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NICOLÁS SEOANEHabitat use of semi-feral cattle1
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First records on habitat use of semi-feral cattle in southern forests: topography reveals3
more than vegetation 4
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Nicolás Seoane6
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Laboratorio Ecotono, Universidad Nacional del Comahue, Quintral 1250, (8400) Bariloche,8
Argentina. (Email: [email protected]) Tel. +54 (02944) 4233749
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NICOLÁS SEOANE ABSTRACT 24
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While the presence of cattle in forests is quite common, how they use the habitat is often26
overlooked. When this is examined, most studies focus on measurements of the vegetation27
variables influencing habitat selection. This current study provides a suitable model to study28
habitat use by livestock in forested areas. The model was applied to data from semi-feral cattle in29
order to obtain the first description of their habitat use in southern forests. Furthermore, the30
model accounted for individual variability, and hinted at population patterns of habitat use. The31
positions of four individual animals with GPS collars were recorded during four months in a32
Nothofagus (southern beech) forest in Patagonia (Argentina). By projecting these GPS location33
data into a geographical information system (GIS), a resource selection probability function34
(RSPF) that considers topographic and vegetation variables was built. The habitat use of semi-35
feral cattle in southern beech forests showed a large interindividual variability, but also some36
similar characteristics which enable a proper description of patterns of habitat use. It was found37
that habitat selection by cattle was mainly affected by topographic variables such as altitude and38
the combination of slope and aspect. In both cases the variables were selected below average39
relative to availability, suggesting a preferred habitat range. Livestock also tended to avoid areas40
of closed shrublands and showed a slight preference for pastures (meadows). This result suggests41
that cattle are showing adaptations to the major features of these mountainous forests due to42
ferality. Furthermore, these results are the basis for management applications such as predictive43
maps of use by semi-feral livestock.44
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NICOLÁS SEOANEKEYWORDS 47
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Nothofagus forests; beef cattle; semi feral cattle; resource selection probability functions (RSPF);49
GPS tracking; habitat distribution modeling. 50
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NICOLÁS SEOANEINTRODUCTION 70
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Cattle ( Bos taurus) are an important species for forest dynamics globally, and the originally72
domesticated animals have transgressed to become semi-feral to feral again in many areas73
(Decker et al. 2015, Berteaux and Micol 1992, Hernandez et al. 1999, Baker 1990, Atkinson74
2001, Lazo 1994). Livestock can affect the diversity and structure of plant communities75
(Coughenour 1991, Cingolani et al. 2008) mainly through diet selection (Rook et al. 2004), but76
also due to indirect actions such as changing the distribution of nutrients and compacting the soil77
by trampling (Milchunas et al. 1998, Cingolani et al. 2008). Thus, it is possibly one of the main78
ecosystem engineers for these environments, as has already been demonstrated in grasslands79
(Jones et al. 1997). Although the impacts of cattle are often studied, their habitat use, which80
would allow a comprehensive understanding of the livestock-forest ecological system, has81
generally been overlooked.82
Southern beech forests (i.e., Nothofagus forests) in Patagonia have been used to raise83
cattle since the late 19th century. The traditional management involves extensive cattle ranching84
in the forest. But occasionally and for different reasons, some animals escape the herd,85
establishing feral populations (Atkinson 2001, Veblen et al. 1996). Although feral cattle can be86
found in many areas of the Nothofagus forests in Patagonia (Novillo and Ojeda 2008) and New87
Zealand (Howard 1966, Veblen and Stewart 1982), no studies have been performed on their88
behavior or habitat selection in southern forests. Additionally, many levels of ferality already89
exist depending on contact with human settlements, hunting pressure, isolation time, etc.90
The proportion of time an animal spends in a location to meet their biological demands91
defines its habitat use (Beyer et al. 2010). As resources are often heterogeneously distributed,92
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NICOLÁS SEOANEindividuals need to explore multiple environments to satisfy their needs (Law and Dickman93
1998). Therefore, the selection of habitats is a key feature of behavior and population dynamics94
(Morris 1987). This is especially relevant for large herbivores, because their distribution on the95
landscape can decisively influence the productivity and biodiversity of fields and pastures96
(Bailey and Provenza 2008).97
There are multiple relationships between livestock and their environment but one of the98
major ones is the change in the heterogeneity of vegetation produced by foraging (Adler et al.99
2001). In the case of forests, despite having high heterogeneity and frequent cattle presence,100
there is no systematic review of the resulting ecological processes due to the introduction of this101
herbivore worldwide. Furthermore, it is common to have little or no information about the102
activity patterns of semi-wild cattle in general, which makes their management even more103
difficult. Some studies that have been conducted in different forests account for diet selection104
along seasons (Marquardt et al. 2010) or in relation to anthropogenic changes, usually due to105
forest management practices (Roath and Krueger 1982, Kaufmann et al. 2013). In these studies,106
vegetation type and topography were the most common variables to explain habitat selection by107
cattle in forests.108
A variety of technological and statistical tools can be applied to study habitat use. Among109
the first, radiotelemetry and GPS devices allow for accurate and frequent data collection on110
animal location, which facilitates the development of mechanistic models of habitat use and111
movement (Beyer et al. 2010). Therefore, their use is widespread (Johnson et al. 2004, Ungar et112
al. 2005, Cagnacci et al. 2010, Peinetti et al. 2011). In addition to these data collection methods,113
habitat use can be modeled by resource selection functions (RSF) and resource selection114
probability functions (RSPF) that estimate the probability that a given environment has been115
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NICOLÁS SEOANEused by an animal (Boyce and McDonald 1999, Beyer et al. 2010). Thus, the RSFs provide a116
framework for developing an ecological theory of habitat use and allow linking landscape117
ecology and population biology (Boyce and McDonald 1999, Boyce 2006). One way to build a118
RSPF with GPS data is to model the intensity of use in sampling units, relating the relative119
frequency of records per unit to the environmental features of such sampling units (Nielson and120
Sawyer 2013).121
The objective of this study was to evaluate the habitat use of semi-feral cattle in southern122
beech forests as no current data or a common methodology exists for this purpose. Furthermore,123
the use of vegetation variables is usual in studies on habitat use by cattle, but the role of124
topography could be of importance especially in mountainous areas, yet is often overlooked.125
Specifically, this study address the following questions: (i) What is the habitat use of semi-feral126
cattle in Nothofagus forests?; and (ii) Are the vegetation variables of the landscape more127
important than topography for habitat use by cattle?. These research questions were addressed by128
using a utilization distribution approach and the construction of a resource selection probability129
function (RSPF) which included topographic and vegetation variables.130
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METHODS 133
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- Study Area 135
The study was carried out in the Llodconto Valley, Northwest Patagonia (Rio Negro, Argentina).136
The valley is within the Nahuel Huapí National Park, between 41º21' and 41º27' south latitude,137
and 71º31' and 71º41' west longitude, at an elevation ranging from 920 to 2000 m.a.s.l.. It is a138
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NICOLÁS SEOANEmountainous area with cold-temperate to moist-cold climate with prevailing westerly winds. The139
summers are dry and winters rainy, with a mean monthly temperature ranging from 2.4ºC in July140
to 12.9ºC in January. The area is part of the Andean-Patagonian forest and includes a wide141
variety of habitats such as forests, shrublands, meadows, burned forest, riparian and high142
mountain areas. The dominant species is Nothofagus antarctica which is one of the main143
components of both forests and shrublands. Other species such as the Nothofagus pumilio tree,144
the Schinus patagonicus shrub, and the Chusquea coleou bamboo are also abundant. An145
uncertain number of free-ranging cattle inhabit the valley and are traditionally and minimally146
managed by local people only during the summer months.147
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- Data collection and explanatory variables149
In order to capture the animals, an enclosure located at the center of the valley was used to herd150
the animals into. Four female adults caught in 2012 and one more caught in 2013 were fitted151
with custom-made GPS collars (GPS Module ZX4120 and Amicus GPS Shiled Antenna, ©152
Crownhill Associates Ltd.). These GPS collars store data on board and recapture was necessary153
to recover the data. Each collar was set to record location every ten minutes and these records154
were later screened by hour in order to standardize the records and eliminate the unacquired data.155
All capture and handling methods used in this study met the standards of animal welfare156
practices recommended for scientific research (Rollin and Kessel 1998, Gannon and Sikes 2007).157
All research was approved by the proper authorities of the protected area.158
Topographic mapping and spatial statistical analysis were conducted using Quantum159
GIS™ software (QGIS™) version 1.8.0 using a digital elevation model (ASTER GDEM) as the160
data source. Animal locations were overlayed on the digital elevation model and a minimum161
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NICOLÁS SEOANEconvex polygon (MCP) with a 200 meter buffer was used around the complete set of GPS162
positions to define our study area. One thousand sampling units of 100 meters radii each (i.e.,163
sampling unit area= 0.03km2) were allocated randomly inside this area.164
Habitat types were assigned for each sampling unit by means of a visual classification on165
Google Earth (© Google Inc.), identifying the following vegetation types: forests, shrubland,166
burned areas, meadows, open areas (vegetation cover 0.6) were not used together in the same model. Squared177
elevation and slope were included in the models for the purpose of evaluating preferred ranges of178
use by animals. In order to account for a strong regional climatic pattern, the interaction between179
slope and NWness was also considered since steep and northwest exposed slopes are generally180
arid, with short vegetation and low coverage.181
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- Data analysis 183
There are several ways to model habitat use. The most widely used include the GLMs and related184
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NICOLÁS SEOANEmodels (Guisan and Zimmermann 2000), but multivariate approaches (Clark et al. 1993, Burke185
et al. 2013), GIS-based habitat suitability models (Osborne et al. 2001, Store and Jokimäki186
2003), individual based models (Railsback and Harvey 2002), artificial neural networks187
approaches (Özesmi and Özesmi 1999), and maximum entropy modeling (Baldwin 2009) also188
exist. Here I applied a utilization distribution approach described by Nielson and Sawyer (2013)189
to develop the RSPF because it accounts for intensity of use among habitats and is unbiased in190
the face of temporally correlated animal location data.191
The utilization distribution approach allows the modeling of habitat use by using the192
relative frequency of positions within each sampling unit as an empirical estimator of the use193
distribution (Millspaugh et al. 2006). This relative frequency of positions was used as a response194
variable in a generalized linear model (GLM) with negative binomial distribution that accounts195
for over-dispersed count data. In order to relate the observed counts in a sampling unit with the196
total number of positions recorded for each animal, an offset term was included, thus allowing for197
estimating the probability of use by individual ( [i]/total , references below).198
199
Thus, the RSPF was set as follows: 200
θ , μnomial NegativeBi y ii ~ 201
...orest.Wness.uggedness.lope.levation.lnln 54321 iiiii0i f β N β r β s β e β + β +total = μ 202
203
where:204
yi = number of observations at the i-th sampling unit; = relative frequency in each sampling205
unit by animal; = overdispersion parameter; total = number of recorded positions in the whole206
study area by animal; i = sampling unit with associated habitat covariates.207
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NICOLÁS SEOANESeveral models exploring different combinations of predictor variables were fitted for208
each animal and compared using the Akaike information criterion (AIC). The overdispersion209
parameter of the negative binomial distribution was estimated simultaneously with the models210
using the glm.nb function in R (Venables and Ripley 2002, R Core Team 2013 version 3.0.2). A211
set of ecologically plausible models was chosen to work with all animals based on a stepwise212
regression. The package glmulti (Calcagno and Mazancourt 2010) was used as a tool for quickly213
identifying the best models for each animal. The deviance (Guisan et al. 2000) was used as a214
goodness of fit indicator.215
A population-level model was then developed considering each animal as an216
experimental unit, which reflects the individual nature of resource selection (Marzluff et al.217
2004, Millspaugh et al. 2006). A single set of covariates was selected considering the comparison218
among models and the biological relevance of the variables. The estimated coefficients, variance219
and confidence intervals were calculated for each parameter of this general proposed model.220
The coefficients of the population model were calculated according to: 221
̂ ̄βk =1
n ∑ β̂kj
222
where ̂ ̄βkj is the estimated coefficient k for each individual j ( j=1,2,3,4) and n is the total number223
of animals (n=4).224
The variance of the estimated coefficients for the population model was computed as:225
Var ( ̂ ̄βk )= 1n− 1∑ j= 1
n
( ̂βij− ̂̄βij)2
226
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NICOLÁS SEOANERESULTS 230
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A total of 2575 position records (an 87% fix rate for the GPS collars) was obtained from 4 of the232
animals. One of the collared animals could not be relocated during the study and therefore was233
not recaptured.234
Based on the fitted models for each animal, the general pattern observed that topographic235
variables were always more important than vegetation. Given the full additive model (not shown)236
as an example, the only meaningful variables for all animals were elevation, aspect and the237
interaction between slope and NWness. In all cases, topographic variables had negative238
coefficients because the animals preferred elevations below the available mean in the landscape239
and avoided hillsides with northwestern exposure and steep slopes. The vegetation variables240
showed different tendencies for each individual and in most cases were not significant (p > 0.05).241
The burned areas and shrubs had a tendency of being avoided by cows 1 and 2 but were242
preferred by cow 3, which also preferred wet meadows. Forest habitat had low coefficients for all243
the animals with values near zero, which showed no tendency to be selected, either in favor or244
against.245
By means of a step-by-step selection procedure over all the models, five plausible246
ecological models were selected for each animal in order to use them as a RSPF for semi-wild247
cattle (Table 1). These models were among the best ten for each animal. It can be seen that all248
models have as variables elevation, some aspect indicator, its interaction with slope, and in some249
cases the vegetation types with greater selection: shrubs and meadows. The coefficients of these250
five models for all the animals are summarized in Appendix 1.251
The most parsimonious models for each animal (Table 2) were significantly different252
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NICOLÁS SEOANEfrom the null model (p
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NICOLÁS SEOANEDISCUSSION 276
277
Using GPS logs and an approach that takes into account the intensity of use, a RSPF that278
accounts for topography and vegetation was built for semi-wild cattle in the Andean-Patagonian279
forest. This study shows that habitat use by livestock in these forests has a large inter-individual280
variation but also common features that would allow a description of a pattern of use.281
Topographic variables affected the probability of habitat use by cattle to a greater extent than282
vegetation types. In the general proposed model, for instance, a change in a single standard283
deviation unit of elevation (~ 300 meters) resulted in a threefold increased in the probability of284
use (Table 3) and the squared slope has a similar role in some of the alternative models (Table 1).285
This suggests that terrain features have a leading role in defining the use of space by livestock,286
perhaps as an adaptation due to the long history of use and because topography is important in287
these mountainous forests.288
Some common features such as the preferred elevation range and the tendency to prefer289
meadows suggests that it would be possible to describe a population-level home-range for a more290
comprehensive description of habitat use. In this regard, the squared elevation would play an291
important role in defining a preferred elevation range, although there seems to be a high292
variability among individuals. This variability could be modeled by a RSPF with hierarchical293
formulation. Cattle tend to slightly avoid shrubland (Table 3). This may be due to a lower294
proportion of trails in this type of thick vegetation, in contrast to other more open areas or those295
that offer better protection from snow, such as forest. Moreover, these paths are unusable in the296
winter. In this scenario, the availability and network of trails would play an important role in297
defining the population-level home-range of cattle, and therefore, in determining habitat use. At298
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NICOLÁS SEOANEthe same time, use by livestock has created the paths over time, so there is a reciprocal process299
that shows a type of population memory process.300
The response variable used in this study proved to be appropriate and useful as it301
describes the habitat use of cattle in probabilistic terms. However, the experimental design has302
limitations that must be made explicit. This includes the low sample size of animals. However,303
the different trajectories and spaces used by the sampled individuals suggest that the study was304
able to capture considerable variability in resource use by cattle (Figures 1 and 2) and even305
studies with one GPS-collared animal have accomplished some general features in the behavior306
of a herd (Moritz et al. 2010). Another limitation is the temporal span of the data collection. As307
the data collection period occurred between the months of April and July, it can be assumed that308
this covers a typically cold season (corresponding to the austral autumn and winter). Therefore,309
all models and conclusions of this work should be most reflective of that season.310
A disadvantage of using logistic regressions such as RSPFs is that the model coefficients311
decrease as the availability of a resource increases, and may even change the sign (Beyer et al.312
2010). This could explain some of the inter-individual variation observed in the case of meadow313
use (Figure 2), with animals 1 and 2 both negatively selecting this vegetation, and moving into314
an area where the abundance of meadows is higher in relation to the areas where individuals 3315
and 4 moved (unreported movement data). A similar case is that of the forest variable, which was316
not significant in any model (Table 1) and yet it is a very abundant vegetation type and widely317
used by cattle. To overcome this limitation it would be necessary to develop models that also318
account for animal movement, as habitat selection and movement are not independent processes.319
Methods for estimating movement patterns and habitat selection simultaneously have recently320
been developed (Moorcroft and Barnett 2008, Smouse et al. 2010).321
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NICOLÁS SEOANEComparing the results of this study with others conducted on cattle in forests we find322
interesting relationships in the applied variables and scales. Studies on seasonal diet selection323
(Marquardt et al. 2010) in Bolivian forests for example, show that different functional groups of324
plants are preferred in different seasons. It would be possible to infer species selected by325
livestock throughout the year in the Andean-patagonian forest if direct observations of consumed326
plants was added to this study. Roath and Krueger (1982) also used topographic and vegetation327
variables to study the behavior of cattle in a forested mountainous environment, finding that the328
type of vegetation and water availability determined the degree of use by livestock. These329
apparently opposite results are consistent if we consider the differences between the study areas.330
While Roath and Krueger (1982) worked in a dry, arid vegetation and elevational range of 300331
meters, Llodconto Valley is an area with high water availability, abundant streams, and altitudes332
ranging from 800 to 1900 masl. Kaufmann et al. (2013) found that in silvopastoral systems cattle333
prefer places with forest cover, avoiding places with high slope and low biomass, particularly334
logged sites. A comparison with this study would make an analogy between logged and burned335
areas, both without forest cover. Nevertheless, with the available data we cannot make inferences336
about the type of selection on burned areas so far (Tables 1- 3). It may be noted, however, that337
other studies in Patagonia (Blackhall et al. 2008, De Paz and Raffaele 2013) found a significant338
influence of livestock on vegetation in burned environments, making it clear that cattle forage in339
these areas. Interestingly, Johnson et al. (2004) conducted a study on resource selection by340
caribou at two spatial scales, finding that topographic variables such as elevation and slope are341
more important at a landscape scale, while vegetation variables were more important at the patch342
scale. Something similar could be occurring in this present study, which is closer to the343
landscape scale.344
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NICOLÁS SEOANEAs mentioned, cattle can increase landscape heterogeneity, especially when the spatial345
pattern of vegetation is already heterogeneous (Adler et al. 2001) as in the case of the currently346
studied forests. This has implications for forests dynamics because although there are studies on347
cattle inhabiting forest (Bartolomé et al. 2011, Kauffmann et al. 2013, etc.), there still remains a348
lack of knowledge regarding how forest dynamics are affected by the distribution of wild cattle349
worldwide. Recently, a study conducted in Patagonia developed a model that postulates distance350
to meadows as an estimator of forest use by livestock (Quinteros et al. 2012). While meadows351
are of crucial importance to the Andean-Patagonian forests and Quinteros et al. (2012) make a352
practical contribution in relation to forest management, their model has a spatial limitation, being353
restricted to meadows surroundings. Thus, it is not possible to make inferences at a landscape354
scale and the model fails to consider other relevant variables such as topography, which proved355
to have greater importance in resource selection by cattle (Tables 2-3, Figure 2). Nonetheless, the356
approaches are consistent, because as the distance to meadows increases, the intensity of use by357
livestock decreases (Quinteros et al. 2012), although altitude is also higher, which is predicted by358
the model in the current study.359
There remains a need for tools that enable animal management in agreement with360
conservation management practices of native forests. In Patagonia, for instance, extensive361
livestock farming has been identified as an activity with potential to be carried out in the forest.362
Yet, there have been only some pioneer experiences of silvopastoral handling (Fertig and Guitart363
2006), and current livestock handling is still scarce (Ormaechea et al. 2009). The main364
limitations for management worldwide are a lack of understanding of animal foraging decisions365
and the spatial scale of diet selection (Rook et al. 2004). Although effective management366
depends heavily upon knowledge about the interaction of the animal with its environment, it is367
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NICOLÁS SEOANEcommon to have poor information on the activity patterns of semi-wild cattle (Moyo et al. 2012).368
This current study provides a direct contribution to this knowledge-gap, and direction for future369
research in these systems. As an example, a practical application of this work is the production of370
maps showing intensity of land use by livestock. Similar maps have been developed for other371
regions using equivalent methodologies (Nielsen et al. 2003, Sawyer et al. 2009), and therefore372
offer a management and research tool within protected areas or productive forests.373
In summary, the present study demonstrated that the habitat use by semi-feral cattle in374
southern beech forests has a large inter-individual variability but also similar characteristics375
which allow the description of a pattern of use. Specifically, the results indicate that vegetation376
variables are no more important than topography features. Conversely, habitat selection by cattle377
is mainly affected by topographic variables such as altitude and the combination of slope and378
aspect. These results suggest an adaptation by free-ranging cattle to the major features of the379
landscape of these mountainous forests maybe due to ferality.380
381
382
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ACKNOWLEDGEMENTS 384
385
Funding for this research was provided by a PhD grant from the CONICET (Argentina). The386
author thanks to Juan Manuel Morales for helpful comments on the manuscript.387
388
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NICOLÁS SEOANETable 1. Alternative models for free-range cattle habitat use in the study area; the table shows539
Akaike information criterion value (AIC), number of predictor variables (K ) and ranking of540
models relative to each individual (R).541
542
Models Animal 1 Animal 2 Animal 3 Animal 4
K AIC ΔAIC R AIC ΔAIC R AIC ΔAIC R AIC ΔAIC R
A
Elevation + elevation2 +
slope + slope2 + NWness 5 187.5 0.0 1 619.9 2.4 3 516.0 5.8 2 1813.5 18.1 4
B
Model A + shrubland +
meadow 7 188.9 1.4 2 622.7 5.2 5 510.2 0.0 1 1809.2 13.8 3
C
Elevation +
NWness*slope 4 189.5 2.0 3 617.5 0.0 1 528.9 18.7 5 1795.4 0.0 1
D
Model C + shrubland +
meadow 6 192.4 4.9 4 619.8 2.3 2 520.5 10.3 3 1795.5 0.1 2
E
Elevation + slope +
Nwness + forest +
shrubland + meadow 6 196.3 8.8 5 620.8 3.3 4 521.7 11.5 4 1832.1 36.7 5
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Table 2. Coefficients and 95% confidence intervals of the most parsimonious RSF models for549
selection of topographic and vegetation resources during autumn by free-range cattle in the study550
area.551
552
Variables Animal 1 Animal 2 Animal 3 Animal 4
Coef. 95% CIs Coef. 95% CIs Coef. 95% CIs Coef. 95% CIs
Intercept -39.91 -67.66 -12.16 -2.73 -4.05 -1.61 -2.22 -3.44 -1.07 -1.59 -2.38 -0.83
Elevation -56.92 -110.64 -3.20 -1.54 -2.90 -0.46 -3.54 -4.63 -2.56
Elevation² -1.68 -2.82 -0.73
Slope -2.65 -4.21 -1.11 -1.43 -2.79 -0.33 1.45 0.68 2.38 0.73 0.20 1.30
Slope² -0.26 -1.20 0.48 1.08 0.47 1.76
NWness -2.64 -5.27 -0.00 0.77 0.12 1.46 1.15 -0.01 2.54 2.01 1.29 2.73
Shrubland -0.58 -1.63 0.53
Meadow -1.21 -3.46 1.04 4.01 1.61 7.54
NW*Slope -1.42 -2.90 -0.26 1.99 1.38 2.63
553
554
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556
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NICOLÁS SEOANE560
Table 3. Population model of resource selection by cattle, with estimate coefficients561
(standardized variables), SEs and 90% percentile confidence intervals.562
563
90% Confidence intervals
Coefficient SE Lower limit Upper limit
Intercept -9.368 1.136 -12.409 -6.327
Elevation -3.016 0.864 -4.776 -1.255
Slope:Nwness -4.772 1.319 -8.867 -0.677
Slope -5.958 1.799 -13.576 1.660
NWness -8.165 1.598 -14.180 -2.151
Shrubland -0.060 0.795 -1.432 1.545
Meadow 1.521 1.361 -2.839 5.882
564
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NICOLÁS SEOANE572
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Figure 1. Use vs. Availability of topographic resources elevation and slope.574
575
Figure 2. General proposed model estimates (black) and individual model estimates (grey) with576
90 % confidence intervals.577
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Figure 1597
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619
Figure 2620
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Appendix: The models and their coefficients for all animals.642
############################################################ 643
1) Description of the models644
2) Coefficients of all tested models and animals645
############################################################646
1- Description of the models647
θ , μnomial NegativeBi y ii ~ 648
i j0i variable β + β +total = μ .lnln 649
where:650
y = number of observations per sampling unit; = relative frequency in each sampling unit by animal; =651
overdispersion parameter; total = number of recorded positions in whole study area by animal; i = sampling unit652
with associated habitat covariates; j = number of variables in each model.653
654
Model Description of variables involved in each model
A elevation + sqr.elev + slope + sqr.slope + NWness
B elevation + sqr.elev + slope + sqr.slope + NWness + shrubland + meadow
C elevation + slope + NWness * slope
D elevation + NWness * slope + shrubland + meadow
E elevation + slope + NWness + forest + shrubland + meadow
655
656
657
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661
2- Coefficients of all tested models and animals* 662
* Coefficient with this symbol are significant at a level of 95% 663
# Animal 1664
Model A Model B Model C Model D Model E
Intercept -72.577* -75.991* -11.633* -11.422* -10.516*
Elevation -130.564* -137.462* -5.748* -5.718* -5.394*
Elevation2 -62.145* -65.576*
Slope -2.343 -2.657 -3.063* -3.051* -2.536*
Slope2 -0.101 -0.236
NWness -0.060 -0.043 -2.852* -2.621* -0.010
Shrubland -0.259 -0.284 -0.252
Meadow -1.681 -1.127 -1.218
Forest 0.174
NWness*Slope -2.279* -2.117*
Theta 0.212 0.238 0.164 0.168 0.160
Theta SE 0.098 0.113 0.067 0.069 0.067
665
# Animal 2666
Model A Model B Model C Model D Model E
Intercept -3.174* -2.686* -3.614* -3.272* -3.126*
Elevation -1.577* -1.585* -1.967* -1.937* -1.572*
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Elevation2 0.127 -0.078
Slope -1.515* -1.442* -1.334* -1.227* -1.167*
Slope2
-0.231 -0.252
NWness 0.650* 0.783* 1.039* 1.312* 0.784
Shrubland -0.627 -0.685 -0.275
Meadow -0.203 -0.232 0.105
Forest 0.387
NWness*Slope 0.403 0.516
Theta 0.062 0.063 0.063 0.065 0.064
Theta SE 0.011 0.011 0.011 0.012 0.012
667
# Animal 3668
Model A Model B Model C Model D Model E
Intercept -2.834* -2.683* -1.943* -2.264* -2.939*
Elevation -1.512* -1.636* -1.713* -1.698* -1.711*
Elevation2 -1.288* -1.597*
Slope 1.652* 1.969* 2.344* 3.010* 2.927*
Slope2 1.196* 1.043*
NWness 0.874* 1.444* 0.233 1.034* 0.994*
Shrubland -0.672 -0.712 -0.202
Meadow 2.929* 3.937* 4.166*
Forest 1.340
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NWness*Slope -0.161 -0.873*
Theta 0.040 0.044 0.031 0.037 0.036
Theta SE 0.008 0.008 0.006 0.007 0.007
669
# Animal 4670
Model A Model B Model C Model D Model E
Intercept 0.073 -1.003* -1.594* -1.817* 0.230
Elevation -3.173* -3.131* -3.545* -3.420* -2.639*
Elevation2 -1.273* -0.782*
Slope 1.471* 1.591* 0.739* 0.783* 1.364*
Slope2 0.714* 0.830*
NWness 0.214 0.132 2.012* 1.740* -0.177
Shrubland 1.120* 0.634 -0.053
Meadow 0.212 0.794 0.764
Forest -0.647
NWness*Slope 1.996* 1.872*
Theta 0.072 0.075 0.078 0.079 0.068
Theta SE 0.007 0.007 0.007 0.008 0.006
671
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673
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