85 0.9 0.1 0.11 0.16

Caesalpinia glabrata 0.01 0.01 0.03 0.42 0.38 0.66 0.95 0.48 0.1

Chloroleucon mangense 0.01 0.01 0.01 0.1 0.04 0.01 0.68 0.05 0.24

Coccoloba ruiziana 0.15 0.29 0.05 0.65 0.7 0.23 0.34 0.65 0.44

Cochlospermum vitifolium 0.2 0.07 0.11 0.72 0.29 0.69 0.48 0.13 0.04

Colicodendron scabridum 0.57 0.07 0.14 0.09 0.77 0.35 0.31 1 0.48

Cordia lutea 0.08 0.68 nc 0.47 0.81 nc 0.81 0.55 nc

Croton sp 0.33 0.63 0.9 0.09 0.38 0.37 0.07 0.62 0.39

Cynophalla mollis 0.01 0.01 0.01 0.03 0.38 0.16 0.7 0.07 0.01

Eriotheca ruizii 0.01 0.01 0.02 0.05 0.48 0.67 0.69 0.45 0.91

Erythrina velutina 0.36 0.14 0.13 0.02 0.37 0.99 0.79 0.07 0.08


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ISAR (Individual Species–Area Relationship), IFDAR (Individual Functional-Diversity–Area Relationship) and IPSVAR (Individual Phylogenetic Species Variability Area

Relationship).

Erythroxylum glaucum 0.02 0.39 0.43 0.33 0.76 0.59 0.51 0.8 0.7

Geoffroea.spinosa 0.01 0.01 0.01 0.13 0.42 0.84 0.53 0.84 0.11

Jacquinia sprucei 0.01 0.01 0.01 0.01 0.01 0.01 0.05 0.01 0.01

Leucaena trichodes 0.57 0.41 0.64 0.12 0.67 0.56 0.04 0.5 0.94

Malphigia emarginata 0.01 0.01 0.02 0.01 0.51 0.1 0.21 0.26 0.66

Piptadenia flava 0.27 0.19 0.42 0.20 0.07 0.59 0.04 0.59 0.82

Pisonia aculeata 0.05 0.54 0.71 0.46 0.78 0.68 0.43 0.15 0.52

Pithecellobium excelsum 0.28 0.02 0.01 0.5 0.04 0.05 0.21 0.09 0.08

Tabebuia billbergii 0.55 0.13 0.09 0.01 0.07 0.49 0.57 0.52 0.64

Tabebuia chrysantha 0.12 0.46 0.35 0.12 0.3 0.44 0.08 0.37 0.41


135

In ISAR some species showed different spatial patterns depending on the proximity to the target

plant. For example, Erythroxylum glaucum accumulated diversity at short distances (r= 0-20 m)

and Pithecellobium excelsum, accumulated taxonomic diversity at large distances (r= 21-60 m)

(Table 2). For IFDAR five species (22%) accumulated diversity at short distances (r= 0-20 m),

and two species (9%) were accumulators in middle distances (r= 21- 40 m) (Table 2). Finally,

Chloroleucon mangense were repeller of diversity in greater distances (r= 21-60 m) (Table 2).

We found three species (13%) that behaved as accumulators in IPSVAR to large distances

Armatocereus sp. (r= 21- 60 m), Cynophalla mollis and Jacquinea sprucei (r= 41-60). Five

species (26%) behaved as repellers of phylogenetic diversity in the IPSVAR at short distances (4

species) and only one species at large distances (Cochlospermum vitifolium)(Table 2).

Cynophalla mollis showed an accumulator behavior so much for ISAR, IFDAR and IPSVAR in

different scales spatial (Fig.2).

0 20 40 60

0

5

10

15

20

25

30

a) Cynophalla mollis-ISAR

r

ISA

R(r

)

i sarobs(r)i sar(r)i sarhi(r)i sar l o(r)

0 20 40 60

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

b) Cynophalla mollis-IFDAR

r

IFD

AR

(r)

i fdarobs(r)i fdar(r)i fdarh i(r)i fdar lo(r)

0 20 40 60

0.80

0.85

0.90

c) Cynophalla mollis-IPSVAR

r

IPS

VA

R(r

)

ipsvarobs(r)ipsvar(r)ipsvarhi(r)ipsvar lo(r)

Figure 2: Proportion of significant accumulators for Cynophalla mollis (a) ISAR (Individual Species–

Area Relationship) in radius of 0-60 m, (b) IFDAR (Individual functional-diversity–area relationship) in

radius of 0-20 m, (c) IPSVAR (Individual Phylogenetic Species Variability Area Relationship) in radius


136

of 40-60 m.

Our results also showed that five out of eight species that showed an accumulator behavior in

ISAR showed the coincident behavior in IFDAR, however the accumulator effect in IFDAR was

significantly only at short scales (Table 2).

Chloroleucon mangense was the only species that behaved as a repeller in the ISAR and IFDAR

(Fig. 3).

0 20 40 60

0

5

10

15

20

25

30

a) Chloroleucon mangense-ISAR

r

ISA

R(r

)

i sarobs(r)i sar(r)i sarhi(r)i sar lo(r)

0 20 40 60

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

b) Chloroleucon mangense-IFDAR

r

IFD

AR

(r)

i fdarobs(r)i fdar(r)i fdarhi(r)i fdar lo(r)

Figure 3. Proportion of significant repeller for Chloreucon mangense (a) ISAR (Individual Species–Area

Relationship) in radius of 0-60 m, and (b) IFDAR (Individual functional-diversity–area relationship) in

radius of 21-60 m.

In the case of Armatocereus sp, was repeller en IFDAR and accumulator of

diversity in IPSVAR, Jacquinea sprucei was accumulator in IFDAR and repeller

of diversity in IPSVAR (Table 2).


137

Discussion

Alternative dimensions of biodiversity as functional and phylogenetic diversity may have the

ability to convey species–area relationships and determining the degree to which processes as

random placement, habitat filtering and/or limiting similarity contribute to the spatial distribution

and assembly of communities (Webb 2000, Swenson et al. 2007, Kraft et al. 2008, Swenson and

Enquist 2009, Helmus and Ives 2012). Our results showed that at all the spatial scales considered

for ISAR, IFDAR and IPSVAR, most species behaved as ‘neutral’, i.e. they are surrounded by

the expected richness, under a null model that only accounts for environmental heterogeneity.

In the case of ISAR some species behaved as accumulators in all spatial scales. Several authors

(Maestre and Cortina 2005, Wiegand et al. 2007a, Brooker et al. 2008) suggest that in the

harshest environments some species tend to improve their surrounding environment, which

results in a strong spatial structuring of the community. Under these conditions the importance of

negative interactions is reduced and increases the importance of positive relationships (Callaway

2007), so it is expected that positive interactions such as facilitation would be more important

and consequently accumulator species would predominate over repeller ones. In particular with

IFDAR, some species were significant accumulators of diversity only on local spatial scales (0-

20 m) and generally behaved neutrally on larger spatial scales (Table 1), and this suggests that

some process limiting similarity is controlling community assembly at fine scales. If this were

the case, inter-specific competition would make that species avoid functionally similar species in

their close proximity (De la Cruz in prep). The frequent clustering of species accumulators on the

functional diversity may be also driven by facilitation, allowing different species around it. In the


138

case of Chloroleucon mangense was a repeller for ISAR and IFDAR, this repeller behavior could

be the consequence of a clumped spatial structure, where much of the living space surrounding

its individuals will be occupied by conspecifics, leaving less space for heterospecific species

(Yang et al, 2013) this process was generate for habitat filtering.

In the case of Phylogenetic diversity accumulators are hypothesized to be evidence also of

facilitation among distantly related species, while phylogenetic diversity repellers are

hypothesized to be target species where closely related species filter into the same neighboring

environment (Yang et al 2013).

Finally observe some cases as Cynophalla mollis and Jacquinia sprucei that were accumulator

for ISAR-IFDAR and IPSVAR (Table 2), these species could be acting as community engineers,

favoring the recruitment of multiple species through the improvement of environmental

conditions (Espinosa et al. 2015) allowing its around different functional species.

In this study highlights how analyzing alternative dimensions of biodiversity, such as functional

and phylogenetic diversity, may help us understand the co-occurrence of species in diverse

assemblages. These results suggest that some species showed a different behavior to the plant-

plant interactions, where the abiotic interactions and environment are also important in the

community assembly at functional and phylogenetic levels. In particular, we have shown that the

phylogenetic and functional distribution of some species are accumulators and repellers in this

dry forest is non-random indicating the importance of functional traits and past evolutionary

history in dictating the ecological interactions we presently observe.


139

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Appendice 1

Table.1: Gof u values of the different models fitted to each species IPCP: inhomogeneous

Poisson cluster process; IPP: inhomogeneous Poisson process.

Species Best model value-u

24. Armatocereus cartwrightianus IPCP 94235537

25. Bursera graveolens IPCP 5847679

26. Caesalpinia glabrata IPCP 31586555

27. Coccoloba ruiziana IPCP 65851604

28. Colicodendron scabridum IPCP 983903.6

29. Cordia.lutea IPCP 506813760

30. Croton rivinifolius IPCP 12895271

31. Erythrina.velutina IPCP 107762648

32. Erythroxylum.glaucum IPCP 4196608

33. Geoffroea.spinosa IPCP 6905221

34. Leucaena.trichodes IPCP 12515970

35. Malphigia.emarginata IPCP 5186331

36. Piptadenia.flava IPCP 15047050

37. Tabebuia.chrysantha IPCP 13088950

38. Achatocarpus.pubescens IPP 94235537

39. Chloroleucon.mangense IPP 8431424

40. Cochlospermum.vitifolium IPP 991187.5

41. Cynophalla.mollis IPP 1461603

42. Eriotheca.ruizii IPP 3137833

43. Jacquinia.sprucei IPP 56464279

44. Pisonia.aculeata IPP 72624176


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45. Pithecellobium.excelsum IPP 11102231

46. Tabebuia.billbergii IPP 965215.2


146


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CONCLUSIONES GENERALES


148


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Los resultados obtenidos en la presente investigación han permitido dilucidar algunos

procesos de ensamble de la comunidad de los bosques tropicales estacionalmente secos, a

partir de los cuales se extraen las siguientes conclusiones generales:

Confirmamos la influencia tanto de filtrado de hábitat y el limite de similitud en el

ensamble de la comunidad de los bosques secos Tumbesinos. Nuestros resultados

encontraron que los valores de los rasgo a través del gradiente de precipitación es

consistente con la idea de filtrado de habitat. Los resultados del análisis de

correlación sugieren que en los sitios más secos, la estrategia funcional consiste en

alta SLA, alta WD, alta y baja Hmax SM. Se confirmó que el gradiente también

modifica la fuerza de la similitud limitante y los procesos de filtrado hábitat, con una

escasez de las estrategias funcionales permitidos (es decir, una disminución de la

gama de valores del rasgo) y un aumento de los efectos de la competencia (a más

distribución de los valores de rasgo), para la mayoría de los rasgos, cuando las

condiciones se vuelven más seco. Estos hallazgos son muy interesantes desde el

punto de vista funcional de las interacciones de las especies, que pueden ser usados

para el manejo y conservación de estos bosques.

Si bien estos resultados son consistentes con lo propuesto para los bosques

secos tropicales, donde se evidencia baja diversidad funcional en sitios que están

sometidos a mayor estrés sugiriendo evidencias de filtrado de hábitat en los valores

tomados por la mayoría de los rasgos a lo largo del gradiente de estrés (medido como

CWN), hay evidencia de que la diversidad funcional se movió a lo largo del


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gradiente ambiental fue encontrado por cualquier rasgo excepto por el tamaño de la

hoja. Esta variación en el tamaño de la hoja es mediada por una transición del tamaño

de la hoja, es decir hojas grandes, tipico de hojas caducas, delgadas, y hojas pequeñas

con mayor espesor de especies perennes.

Un enfoque actualemente utilizado es medir como si la dispersion de los rasgos estan

relacionados con la escala especial, nuestros resultados demuestran que

funcionalmente las especies son mas similares en escalas espaciales pequeñas,

indicando la importancia de filtrados abióticos. Es decir las condiciones abióticas

locales permite la coexistencia de sólo un subconjunto funcional en la piscina de

especie. Los resultados también apoyan la hipótesis de que las especies se encuentran

filogenéticamente mas agrupadas a escalas pequeñas es decir son filogenéticamente

mas similares y que a escalas superiores este comportamiento cambia siendo mas

distinta de los esperado por el azar.

Utilizamos un nuevo enfoque metodológico mediante el análisis de las dimensiones

alternativas de la biodiversidad, como la diversidad funcional y filogenética, para

entender la co-ocurrencia de especies en diversos ensambles. Estos resultados

sugieren que algunas especies mostraron un comportamiento diferente a las

interacciones planta-planta, donde las interacciones abióticas y medio ambiente

también son importantes en la asamblea de la comunidad en los niveles funcionales y

filogenéticos. En particular, hemos demostrado que la distribución filogenética y

funcional de algunas especies son acumuladores y repelentes en este bosque seco es


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no aleatoria que indica la importancia de los rasgos funcionales y la historia evolutiva

pasado en el dictado de las interacciones ecológicas que actualmente observamos.

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AGRADECIMIENTOS

Durante estos años tengo mucho que agradecer a mucha gente! Muchos han puesto su granito de

arena en esta tesis de manera muy distinta.

"Un grano no hace un granero, pero ayuda al compañero".

A mis Padres quienes han sido mi más grande fortaleza, a mis queridos herman@s Diani, Joha,

Pame, Angel, mis sobrinos Patricio y Angelito y a mi cuñada Jessenia que es como una hermana,

gracias por ese apoyo incondicional e invaluable en las largas jornadas de campo y laboratorio,

por todos los sacrificios que han hecho para que yo realice mis estudios.

A mis directores de tesis Adrián y Marcelino, sin ustedes esta tesis, nunca habría sido posible.

Adrian, mil gracias por apoyarme y dedicarle tanto tiempo e ilusión a esta tesis! Eres un

excelente investigador y sobre todo una persona increible.

Marcelino, muchas gracias por haberme permitido hacer la tesis contigo, por tu paciencia e

ilusión durante todos estos años eres lo máximo.

Gracias a los dos por meterme de lleno en el mundo de la ciencia.

A mis compañeros del Departamento de Ciencias Naturales, especialmente a Carlos Iván quien

ha sido un pilar fundamental en el desarrollo de mi tesis, a Pablo Ramón y David Duncan

quienes me apoyaron en mis dudas estadísticas, Angel B., Anita A., Diego G, Dalton Q., Jorge

A., Andrea J., José R, Ramiro M, Bayo y Rosita quienes me apoyaron en alguna fase de mi tesis

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y en mis estancias en Madrid … A Aran Luzuriga quien me ayudo como revisora de algunos

capítulos de mi tesis… A María Belen amiga incondicional.

La realización de esta tesis fue apoyada parcialmente por los proyectos A/024796/09 y

A/030244/10 de la Agencia Española de Cooperación Internacional y para el Desarrollo

(AECID) y el proyecto Islas-Espacio CGL2009-13190-C03-02 del Ministerio de Ciencia e

Innovación. A la beca doctoral SENESCYT 2009. A los proyectos internos de la UTPL,

PROY_CCNN 0030; PROY_CCNN 1260; PROY_CCNN 1054.

GRACIAS A TODOS

Eli Gusmán M.

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