1

A quantitative assessment of

geodiversity and human influence

in geoparks

MSc thesis Emma Polman - 10799478

9/11/2020

Supervisor dr. Harry Seijmonsbergen Co-assessor dr. rer. nat. Daniel Kissling

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Abstract

UNESCO global geoparks aim at protecting globally significant geoheritage and geodiversity using a

management strategy that relies upon sustainable development for the local population. However,

both geodiversity and human influence in geoparks have never been quantified and assessed in a

global context and the extent to which geodiversity and human influences are represented in

geoparks is therefore unknown. Here, geodiversity, human influence and human influence change

between 1993 and 2009 are quantified in 147 geoparks and compared to global Asian and European

geodiversity, human influence and human influence change. In addition, the representation of soil

and lithology types in geoparks was assessed. The results show that the total geodiversity and

lithological and topographic diversity were in general significantly higher in geoparks, while soil

diversity was not significantly higher and hydrological diversity was lower. This is most likely caused

by the emphasis on geology and geomorphology in the UNESCO application procedure and the large

share of geoparks located in mountainous areas, where the lithological and topographic diversity and

subsequently the total geodiversity are high. Igneous and volcanic rocks and andosols were

represented best in geoparks, especially in Europe. Of all soil and lithology types 22 and 65% were

not represented in any geopark, respectively, likely due to the uneven global distribution of

geoparks. The human influence indices were higher in geoparks indicating that there is a substantial

amount of human activities present in geopark areas. Human influence change was not significantly

different in geoparks, compared to global, Asian and European human influence change. It is

recommended that future geoparks put more emphasis on soil and hydrological diversity and on the

soil and lithology types that are not or hardly represented in geoparks to better reflect global

geodiversity. Human activities in geoparks should be monitored in order to assess if damage is

caused to geodiversity.

List of abbreviations

GI geodiversity index

GGI global geodiversity index

HF human footprint

gHM global human modification

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Contents

1. Introduction ....................................................................................................................................4

2. Methods .........................................................................................................................................6

2.1 General Framework ...................................................................................................................6

2.2 Geoprocessing...........................................................................................................................8

2.2.1 Sampling in geoparks and random samples ........................................................................8

2.2.2 Sampling soil and lithology types ........................................................................................9

2. 3 Statistical Analysis .................................................................................................................. 10

2.3.1 Comparing geodiversity and human influence between geoparks and random samples ... 10

2.3.2 Geodiversity index correlation analysis ............................................................................. 10

2.3.3 Analysis of distinct soil and lithology types ....................................................................... 11

2.4 Software ................................................................................................................................. 11

3. Results .......................................................................................................................................... 12

3.1 Geodiversity index in geoparks and random samples............................................................... 12

3.1.1 Geodiversity index ............................................................................................................ 12

3.1.2 Geodiversity index correlations ........................................................................................ 13

3.2 Soil and lithology types ............................................................................................................ 14

3.3 Human influence in geoparks and random samples ................................................................. 16

3.4 Human footprint change in geoparks and random samples ..................................................... 16

4. Discussion ..................................................................................................................................... 18

4.1 Comparing the geodiversity index ........................................................................................... 18

4.2 Comparing soil and lithology types .......................................................................................... 19

4.3. Comparing human influence................................................................................................... 20

4.4 Comparing human influence change ....................................................................................... 20

4.5 Technical limitations and future directions .............................................................................. 21

5. Conclusion .................................................................................................................................... 22

References ........................................................................................................................................ 23

Appendix I ........................................................................................................................................ 27

Appendix II........................................................................................................................................ 32

Appendix III....................................................................................................................................... 36

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1. Introduction

The conservation of geological heritage has become of increased importance over the past years.

Awareness has risen that geological features hold valuable information about past earth processes

and that their loss is irreversible given the large time scales associated with their formation (Gray,

2008; Brilha, 2016). Related to this increasing valuation of the earth's abiotic features is the concept

of geodiversity. The term geodiversity was introduced in the 1990s (Gray, 2008) and the now most

widely accepted definition is the "natural range (diversity) of geological (rocks, minerals, fossils),

geomorphological (landforms, topography, physical processes), soil and hydrological features. It

includes their assemblages, structures, systems and contributions to landscapes" (Gray, 2013).

Geodiversity is now increasingly used within the scientific community (Ibáñez et al., 2018; Zwoliński

et al., 2018).

An attempt to conserve globally significant geoheritage and geodiversity is through the

establishment of UNESCO Global Geoparks. From 2001-2019, UNESCO has awarded the status of

"Global Geopark" to 147 areas. Geoparks are defined as "single unified geographical regions where

sites and landscapes of international geological significance are managed with a holistic concept of

protection, education and sustainable development" (UNESCO, 2015). Geodiversity is one of the

assessment criteria during the geopark application procedure (UNESCO, 2016), but most geodiversity

related research in geoparks concerns qualitative analyses about the relation between geodiversity

and touristic values (Boothroyd & Henry, 2019). The term "geodiversity" is only used in the official

description of 41 geoparks, and many geoparks consider geodiversity as a synonym for geoheritage

or the presence of unique geological features (Ruban & Yashalova, 2018), while these are only a part

of geodiversity (Crofts & Gordon, 2015; Brilha, 2016). Up to this date, no systematic research has

been conducted on quantifying geodiversity in geoparks and assessing its relation to global

geodiversity.

The majority of geoparks are located in Europe and China (Appendix I fig 1), making it

impossible to capture the entirety of different geofeatures present on earth (Ibáñez et al., 2018).

Geodiversity in the geopark application procedure is only assessed as the number of different rock

types, geomorphological features and geological time periods represented in the park (UNESCO,

2016), and the main focus is thus on lithological and geomorphological diversity. Other abiotic

features such as soils and hydrology are not taken into account, and neither are the spatial

distribution and assemblage of different features, while these are often considered in geodiversity

indices (e.g. Cañadas & Ruiz-Flaño, 2007; Hjort & Luoto, 2010; Seijmonsbergen et al., 2018).

Geoparks also focus on protecting geosites, sites with a high scientific, cultural educational or

aesthetic value. Though Gray (2008) states that geosites aim at being representative for their abiotic

surroundings, Hjort and Luoto (2010) found that geosites alone are often not capturing the total

geodiversity of an area. It is therefore unlikely that geoparks capture and conserve global

geodiversity.

In order to assess geodiversity, various qualitative and especially quantitative methods have

been proposed in the last years (Zwoliński et al., 2018). Many studies quantify geodiversity by

calculating a geodiversity index (GI) which is the sum of multiple sub indices, such as geological,

hydrological, soil, topographic, geomorphological and paleontological diversity (Cañadas & Ruiz-

Flaño, 2007; Pereira et al., 2013; Araujo & Pereira, 2018; Seijmonsbergen et al.,. 2018; da Silva et al.,

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2019; Gonçalves et al., 2020; dos Santos et al., 2020). Sub indices are often calculated using a grid-

based approach. Thematic maps of the research area are overlain with a grid and the diversity is

determined for each grid cell by counting the number of unique geo features within the cell (e.g.

Hjort & Luoto, 2010; Santos et al., 2017; Fernández et al., 2020). However, the different methods and

sub indices used to calculate the GIs and the local extent of the studies make it impossible to

compare GI values across areas (Ibáñez & Brevik, 2019). A global geodiversity index (GGI) based on

topographic, hydrologic, lithology and soil datasets, as used by (Muellner‐Riehl et al., 2019) does

allow for a transparent analysis of geodiversity. However, a GI only takes into account the variety of

geo features within a grid cell, but ignores the spatial distribution of specific types of geo features,

e.g. geology and soil types. Additional assessment of the types of geo features in an area is necessary

to quantify their global representation. There is at the moment no standardised method for assessing

geodiversity.

Geoparks are not legally protected areas, and parks encompass roads, railways, airports,

urban and industrial areas, and agricultural lands. Within the parks geodiversity is also used to

support sustainable development while protecting the landscape (UNESCO, 2015), but certain human

activities such as urbanisation, mining and land use changes can be a potential threat to geodiversity

as these can destruct landforms, disrupt geomorphological processes and cause soil erosion (Crofts &

Gordon, 2015; Hjort et al., 2015). Geotourism is a main component of the sustainable development

in parks (Farsani et al., 2011), but it can cause an increase in infrastructure (Shui & Xu, 2016), which

can eventually lead to visitors damaging sites (Wang et al., 2015), erosion (Sumanapala & Wolf, 2020)

and fragmentation of sites (Crofts & Gordon, 2015; Hjort et al., 2015). Many geoparks are in Europe

and East Asia, where human influence is high (Venter et al., 2016b), but the management bodies in

geoparks should prevent that human activities cause harm to geodiversity (UNESCO, 2015). There is

however no global study quantifying the status of human influence and how human influence

changes over time in geoparks in relation to global human influence and global human influence

change.

This research aims at determining to what extent global geodiversity and human influence

are represented in UNESCO global geoparks by conducting an objective quantification and

assessment. This assessment will be done on the global, Asian and European extent, as the majority

of geoparks are located in Asia and Europe. This research aim leads to the following four research

questions: 1) how does geodiversity in geoparks compare to global, Asian and European

geodiversity? 2) how do the soil and lithology types present in geoparks compare to the soil and

lithology types globally, in Asia and in Europe? 3) how does human influence in geoparks compare to

global, Asian and European human influence? and 4) how does human influence change in geoparks

compare to global, Asian and European human influence change? It is hypothesised that 1) the

lithological and topographic diversity and subsequently the total geodiversity will be higher in

geoparks, while soil and hydrological diversity will be lower, because these are not explicitly assessed

by UNESCO and associated less with geodiversity in the geoparks context (UNESCO, 2016; Ruban,

2017) 2) not all soil and lithology types are represented in geoparks because of the uneven spatial

distribution of the parks (Ibáñez et al., 2018) 3) human influence is equally high or higher in geoparks

compared to the world, Asia and Europe, since parks are established in areas with high human

influence (Venter et al., 2016b; Kennedy et al., 2019) 4) human influence in geoparks is stable or

decreasing in geoparks, because of the management requirements set by UNESCO.

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2. Methods

2.1 General Framework In order to compare geodiversity, human influence and human influence change between

geoparks, the world, Asia and Europe data layers were collected, sampled and (statistically)

compared (Fig 1). Additionally, a correlation analysis was part of the GI analysis to gain insight in the

contribution of each GI sub layer to the total GI (GDsum) (Fig 1, H1).

Figure 1. Workflow diagram showing the three components (A-C) used to answer the four research questions

and hypotheses (H1-H4). All input data layers were collected (row A) and GGI (H1), human influence (H3) and

human influence change (H4) data layers were sampled in geoparks and random samples in the geoprocessing

phase (row B). The soil and lithology data layers were sampled in geoparks, the world, Asia and Europe (H2).

Finally, the correlations between the GDsum and GGI sub layers were calculated (H1) and the differences

between geoparks and random samples were statistically tested for H1, H3 and H4 (row C). For H2 the

percentage area of each soil and lithology type in geoparks relative to the global, Asian and European area was

calculated.

Geopark data

A database containing all geoparks and data attributes was built using the lists of geoparks available

at the Global Geoparks Network website (2019) and the UNESCO website (2019). The polygons of the

geoparks were requested to all individual geoparks by mail, since these data are not publicly

available. In addition, several geopark polygons were found online. This resulted in a total of 45

geopark polygons. The remaining 102 geopark polygons areas were estimated using the point

location coordinates and surface area data made available by UNESCO (2019). The estimated

geopark polygons were created by defining a circular buffer around each point coordinate with a

radius corresponding to the geopark area. Some of these estimated polygons overlapped with the

nearby sea surface, and to avoid bias since the analysis and data only concern the terrestrial surface,

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maps of these geoparks were searched online and digitized by hand. A list of geoparks and the type

of polygon used in the analysis is provided in Appendix II and the geoparks shapefile in Appendix III.

Geodiversity index data

For the GI analysis the GGI data described in the research by Muellner‐Riehl et al. (2019) were used

(table 1). The data were obtained in person from Hannes Versteegh, who contributed as a co-author

in that research. This dataset consists of six diversity raster layers and one raster with the summed

geodiversity (GDsum). The resolution of the GGI and sub layers is 10x10 km. The six sub layers used

are the number of soil types in a grid cell (SoilDiv) (Hengl et al., 2017), number of lithology types in a

grid cell (LithoDiv) (Hartmann & Moosdorf, 2012), the slope range (SlopeRange) and slope standard

deviation (SlopeSTD) (Yamazaki et al., 2017) in a grid cell, the total river length in a grid cell

(RiverLength) (Lehner et al., 2008) and the total lake area (Lake Area) (Messager et al., 2016) in a grid

cell. These diversity layers were reclassified into four sub-indices (soil, lithology, hydrology and

topography) using Jenks natural breaks. The GDsum layer is the sum of these four sub indices.

Soil and lithology type data

For the analysis of lithology the Global Lithological Map (GLiM) was used (Hartmann & Moosdorf,

2012) (table 1). This polygon dataset distinguishes 15 unique main lithological types at the first

legend level and 437 unique rock types at the combined first, second and third legend level. The

SoilGrids (Hengl et al., 2017) (table 1) data were used for assessing the unique soil types. This raster

dataset contains 118 unique soil types based on the World Reference Base (WRB) classification (IUSS

Working Group, 2006). This classification includes 30 of the 32 main WRB soil types and the most

common prefix and suffix qualifiers.

Human influence data

The human influence analysis included two open source datasets: the human footprint (HF), available

for 1993 (HF1993) (Sanderson et al., 2002) and 2009 (HF2009) (Venter et al., 2016b) and the Global

Human modification of 2016 (gHM) (Kennedy et al., 2019) (table 1). The HF maps are based on

population density, electrical power infrastructure, human accessibility and land transformation,

which are considered to be explicit proxies of human population and infrastructure (Venter et al.,

2016a). The gHM uses similar drivers as the HF, but added higher coverage of transportation

infrastructure and mining and energy activities as stressors.

Human Footprint Change

The human footprint change (HFchange) was calculated by subtracting the HF2009 raster from the

HF1993 raster. The HF increased mainly in tropical areas and areas with high biodiversity. Small

decreases were observed in countries with relatively high wealth and strong legislative enforcement

(Venter et al., 2016b).

Continents and terrestrial outline

The continent outlines used in the research were made by merging the Esri world countries dataset

(Esri, 2015) on the continent attribute. This maintained the country-level detail to the borders of the

continent. Since no human influence and geodiversity data were available for Antarctica it was

excluded from the analysis.

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Table 1. Descriptions and metadata of the geodiversity and human influence datasets used in the research.

Data set Data

type

Cell

Size

(m)

Projection GCS Extent Description of

data attributes

Value

Range

Source

Geodiversity

Soil diversity raster 10,000 Mollweide WGS84 Global* Number of soil types in a grid cell

1-27 Muellner‐ Riehl et al. (2019)

Lithology diversity

raster 10,000 Mollweide WGS84 Global* Number of lithology types in a grid cell

1- 11 Muellner‐ Riehl et al. (2019)

Slope standard deviation

raster 10,000 Mollweide WGS84 Global* Standard deviation of slope in a grid cell

0-22 Muellner‐ Riehl et al. (2019)

Slope range raster 10,000 Mollweide WGS84 Global* Slope range in a grid cell

0-78.9 Muellner‐ Riehl et al. (2019)

River Length raster 10,000 Mollweide WGS84 180 W - 180 O 90 S -

56.6 N*

Total length (km) of all rivers in a grid cell

0-103 Muellner‐ Riehl et al. (2019)

Lake Area raster 10,000 Mollweide WGS84 Global* Total lake area (km2 ) in a grid cell

0-100 Muellner‐ Riehl et al.

(2019) Geodiversity Sum

Raster 10,000 Mollweide WGS84 180 W - 180 O 90 S - 56.6 N

Sum of the sub indices

2-20 Muellner‐ Riehl et al.

(2019) In person

Soil grids raster 250 Not projected

WGS84 Global* Soil type code 118 unique types

Hengl et al. (2017) link

GLiM vector - World

Eckert IV

WGS84 Global Lithology types

(first level and third level classification)

437

unique types

Hartmann &

Moosdorf (2012) link

Human Influence Human Footprint 1993 & 2009

raster 1000 Mollweide WGS84 Global* Human footprint score

0-50 Venter et al. (2016a), 2018 release link

Human Modification 2016

raster 1000 Mollweide WGS84 Global* Human modification score

0-1 Kennedy et al. (2019)

*excluding Antarctica

2.2 Geoprocessing

2.2.1 Sampling in geoparks and random samples

To compare geoparks with the world, Asia and Europe, random samples were taken from the GGI

(sub) layers, human influence layers and HFchange layer. For one random sample set, the same

amount of random sample areas as geoparks used in the analysis were created, and this procedure

was iterated 100 a times (table 2). In the GGI dataset, no RiverLength data was available above 56.6⁰

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Northern latitude and subsequently no GDsum data. The analysis of the RiverLength and GDsum

layers was therefore constrained to 56.6 deg N, and did not include the geoparks above this latitude.

The other GGI sub layers and human influence data have global coverage and included all geoparks

(table 2). For the GGI correlation analysis all random samples were constrained to 56.6 degrees

northern latitude, since all layers were compared to the GDsum layer. Similar procedures were

followed for the Asian and European analysis.

Table 2. The number of geoparks and random samples involved in each of the analyses.

Analysis Extent Ngeoparks Nrandom

Geodiversity:

- SlopeSTD, SlopeRange, LakeArea SoilDiv & LithoDiv

comparison.

Human Influence:

- HF change calculation

- HF1993, HF2009, HF change & gHM comparison

Global 147 14,700

Asia 61 6100

Europe 74 7400

Geodiversity:

- GDsum & RiverLength comparison.

- Correlation analysis

Global

56.6⁰N

139* 13,900

Asia

56.6⁰N

61 6100

Europe

56.6⁰N

66* 6600

* Geoparks excluded from analysis: Shetland (UK), North West Highlands (UK), Magma (Norway), Gea

Norvegica (Norway), Trollfjell (Norway), Rokua (Finland), Katla (Iceland), Reykjanes (Iceland).

The locations of the random samples were created using the create random points tool, and the area

where the random points could be placed was constrained to the terrestrial surface. The frequency

distribution of the random sample areas resembled the frequency distribution of the geopark areas

in order to increase the comparability of the data. From a table with radiuses corresponding to the

surface area of the included geoparks, a radius was randomly assigned to a random point by

performing a join between the two attribute tables. Each point was buffered using its randomly

assigned radius, creating a polygon feature class of random sample areas. In the case of polygons

overlapping with the sea surface, the overlapping part was removed using the intersect tool, since

the data and analysis are constrained to the land surface. The deletion of areas overlapping with the

sea surface causes some of the random sample areas to be slightly smaller than the geoparks. For

each geodiversity and human influence layer the mean value in the random samples and geoparks

was calculated using the Zonal statistics to table tool and stored in geodatabase tables (Appendix III).

2.2.2 Sampling soil and lithology types

For each continent a soil raster was extracted from the SoilGrids (Hengl et al., 2017) raster, using the

Extract by Mask tool. These raster layers were projected to the Mollweide projection and converted

to polygons. From these polygons the area of each soil type was calculated using Add Geometry

Attributes. For the geoparks, the soil grid data was clipped on the geopark polygons and a similar

procedure was followed. A spatial join was performed to assign each soil type polygon in geoparks to

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the right continent. For lithology types a similar procedure was followed using GliM dataset

(Hartmann & Moosdorf, 2012). Since this was vector data, the Clip tool was used instead of Extract

by Mask.

2. 3 Statistical Analysis

2.3.1 Comparing geodiversity and human influence between geoparks and random

samples

The GGI, human influence and HFchange scores were compared between geoparks and random

samples in order to test if these are significantly higher or different in geoparks (fig. 1, H1, H3 & H4).

The normality of the data was examined visually by making histograms and by performing a Lilliefors

test. Since the majority of the data was not normally distributed, the non-parametric Mann-Whitney

U-test was used to compare the random samples and geoparks. The geopark means were compared

to each of the random sample sets, so 100 comparisons were made per layer. For the GGI layers a

one sided Mann-Whitney U test was used with:

H0 = the mean geodiversity is the same in geoparks and random samples

Ha = the mean geodiversity is larger in geoparks than in random samples.

For the human influence and HFchange layers a two-sided Mann-Whitney U-test was used, with:

H0 = human influence is the same in geoparks and random samples

Ha = human influence is different in geoparks than in random samples.

Since for each layer the geopark values were compared to 100 random sample sets, the p values

needed to be corrected for the family-wise error rate (FWER), caused by multiple testing. The FWER

is the probability of making at least one type I error and is 99.4% when performing 100 tests

(Ranganathan et al., 2016). The Holm-Sidak correction was applied to the p values for both α = 0.05

and alpha = 0.01. This method performs a step-down correction on the p values and has a higher

power than other FWER correction methods, such the Bonferroni or Sidak single step methods

(Stevens et al., 2017).

2.3.2 Geodiversity index correlation analysis

In order to quantify the contribution of each GGI sub layer to the GDsum (fig 1, H1),

spearman's R (rs) was calculated between the GDsum layer and each of the GGI sub layers for both

the random sample sets and geoparks. rs was chosen because it does not assume normality of the

data. Random sample sets that contained null values were removed from the analysis, because the

null values changed the order of sample areas, making the results no longer paired observations. For

each layer, this resulted in one rs for the geoparks and between 73 and 100 rs for the random

samples. A two-sided test with p<0.025 was used to assess if the geopark rs could be drawn from the

distribution of random sample rs with 95% confidence.

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2.3.3 Analysis of distinct soil and lithology types

To assess how well soil and lithology types are represented within geoparks, the total area of

each soil and lithology type was calculated on the global, Asian and European extent by summing the

surface area of all polygon of a specific type. This calculation was also used for the geopark data.

Eventually, this allowed calculating the percentage area of each soil and lithology type that is located

in geoparks using:

With

- = the percentage area of a soil/lithology type located in geoparks

- = the total surface area of a soil/lithology type located in geoparks in km2

- = the total surface area of a soil/lithology type in km2

2.4 Software

The pre-processing and a part of the geoprocessing were done both manually and with the model

builder in ArcGIS Pro 2.4.2 (Esri, 2019). The random sampling model for geodiversity and human

influence layers was written as Python 3.6.8 (Van Rossum & Drake., 2009) scripts, using the ArcPy

module (Esri, 2019). Data analysis and visualisation were done in Python as well, using the pandas

(McKinney, 2010), SciPy (Virtanen et al., 2020) and Statsmodels (Seabold & Perktold., 2010) packages

for the (statistical) analysis and the Matplotlib (Hunter, 2007) and seaborn (Waskom et al., 2020)

packages for visualisation. Python scripts for both the geoprocessing and the statistical analysis are

available as digital Appendix III via https://github.com/emmamarianina/Geopark-GI-HI.

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3. Results

3.1 Geodiversity index in geoparks and random samples

3.1.1 Geodiversity index

The GDsum is significantly higher in geoparks for all comparisons globally and in Europe, but not for comparisons in Asia (fig. 2A; table 3). The LithoDiv, SlopeRange and SlopeSTD are all significantly higher in geoparks than in the random samples, on all extents and on the 99% confidence level (table 3). The median SlopeSTD lies between 1.3-2.6⁰ in random samples, but between 5.1 -6.9⁰ in geoparks, on all extents (fig 2C). This substantial difference is also present in SlopeRange medians (9.7 - 18.4⁰ in random samples, 30.0 - 39⁰ in geoparks; Appendix I fig. 2A). Median LithoDiv ranges from 1.7 - 1.9 in random samples and from 2.5-2.7 types/100 km2 in geoparks (fig. 2D). In contrast, the SoilDiv (fig. 2B) in geoparks is not significantly higher than in most random sample sets. Medians range from 3.7 - 4.1 in random samples and from 4.1 - 4.6 types/100 km2 in geoparks, on all extents. The hydrological diversity is also not significantly higher in geoparks. The median RiverLength is about 3km lower in geoparks than in random samples (fig. 2E) and the difference in LakeArea not consistent between extents and varies from 0.031 (Asia) to 0.29 km2 (Europe) in random samples and from 0.014 (Asia) to 0.17 km2 (Europe) in geoparks (Appendix I fig 2B). The random samples have in general a larger range and more extreme maximum values than the geoparks (fig 2). This indicates that geoparks capture only a part of the total range of diversity values that are present on earth. For example, the highest mean GDsum in geoparks lays around 14.5, while the highest random sample values are about 17. Table 3. Percentage of tests where geodiversity is significantly higher in geoparks than in random sample sets at

α= 0.05 and α= 0.01. p values are corrected with the Holm-Sidak correction.

Global Asia Europe

α = 0.05 α = 0.01 α = 0.05 α = 0.01 α = 0.05 α = 0.01

GDsum 100 100 77 34 100 100

SoilDiv 2 0 0 0 3 1

LithoDiv 100 100 100 100 100 100

SlopeRange 100 100 100 100 100 100

SlopeSTD 100 100 100 100 100 100

RiverLength 0 0 0 0 0 0

LakeArea 3 3 1 0 0 0

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Figure 2. Distribution of GD sum means (A) and geodiversity sub layer means (B - E) in the geoparks and

random samples on the global (blue), Asian (red) and European (green) extent. The random sample box plots

display the combined data from all 100 random sample sets.

3.1.2 Geodiversity index correlations

In geoparks, the GDsum is most strongly correlated with LithoDiv (rs = 0.67, 0.77, 0.63 globally, Asia

and Europe, respectively). These geopark rs fall within the 95% CI of the random sample rs

distributions, and are thus not significantly different from the random sample rs (fig. 3A). SlopeSTD

and SlopeRange are also strongly correlated to the GDsum in the random samples, but moderately

correlated in geoparks. While the random sample rs distribution ranges from 0.61-88, the geopark rs

lies between 0.38-0.47. In addition, the values of SlopeSTD and SlopeRange are very similar within

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each extent, the difference between SlopeSTD and SlopeRange correlations is smaller than 0.03 (fig.

3D; Appendix I fig. 3A). All rs for LithoDiv, SlopeSTD and SlopeRange are significant at the 95%

confidence level (Appendix I table 1).

Both RiverLength and LakeArea are weakly correlated to the GDsum and the majority of the

correlations are not significant (fig. 3C; Appendix I fig. 3B, table 1). The rs of both layers is weakly

positive for geoparks, but the random sample rs are distributed around zero. All geopark correlations

fall within the 95% CI, except for the LakeArea rs in Europe. The GDsum-SoilDiv correlation in

geoparks is higher than the RiverLength and LakeArea correlation, but still weakly positive (rs =0.32,

0.21 and 0.31 global, Asia and Europe respectively) (fig 3B). The geopark rs is lower than the 95% CI

globally and in Europe, while the Asian geopark rs falls on the lower boundary of the 95% CI (fig 3).

Most correlations are significant, which the exception of the Asian geopark correlation (Appendix I

table 1).

Figure 3. Blue histograms showing the distributions of Spearman's r (rs) between the GDsum and geodiversity

sub layers (ABCD columns) for the random sample sets for the global (first row), Asian (second row) and

European (third row) extent. The shaded area in medium grey shows the 95% confidence interval calculated

from the correlation distributions. The geopark rs are displayed as red dotted lines. Geopark and random sample

correlations are considered significantly different from each other when the red dotted line lies outside of the

grey shaded 95% confidence interval. The number of random sample sets used is shown in the upper right corner

of the plots.

3.2 Soil and lithology types

All of the 30 main WRB soil types are located within the 147 geoparks. On the global extent, the

percentage area in geoparks is <1.2% for all soil types. Andosols have the highest percentage area in

geoparks, followed by alisols. Only a very small percentage area (<0.01%) of the histosols, plinthisols,

solonetz and albeluvisols is located in geoparks (fig. 4A). On the Asian and European extent, the

15

percentage area in geoparks are slightly higher in Asia and much higher in Europe (fig 4B). Especially

andosols are well represented in Europe with over 35% of their area present in geoparks. However,

the percentage area in geoparks is still <5% for most soil types on the Asian and European extent. In

particular, durisols, solonetz, umbrisols and stagnosols are absent in the Asian geoparks. Ferrasols,

gypsisols, planosols, nitisols and solonetz do not occur in European geoparks. Only 7 out of the 118

soil types are represented for >1% in geoparks globally. 22% of these soil types are not present in any

geopark on the global extent. In Asia, 44% of 111 unique soil types are not present in geoparks and in

Europe this is 30% on a total of 90 unique types.

Figure 4. The percentage of the area of each WRB group soil type from the SoilGrids dataset that is located

within geoparks compared to the total global area (A) and total Asian and European area (B) of this type.

For each of the 15 first legend level lithology classes in the GLiM dataset less than 1% area is covered

by the 147 existing geoparks (fig. 5A), since the total geopark area is small in comparison to the

global land surface area. Intermediate volcanic rocks and intermediate plutonic rocks are covered the

most by geoparks, evaporites the least. None of the ice and glacier surface is present in geoparks.

These percentages are in general slightly higher on the Asian extent, and substantially higher on the

European extent (fig. 5B). Especially igneous and volcanic rocks are best represented in geoparks,

with a percentage area in geoparks >5% in Europe, while the percentage area in geoparks is lower for

sedimentary and metamorphic rocks. The evaporites are the exception, only a very small percentage

is located in Asian geoparks and 0% is present in European geoparks. The three legend levels

combined yield a total of 437 unique lithology type combinations. For only 31 of these types the

16

percentage area in geoparks >1% on the global extent. One lithology type, unconsolidated sediments

with mixed grain sized and black shales mentioned (sumxbs), is almost entirely (99.97) present in the

geoparks. On the other hand, 65% of the 437 unique third level lithology classes is not present in any

geopark. Of the 227 unique lithology types in Asia the percentage not present in geoparks is 65% as

well. In Europe 45% of 177 unique lithology types does not occur in geoparks.

Figure 5: The percentage of the area of each first legend level lithology type from the GLiM dataset that is

located within geoparks compared to the total global area (A) and total Asian and European area (B) of this type.

3.3 Human influence in geoparks and random samples

The HF1993, HF2009 and gHM are all higher in geoparks than in random samples, on all extents (fig.

6A; Appendix I fig. 4). This difference is significant for all tests on the global and Asian extent (table

4). In Europe the difference between geoparks and random samples is significant for the majority of

the human footprint comparisons. For the gHM the difference is not significant in the majority of

comparisons.

3.4 Human footprint change in geoparks and random samples

The HFchange is slightly positive for geoparks and random samples globally and in Asia, but slightly

negative for Europe (fig. 6B). For both geoparks and random samples the distribution is symmetrical

around zero. The difference between geopark and random sample HF change is not significant on any

extent (table 4). However, the minima and maxima of the random sample HF change are higher than

the geopark range.

17

Table 4. Percentage of tests where HI is significantly different in geoparks than in random sample sets at α= 0.05

and α = 0.01. p values are corrected with the Holm-Sidak method.

Global Asia Europe

α = 0.05 α = 0.01 α = 0.05 α = 0.01 α = 0.05 α = 0.01

HF1993 100 100 100 100 81 56

HF2009 100 100 100 100 64 43

HFchange 0 0 0 0 0 0

gHM 100 100 100 100 17 4

Figure 6. Distribution of human influence layer means of the geoparks and random samples on the global (blue),

Asian (red) and European (green) extent. The random sample box plots display the combined data from all 100

random sample sets.

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4. Discussion

This study compared geodiversity, human influence and human influence change in geoparks with geodiversity, human influence and human influence change in the world, Asia and Europe. These results show that the GDsum, topographic diversity and LithoDiv are significantly higher in geoparks, while soil and hydrological diversity are not. The GDsum is also strongly correlated with the LithoDiv and moderately correlated with the topographic layers. In Europe the highest percentages of lithology and soil types are located in geoparks, but not all types are represented. Human influence is significantly higher in geoparks, except for Europe, and the HFchange is not significantly different from the random samples.

4.1 Comparing the geodiversity index

The GDsum, LithoDiv, SlopeSTD and SlopeRange are significantly higher in geoparks. The strong

correlation between the GDsum and LithoDiv indicates that lithological diversity highly contributes to

the total geodiversity, in both geoparks and random samples. The finding that the correlation

between the GDsum and topographic diversity is only moderate in geoparks, while strong relations

occur in random samples, emphasizes the importance of lithological diversity for the total

geodiversity in geoparks even more. This is in line with earlier listings of the main geoheritage types

in geoparks, which are mostly related to geology and geomorphology (e.g. karst, tectonics,

landforms, and stratigraphical geoheritage) (Brilha, 2018). Soil diversity is not significantly higher in

geoparks and the correlation between the GDsum and SoilDiv is lower in geoparks than in random

samples. This demonstrates that soil diversity is less important in geoparks and is also in agreement

with the ill representation of pedological heritage in geoparks reported by Ruban (2017). Though a

literature review concluded that hydrological diversity gets significant attention in geoparks (Ruban,

2019), this is not reflected by the lower hydrological diversity in geoparks and weak correlation

between the hydrological diversity layers and the GDsum.

A possible explanation for the high lithological and topographical diversity is the emphasis on

lithological and geomorphological diversity in the geopark application procedure, which was

mentioned in the hypothesis. Aspiring parks get more points assigned for higher numbers of different

rock types and distinct geological or geomorphological features (UNESCO, 2016). Hydrology and soils

are not explicitly taken into account in this assessment. The higher topographic variability in

geoparks is likely also related to high number of geoparks located in mountains. Of all geoparks,

58.5% are located within mountainous areas as defined by the Global Mountain Biodiversity

Assessment (GMBA), while only 12.3% of the global land area is considered mountainous (Körner et

al., 2017). The areas with the highest GDsum values are also mostly located in mountainous areas

(fig. 7). In addition, the many features aesthetic values and the better exposed rocks and tectonic

structures make mountainous areas popular for geopark establishment (Brilha, 2018).

The bias towards mountainous areas also offers an additional explanation for the not

significantly higher soil and hydrological diversity. Hydrology in mountainous areas consists often of

small and fragmented streams that might be too small to appear in the HydroSHEDS dataset that was

used for the RiverLength layer (Lehner et al., 2013). Patterns of high soil diversity do also not

necessarily coincide with mountainous areas or areas with high lithology diversity (Appendix I fig 5),

as soil diversity is the result of the interplay of more factors than relief and parent material alone

19

(Jenny, 1961). In earlier research lower soil diversity was associated with extreme temperatures and

precipitation (Minasny et al., 2010) and increasing elevation (Vacek et al., 2020). In the humid tropics

and subtropics soil diversity was highly influenced by time and erosion processes in mountainous and

hill areas, while parent material had more influence on diversity in the lower altitudes (Gracheva,

2011).

While the GDsum, SlopeSTD, SlopeRange and LithoDiv are significantly higher in geoparks,

the random samples contain higher maximum values (fig 2). Considering that the highest GDsum

values are located in mountainous areas (fig. 7), it could be that these areas are too sparsely

populated to become geoparks, as an area needs to have at least 7 inhabitants/km2 to become a

geopark (UNESCO, 2020). This means that though geoparks capture regions with high geodiversity,

they do not capture the areas with the highest geodiversity on earth.

Figure 7. Map showing the areas with the highest GDsum values, overlain with mountain polygons as defined by

the GMBA inventory. Areas with high geodiversity highly coincide with mountainous areas. The highest mean

GDsum values in geoparks are between 12-15, while the highest mean GDsum in random samples are between

15 and 17.5.

4.2 Comparing soil and lithology types

The percentage area in geoparks are very low on the global and Asian extent, due to the small

geopark surface relative to the continent, but considerable percentages of some soil and lithology

types were found in Europe. The high percentages of igneous rocks are in line with an earlier study

on representation of geodiversity types in geoparks on the national level (Ruban, 2017) and the very

high percentage area in geoparks of andosols, pyroclastics and volcanic rocks suggest that relatively

many geoparks are in areas shaped by (past) volcanic activity. This endorses earlier statements about

the popularity of volcanic geoheritage in geoparks, as concluded from qualitative analyses (Liu et al.,

2012; Brilha, 2018). Many of the lowest percentage area in geoparks soil types, such as solonetz,

plinthosols, durisols, gypsisols are zonal soils associated with (semi) arid climatic zones. The same

holds true for the low percentage area in geoparks for evaporites, which are only preserved in arid

climates. This is a result of the absence of geoparks in arid climates, and stresses the importance of

20

reducing the spatial bias in geopark locations, which was earlier recommended too by Ibáñez et al.

(2018) and Ibáñez & Brevik (2019). Geoparks should be present in all climate zones to capture all soil

types, as many soil types are zonal and thus restricted to a specific climatic area (Bockheim, 2005).

Despite relatively high percentage area in geoparks in European geoparks, still a considerable

amount of soil and lithology types are not present in geoparks when taking into account the most

detailed legend levels of the datasets. This percentage is higher globally and in Asia than in Europe,

indicating that in Europe geoparks are a better representation of all soil and lithology types present

on the continent. This is most likely due to the large amount of geoparks in Europe which are

relatively evenly distributed over the continent when compared to Asia or the world (Appendix I, fig.

1). Some soil and lithology types are globally low in abundance and have a small percentage area in

geoparks, which means that very little of the already little that is there is protected. This applies for

example to histosols, while these soils can have a high scientific value because these can function as

paleo archives (Seijmonsbergen et al., 2010; Seijmonsbergen et al., 2019). In future geopark

designations attention should be paid to poorly or non-represented soil and lithology types to

increase the representativeness of geoparks.

4.3. Comparing human influence

Human influence is higher in geoparks for all three indices, indicating a high presence of human

activities in geoparks. In Europe, the difference between geoparks and random samples was not

significant for all comparisons, especially not for the gHM. This is most likely because of areas with

low human influence, such as Siberia, the Sahara and central Asia (Venter et al., 2016a; Kennedy et

al., 2019) that are randomly sampled relatively often because of their large surface area. As such

areas are restricted in Europe the difference between geoparks and random samples becomes less

pronounced. As mentioned in paragraph 4.1 is having a significant population a prerequisite for

becoming a geopark (UNESCO, 2020). Thereby is geoconservation in geoparks combined with the

promotion of the local economy (Farsani et al., 2011; UNESCO 2015) and this clearly leads to

geoparks being located in areas modified by human activities. These human activities in geoparks do

not necessarily have to be a threat to geodiversity when properly managed and some geoheritage

types, such as mining, were even created by human activities (Crofts & Gordon, 2015; Brilha, 2018).

However, many activities associated with economical development as urbanization, geotourism and

land use change can be potential threats to geodiversity (Crofts & Gordon, 2015; Hjort et al., 2015)

and the high human influence found in geoparks further emphasizes the need of adequate

management in geoparks.

4.4 Comparing human influence change

In the 1993-2009 period the human footprint decreased in about half of the geoparks and increased

in the other half, given the roughly symmetrical distribution around zero. There is no significant

difference between geoparks and random samples, indicating that geopark establishment does not

lead to a different HFchange trend. It should be noted that UNESCO only begun with establishing

geoparks in 2001 and that 87 out of 147 geoparks in the analysis were established after 2009, but as

geoparks need to be managed by a recognized legislative body (UNESCO, 2015) it can be assumed

that some sort of conservation measures were already implemented. Looking at the spatial

distribution of areas with increasing and decreasing trends, the HF decreased the most in north-

21

western Europe and a part of southeastern China, where many geoparks are situated (Appendix I fig.

6). However, adjacent regions where also many geoparks are situated such as southern Europe,

Southeast Asia and central China experienced an increase in HF, which could explain the symmetrical

distribution of the HFchange. In Asian geoparks a lacking legislative framework led to geotourism

causing an increase in infrastructure, agriculture and urbanization, which eventually lead to erosion

of geosites (Sumanapala & Wolf, 2020). Such a study is not available for European geoparks.

Decreases in the HF mainly occurred in countries with strong legislative enforcement (Venter et al.

2016b), which further emphasizes the importance of a strong legislative framework and legislative

body in geoparks.

4.5 Technical limitations and future directions

This research had to deal with several technical limitations, the most evident being the limited cover

of the RiverLength and GDsum layers, causing the northern most geoparks to be excluded from some

of the analyses. Due to the incompleteness of the geopark polygon dataset about half of the geopark

areas had to be estimated, causing inaccuracies. Thereby, the global datasets used are unavoidably

subject to spatially uneven data availability and quality, and previous research has shown that higher

diversity in areas can also be the result of the larger scale of the input data in these areas (Minasny et

al., 2010; Gerasimova et al., 2020). In addition, due to its global scale the SoilGrids data does not

contain some uncommon, but relevant soil types such as paleosoils and anthroposols, while these

can contain valuable information about processes that shaped landscapes in the past

(Seijmonsbergen et al., 2019). In both the HF and gHM climate change is not included as a pressure,

while it is considered to be a threat to geodiversity (Gordon et al., 2012; Crofts & Gordon, 2015). In

this research a grid-based GGI was used, but different methods that are qualitative-quantitative

based (Zwoliński et al., 2018) or use centroids (Forte et al., 2018; da Silva et al., 2019) are being

developed. Topographic diversity was used as a substitute for geomorphological diversity in the GGI,

as there is no global geomorphological map available. Hydrological diversity was assumed to increase

linearly with increasing river length and lake area, but differences between drainage densities or lake

types were not taken into account. Despite these data driven limitations, the data used in this

research are amongst the best available datasets that allow an analysis and comparison the global

extent. If to increase the accuracy of these results, further refinements and expansion of data

coverage are necessary.

These results provide organizations, including UNESCO, as well as policymakers with

important information on the extent and representativeness of protected global geodiversity and

identify aspects to which more attention should be paid. In order to fully represent global

geodiversity, geoparks should focus more on soil and hydrological diversity, in addition to lithological

and topographic diversity. Additional research will be needed for this, as soil sites are not sufficiently

studied (Brilha & Reynard, 2018). Thereby should future geoparks be targeted at the large number of

soil and lithology types that are not yet included in geoparks. This focus will probably also help

straightening the uneven distribution of geoparks globally. Establishing monitoring programs in

geoparks as proposed by Brilha (2018) and Sumanapala & Wolf (2020) are needed, as these results

show that geopark areas are significantly modified by human activities. In addition, the HF and gHM

layers only reflected the cumulative human impact on areas. More research into the relative

importance of each specific driver of human influence is necessary to make management practices

targeted more efficiently.

22

5. Conclusion

The results show that the geoparks' aim of protecting sites and landscapes with international

geological significance leads to the protection of areas with a significantly higher geodiversity,

lithological and topographical diversity when compared to global, Asian and European geodiversity.

Soil diversity and hydrological diversity, however, are less represented in geoparks and should be

focus in future geopark designations. Soil and lithology types that are not or hardly represented in

geoparks should be included in future geoparks, paying special attention to those types that are low

in abundance and are vulnerable to human impacts. Geoparks are located in areas that are relatively

highly modified by human activities and HF change data showed a similar trend in geoparks and

random samples. Human activities should be monitored and assessed by a strong legislative body to

prevent damage to geodiversity. Despite some shortcomings are the datasets used in this analysis

the best available data that allow for a global comparison. Future expansion and refinement of global

datasets should further increase the accuracy of these results.

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Appendix I

Additional figures and tables

Figure 1. Distribution of the 147 geoparks. Most geoparks are located in Europe and East Asia, especially in

China.

Figure 2. Distribution of SlopeRange (A) and LakeArea means (B ) in the geoparks and random samples on the

global (blue), Asian (red) and European (green) extent. The random sample box plots display the combined data

from all 100 random sample sets.

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Figure 3. Blue histograms showing the distributions of Spearman's r (rs) between the GDsum and geodiversity

sub layers (AB columns) for the random sample sets for the global (first row), Asian (second row) and European

(third row) extent. The shaded area shows the 95% confidence interval calculated from the correlation

distributions. The geopark rs are displayed as red dotted lines. Geopark and random sample correlations are

considered significantly different from each other when the red dotted line lies outside of the green shaded 95%

confidence interval. The number of random sample sets used is shown in the upper right corner of the plots.

Table 1. Correlations between geodiversity sublayers and the GDsum. For the random samples the 95% CI of the

rs distribution is given. The percentage of random sample rs that are significant at alpha = 0.05 is displayed

below the interval.

Geoparks rs Random samples rs [min, max]

Global Asia Europe Global Asia Europe

SoilDiv 0.32 0.21 0.31 [0.46 - 0.70] [0.21 - 0.68] [0.39 - 0.77]

p<0.01 n.s. p<0.05 100% 91.4% 100%

LithoDiv 0.67 0.77 0.63 [0.62 - 0.78] [0.53 - 0.81] 0.53 - 0.82]

p<0.01 p<0.01 p<0.01 100% 100% 100%

SlopeSTD 0.47 0.40 0.44 [0.61 - 0.79] [0.67 - 0.88] [0.63 - 0.85]

p<0.01 p<0.01 p<0.01 100% 100% 100%

SlopeRange 0.46 0.38 0.45 [0.63 - 0.80] [0.67 - 0.88] [0.65 - 0.86]

p<0.01 p<0.01 p<0.01 100% 100% 100%

RiverLength 0.19 0.23 0.12 [-0.01 - 0.34] [-0.20 - 0.28] [-0.31 - 0.13]

p<0.05 n.s. n.s. 55.1% 1% 10.3%

LakeArea 0.09 0.11 0.08 [-0.09 - 0.26] [-0.35 - 0.18] [-0.39 - 0.08]

n.s. n.s. n.s. 18.2% 7% 25.3%

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Figure 4. Distribution of HF1993 (A) and gHM 2016 means (B ) in the geoparks and random samples on the

global (blue), Asian (red) and European (green) extent. The random sample box plots display the data from all

100 random sample sets together.

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Figure 5. Maps showing the different spatial patterns of soil (A) and lithological (B) diversity. Mountainous

areas are marked with black lines. Based on data by Hannes Versteegh from Muellner-Riehl et al. (2019).

31

Figure 6. Maps showing the areas where the human footprint decreased (A) and increased (B) between 1993 and

2009. Based on data from Venter et al. (2016a)

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Appendix II

Geopark database

continent country geopark name

year of designation Shapfile

surface area (km^2)

Africa Morocco mgoun_global 2014 NA 5730

Africa Tanzania ngorongoro_lengai 2018 NA 11886

Asia China hongkong 2011 digitised from online map 150

Asia China leiqiong 2006 digitised from online map 3050

Asia China ningde 2010 digitised from online map 2660.34

Asia China yandangshan 2005 digitised from online map 298.8

Asia China alxa_desert 2009 NA 630.37

Asia China arxan 2017 NA 3653.21

Asia China dali_mount_cangshan 2014 NA 933

Asia China danxiashan 2004 NA 292

Asia China dunhuang 2015 NA 2067

Asia China fangshan 2006 NA 954

Asia China funiushan 2006 NA 5858.52

Asia China guangwushan_nuoshuihe 2018 NA 1818

Asia China hexigten 2005 NA 1750

Asia China huanggang_dabieshan 2018 NA 2625.54

Asia China huangshan 2004 NA 1200

Asia China jingpohu 2006 NA 1400

Asia China jiuhuashan 2019 NA 139.7

Asia China keketuohai 2017 NA 2337.9

Asia China leye_fengshan 2010 NA 930

Asia China longhushan 2007 NA 966.63

Asia China lushan 2004 NA 500

Asia China mount_kunlun 2014 NA 7033

Asia China sanqingshan 2012 NA 433

Asia China shennongjia 2013 NA 1022.72

Asia China songshan 2004 NA 464

Asia China stone_forest 2004 NA 350

Asia China taining 2005 NA 492.5

Asia China taishan 2006 NA 418.36

Asia China tianzhushan 2011 NA 413.14

Asia China wangwushan_daimeishan 2006 NA 986

Asia China wudalianchi 2004 NA 720

Asia China xingwen 2005 NA 156

Asia China yanqing 2013 NA 620.38

Asia China yimengshan 2019 NA 1804.76

Asia China yuntaishan 2004 NA 556

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Asia China zhangjiajie_sandstone_peak_forest 2004 NA 398

Asia China zhijindong 2015 NA 170

Asia China zhongnanshan 2009 NA 1074.85

Asia China zigong 2007 NA 1630.46

Asia Indonesia rinjani_lombok 2018 digitised from online map 2800

Asia Indonesia batur 2012 NA 370.5

Asia Indonesia ciletuh_palabuhanratu 2018 NA 1260

Asia Indonesia gunung_sewu 2015 NA 1802

Asia Iran qeshm_island 2017 digitised from online map 2063

Asia Japan itoigawa 2009 digitised from online map 750

Asia Japan oki_island 2013 digitised from online map 673.5

Asia Japan san_in_kaigan 2010 digitised from online map 2458

Asia Japan toya_caldera_and_usu_volcano 2009 digitised from online map 1181

Asia Japan unzen_volcanic_area 2009 digitised from online map 459.52

Asia Japan aso 2014 NA 1198

Asia Japan izu 2018 provided by geopark 2027

Asia Japan mt_apoi 2015 provided by geopark 364.3

Asia Japan muroto 2011 provided by geopark 248.2

Asia Korea jeju_island 2010 digitised from online map 1847

Asia Korea cheongsong 2017 NA 845.71

Asia Korea mudeungsan 2018 NA 1051

Asia Malaysia langkawi_island 2007 digitised from online map 478

Asia Thailand satun 2018 digitised from online map 2597

Asia Turkey kula 2013 provided by geopark 300

Asia Vietnam cao_bang 2018 NA 3000

Asia Vietnam dong_van_karst_plateau 2010 NA 2356

Europe Austria ore_of_the_alps 2014 NA 211

Europe Austria eisenwurzen 2004 provided by geopark 586

Europe Belgium famenne_ardenne 2018 provided by geopark 915

Europe Croatia papuk 2007 provided by geopark 524

Europe Croatia vis_archipelago 2019 provided by geopark* 6661

Europe Cyprus troodos 2015 NA 1147

Europe Czech Republic bohemian_paradise 2005 provided by geopark 742

Europe Denmark odsherred 2014 digitised from online map 355

Europe Finland rokua 2010 NA 1326

Europe France bauges 2011 NA 856

Europe France chablais 2012 NA 872

Europe France haute_provence 2004 NA 2300

Europe France luberon 2004 NA 1953

Europe France beaujolais 2018 provided by geopark 1550

Europe France causses_du_quercy 2017 provided by geopark 1855

Europe France monts_ardeche 2014 provided by geopark 2280

Europe Germany bergstrasse_odenwald 2004 NA 3500

Europe Germany harz_braunschweiger_land_ostfalen 2005 NA 11500

34

Europe Germany swabian_albs 2005 NA 6688

Europe Germany terra_vita 2004 provided by geopark 1560

Europe Germany vulkan_eifel 2004 provided by geopark 1220

Europe Germany/ Poland muskau_arch 2011 provided by geopark 578.8

Europe Greece sitia 2015 digitised from online map 517

Europe Greece chelmos_vouraikos 2009 provided by geopark 647

Europe Greece lesvos_island 2004 provided by geopark 1636

Europe Greece psiloritis 2004 provided by geopark 1159

Europe Greece vikos_aoos 2010 provided by geopark 1200

Europe Hungary bakony_balaton 2012 provided by geopark 3244

Europe Hungary/ Slovakia novohrad_nograd 2010 NA 1587

Europe Iceland reykjanes 2015 digitised from online map 852

Europe Iceland katla 2011 provided by geopark 9542

Europe Ireland burren_cliffs_of_moher 2011 provided by geopark 530

Europe Ireland copper_coast 2004 provided by geopark 50

Europe Ireland/ Northern Ireland marble_arch_caves 2004 NA 180

Europe Italy madonie 2004 digitised from online map 400

Europe Italy sardinia 2007 digitised from online map 3800

Europe Italy tuscan_mining_park 2010 digitised from online map 1087

Europe Italy apuan_alps 2011 NA 494

Europe Italy parco_del_beigua 2005 NA 392

Europe Italy sesia_val_grande 2013 NA 2023

Europe Italy adamello_brenta 2007 provided by geopark 1188

Europe Italy cilento 2010 provided by geopark 1841

Europe Italy pollino 2015 provided by geopark 1925

Europe Italy rocca_di_cerere 2007 provided by geopark 1298

Europe Netherlands hondsrug 2013 provided by geopark 1000

Europe Norway trollfjell 2019 digitised from online map 10082

Europe Norway gea_norvegica 2006 provided by geopark 3000

Europe Norway magma 2010 provided by geopark 3000

Europe Portugal arouca 2009 NA 327

Europe Portugal naturtejo 2006 NA 4624.4

Europe Portugal terras_cavaleiros 2014 provided by geopark 700

Europe Portugal azores 2013 provided by geopark* 12884

Europe Romania hateg_country_dinosaur 2005 NA 1024

Europe Slovenia idrija 2013 NA 294

Europe Slovenia/ Austria karawanken 2013 NA 1067

Europe Spain basque_coast 2010 digitised from online map 89

Europe Spain cabo_de_gata 2006 digitised from online map 500

Europe Spain el_hierro_canary_islands 2014 digitised from online map 595

Europe Spain lanzarote 2015 digitised from online map 2500

Europe Spain molina_and_alto_tajo 2014 NA 4520

Europe Spain sobrarbe 2006 NA 2202

Europe Spain subbeticas 2006 NA 320

35

Europe Spain villuercas_ibores_jara 2011 NA 2544.4

Europe Spain catalunya_central 2012 provided by geopark 1300

Europe Spain conca_de_tremp_montsec 2018 provided by geopark 2050

Europe Spain courel_mountains 2019 provided by geopark 577.85

Europe Spain loras 2017 provided by geopark 950.76

Europe Spain sierra_norte_di_sevilla 2011 provided by geopark 1774.84

Europe UK english_riviera 2007 digitised from online map 104

Europe UK geo_mon 2009 digitised from online map 1800

Europe UK forest_fawr 2005 provided by geopark 763

Europe UK north_pennines_aonb 2004 provided by geopark 1938

Europe UK north_west_highlands 2005 provided by geopark 2100

Europe UK shetland 2009 provided by geopark 1468 North America Canada perce 2018 digitised from online map 555 North America Canada stonehammer 2010 digitised from online map 2500 North America Canada tumbler_ridge 2014 NA 7822 South America Brazil araripe 2006 provided by geopark 3441 South America Chile kutralkura 2019 NA 8053 South America Ecuador imbabura 2019 NA 4794.34 South America Mexico comarca_minera 2017 provided by geopark 1848 South America Mexico mixteca_alta 2017 provided by geopark 415.4 South America Peru

colca_y_volcanes_de_andagua 2019 NA 6010.91

South America Uruguay grutas_del_palacio 2013 NA 3611

* shapefile provided by the geopark, but edited to contain land area only

36

Appendix III

Python scripts, geopark shapefiles and geodiversity and human influence results are available via

https://github.com/emmamarianina/Geopark-GI-HI