The Importance of Vegetation Configuration in Coastal

Biology Department Research Group Terrestrial Ecology ______

THE IMPORTANCE OF VEGETATION CONFIGURATION IN COASTAL DUNES TO PRESERVE DIVERSITY OF MARRAM- ASSOCIATED INVERTEBRATES IS HABITAT CONFIGURATION A DRIVER OF DIVERSITY IN DUNES?

Noëmie Van den Bon Studentnumber: 01506438

Supervisor(s): Prof. Dr. Dries Bonte Dr. Martijn Vandegehuchte Scientific tutor: Ruben Van De Walle

Master’s dissertation submitted to obtain the degree of Master of Science in Biology

Academic year: 2019 - 2020

© Faculty of Sciences – research group Terrestrial Ecology All rights reserved. This thesis contains confidential information and confidential research results that are property to the UGent. The contents of this master thesis may under no circumstances be made public, nor complete or partial, without the explicit and preceding permission of the UGent representative, i.e. the supervisor. The thesis may under no circumstances be copied or duplicated in any form, unless permission granted in written form. Any violation of the confidential nature of this thesis may impose irreparable damage to the UGent. In case of a dispute that may arise within the context of this declaration, the Judicial Court of Gent only is competent to be notified.

Table of content

1. Introduction ...... 5 1.1. The status of biodiversity and ecosystems ...... 5 1.2. Ecological concepts in species distribution and ecological communities ...... 5 1.3. Habitat configuration as driver of diversity in ecological communities ...... 6 1.4. Nature conservation ...... 7 1.5. Marram dunes as model-system to assess the influence of habitat configuration on diversity ...... 8 1.6. The invertebrate fauna of marram dunes ...... 9

2. Objectives ...... 11 2.1. Hypothesis 1 ...... 11 2.2. Hypothesis 2 ...... 12 2.3. Hypothesis 3 ...... 12

3. Material and methods ...... 13 3.1. Study area ...... 13 3.2. Data collection ...... 14 3.3. Marram grass properties ...... 15 3.4. Spatial configuration of marram grass around the sampling sites ...... 15 3.5. Species identification ...... 17 3.6. Data analyses ...... 17

4. Results ...... 20 4.1. Data exploration ...... 20 4.2. Models for the local environmental variables ...... 23 4.2.1. The influence of the spatial configuration on marram inflorescences ...... 24 4.2.2. The influence of the spatial configuration on marram vitality ...... 25 4.2.3. The influence of the spatial configuration on marram height ...... 25 4.3. Models for habitat specialisation ...... 26 4.3.1. The influence of the spatial configuration of marram grass on general invertebrate diversity .. 27 4.3.2. The influence of the spatial configuration of marram grass on dune-specific invertebrate diversity ...... 30 4.3.3. The influence of the spatial configuration of marram grass on marram-specific invertebrate diversity ...... 33

5. Discussion ...... 38 5.1. A dynamic environment for higher quality marram grass ...... 38 5.2. Does the species-area relationship hold in the stabilisation of dunes? ...... 39

5.3. Does high-quality marram grass benefit marram-specific invertebrate species? ...... 40 5.4. Does the response to spatial configuration depend on habitat specialisation? ...... 41 5.5. Additional observations and findings ...... 42 5.6. Importance and implications of the results ...... 42 5.7. How do we proceed? ...... 43

6. Conclusion ...... 45

7. Summaries ...... 46 7.1 English summary ...... 46 7.2. Nederlandstalige samenvatting ...... 49 7.3. Laymen summary ...... 51

8. Acknowledgement ...... 52

9. References ...... 53

10. Appendix ...... 59 10.1. Classification ...... 59 10.2. Output of models ...... 72 10.2.1. Models for the local environmental variables ...... 72 10.2.2. Models for diversity (France, Belgium and the United Kingdom) ...... 73 10.2.2.1. Models for general diversity ...... 73 10.2.2.2. Models for dune-specific diversity ...... 74 10.2.2.3. Models for marram-specific diversity ...... 75 10.2.3. Models for diversity (France and Belgium) ...... 76 10.2.3.1. Models for general diversity ...... 76 10.2.3.2. Models for dune-specific diversity ...... 79 10.2.3.3. Models for marram-specific diversity ...... 81 10.3. R-code ...... 84

1. Introduction

1.1. The status of biodiversity and ecosystems

Humanity is altering the biosphere across all spatial scales through high population growth and behaviours like production, consumption, trade, etc. Global change is directly driven by changes in land use, exploitation, climate change, pollution, and invasive alien species (IPBES, 2019). This results in an alarming decline in biodiversity, defined as the variety of life consisting of the diversity within and between species and of ecosystems (Cardinale et al., 2012). Anthropogenic land conversion is the main driver of species extinction and habitat loss, but climate change is expected to become equally or more important. This will be due to continuous anthropogenic global warming causing climate change and the latter aggravating the negative effects of other drivers of global change (Dawson et al., 2011; IPBES, 2019). Climate change is destabilizing ecosystem functioning and resilience through global species and genetic redistribution and increased spatial and temporal variability (Bellard et al., 2012). On land, around 75% of the surface has been converted and around 25% of assessed animal and plant species are threatened with extinction (IPBES, 2019). Extinction is a natural process balanced by speciation, but observations indicate that we are now causing the sixth mass extinction. Mass extinctions consist of a species loss over 75% within a geologically short time interval (Barnosky et al., 2011; Ceballos et al., 2015). The loss of biodiversity and degradation of ecosystems has negative consequences on ecosystem functioning and this is expressed in ecosystem services, on which we strongly depend (IPBES, 2019).

Several species are known to influence their physical environment by ecosystem engineering and the biogeochemical fluxes and productivity through trophic cascades and keystone species. These system-wide effects of many species cause accelerating impacts on the ecosystem with increasing biodiversity loss (Cardinale et al., 2012). Therefore, change in species composition through extinction, redistribution, and invasion often has negative consequences for ecosystem services (Hooper et al., 2005). Variation in these consequences exists, but scientists agree it is important to ensure high diversity, from the genetic to ecosystem level, for a stable supply of ecosystem services. In times of global change and increased variability in abiotic conditions both in time and in space, diversity is seen as a buffer through complementarity, positive interactions, etc. (Wilson & Willis, 1975; Hooper et al., 2005).

1.2. Ecological concepts in species distribution and ecological communities

The distribution of species and thus ecological community structure depends on dispersal ability, stochastic and deterministic processes (Dupre & Ehrlén, 2002). Stochastic processes include random dispersal and extinction, while deterministic processes consist of environmental filtering and competitive exclusion (Van

der Plas et al., 2015). In attempts to explain community structure, the species-area relationship emerged (Preston, 1962). This shows that habitat area has an almost universal positive influence on species richness. However, this law ignores the spatial configuration, determined by area and isolation, of habitat patches (Dupre & Ehrlén, 2002; Loke et al., 2019). In 1967 MacArthur & Wilson suggested the ‘classic theory of island biogeography’ (CTIB), stating that species distribution is determined by a dynamic balance between the immigration of new species and extinction of present species. In this theory, area sets a limit on the number of individuals, population size influences the probability of extinction, and colonisation chances are lowered with the isolation of patches (Zimmerman & Bierregaard, 1986). The main ideas of these laws are useful in our understanding of community composition and species distribution, but the truth is more complicated. Recent developments, suggesting the integration of trophic species interactions, resulted in the ‘trophic theory of island biogeography’ (TTIB). In this theory, the dynamic changes in colonisation and extinction probabilities depend on the presence or absence of other species (Massol et al., 2017).

Species in fragmented areas can often be represented by metapopulation models (Levins, 1969; Hanski, 1999). The difference with CTIB lies in the situation of the source population and the scale, but the main idea is quite similar. The CTIB assumes an unthreatened mainland and a biogeographical scale, while the metapopulation model assumes a fragmented source population at a local scale. In metapopulation models the distribution of a species in a patchy landscape can be interpreted as the equilibrium of extinction and colonisation processes, the latter strongly influenced by dispersal ability. In this model also patch area and isolation are used to explain the distribution of species. According to the metapopulation model, populations are more likely to occur and persist in large and better-connected habitats (Dupre & Ehrlén, 2002; Goncalves-Souza et al., 2014). Nonetheless, climate change and increasing frequency of extreme climatic events, like severe droughts, challenge the concept and predictions based on the metapopulation model. Van Bergen et al. (2020) highlighted that responses of natural populations to extreme climatic events are highly unpredictable.

1.3. Habitat configuration as driver of diversity in ecological communities

Habitat configuration plays an important role in species distribution and can be seen as a driver of diversity in ecological communities. Habitat configuration is the spatial arrangement of patches of suitable habitat at a given time and is determined by patch area and isolation (Dupre & Ehrlén, 2002). The classic theory of island biogeography predicts that species richness is positively correlated with area and negatively with isolation. This theory assumes species as equal, while in reality, different species usually take up a different niche. This brings the aspect of habitat heterogeneity into habitat configuration, from the scale of a single patch to a fragmented landscape. The niche theory (Hutchinson, 1957) states that heterogenous environments provide more niches and may support more diverse communities. However, the interaction with area cannot be neglected. Smaller areas with increasing habitat heterogeneity, lead to a reduction of

suitable niche per species and increased risk of stochastic extinction. Consequently, it is only for larger areas that the effect of habitat heterogeneity on species richness becomes positive (Kadmon & Allouche, 2007).

Anthropogenic activities are causing shifts in habitat configuration and heterogeneity due to habitat loss, increased fragmentation, change in dynamics due to disturbances, etc. These shifts can be perceived as negative (increased isolation or reduced niche area) or positive (increased niche heterogeneity) depending on the scale and species considered (Tews et al., 2003). It is important to maintain functional connectivity, which is a combination of successful movement of organisms in space and the persistence of an organism in time at the same space (Auffret et al., 2015). Therefore, understanding the effect of habitat configuration on populations’ viability is crucial (Villard & Metzger, 2014; Bonnell et al., 2018). The influence of habitat configuration on species diversity will be addressed in this thesis project, considering possible further implications for nature conservation.

The effect of habitat configuration on species viability depends on life-history traits (LHT) (Tews et al., 2003). The possibility of a species to migrate between spatially arranged habitat patches depends on their ability to disperse, a very important LHT, and how these patches are configured (Dupre & Ehrlén, 2002). For example, adult butterflies are good dispersers, but spiders depend on passive movement (ballooning) for long-distance migrations and are thus more affected by spatial habitat configuration (Goncalves-Souza et al., 2014). Life-history traits are key determinants of the viability of and colonisation by species in fragmented landscapes. Besides dispersal ability, feeding niche and reproduction are other important LHTs. For example, species with a narrow feeding niche and low reproduction are most strongly affected by habitat loss, and species with low dispersal ability are strongly affected by increasing isolation due to fragmentation (Ockinger et al., 2010). The influence of habitat configuration on species distribution is thus potentially high and determined by the species’ life-history traits.

1.4. Nature conservation

The reason we need to conserve nature in all its forms is simple; Diaz et al. (2006) quoted “human societies have been built on biodiversity”. Our survival and well-being depend on ecosystem services and therefore on the conservation of ecosystems as a whole (Liquete et al., 2013). Unfortunately, different anthropogenic drivers are causing direct or indirect degradation of ecosystems and biodiversity loss. Efficient nature conservation is complicated due to the complexity of ecosystems and the need to integrate scientific, social, and economic understanding (Granek et al., 2010). It is often a system-based approach in which ecosystem processes are the main objective because losing ecosystems is costly and irreplaceable. However, it has become clear that some species can go extinct under that approach. There is a high need to incorporate and consider more data on species and their responses to the environment and configuration in conservation approaches to halt biodiversity loss and degradation (Maes et al., 2006).

An important aspect of species conservation is the detection of species with a disproportionately positive effect in their communities due to a rich interaction structure (Schulze & Mooney, 1994; Jordan, 2009; Sun et al., 2020). They are called keystone species and are crucial in maintaining the stability, organisation, and diversity of their natural community (Mills et al., 1993). Integrating keystone species in conservation can have direct and indirect positive effects on ecosystem integrity and species composition due to the interconnectedness of species (Lambeck, 1997; Jordan, 2009). The keystone species of interest here is marram grass (Ammophila arenaria). It shapes the physical environment by capturing sand in a dynamic environment and provides niches for multiple invertebrate species (de la Peña et al., 2009).

Also interesting in biodiversity conservation is the difference between generalist and specialist species. Specialists are limited by a narrow range of environmental conditions, while generalists tolerate a broad range. With climate change and increasing anthropogenic disturbances, studies revealed a global decline in specialists due to the direct effects of habitat degradation as increased competition with generalist species. There are concerns about functional homogenisation and studies showing that generalists contribute less to ecosystem functioning (Clavel et al., 2011). An interesting observation is that the perception of disturbance by specialists and generalists depends on whether the disturbance factor increases (destabilises) or reduces (stabilises) the extremity of the naturally occurring environmental conditions. In extreme environments, like dynamic marram dunes addressed here, specialists are evolutionarily adapted to the extreme and variable environmental conditions of the coast. Anthropogenic activities are increasing stabilisation by the cessation of sand dynamics, and consequently fixation of marram dunes. The cessation of sand dynamics is causing a shift in habitat configuration by decreasing the natural patchy configuration while increasing more aggregated areas of marram grass (Keijsers et al., 2015). This leads to the hypothesis that the response of specialist species in marram dunes to these stabilising disturbances will be negative (Attum et al., 2006), causing major implications for biodiversity conservation in marram dune ecosystems. Therefore, the presence of generalists and specialists in relation to stabilisation of marram dunes will be assessed in this thesis project. This presents the main focus of this project, namely the influence of the spatial configuration of marram dunes on generalist and specialist species.

1.5. Marram dunes as model-system to assess the influence of habitat configuration on diversity

The ecosystem addressed in this project is a coastal ecosystem, more specifically marram dunes, and is located at the boundary between marine and terrestrial systems. These dunes are characterised by variable and extreme environmental conditions and provide ecosystem services like coastal protection against flooding and storm events, the control of erosion, maintenance of wildlife, recreation, tourism, etc. (Granek et al., 2010; Keijsers et al., 2015). Anthropogenic global warming is causing increased frequency of extreme

weather events and a global average sea-level rise since 1970, with an observed rise of 2.1 mm/year over the period 1970-2015 and 3.6mm/year between 2006-2015 (IPBES, 2019; Oppenheimer et al., 2019). Coasts are densely populated worldwide, and a considerable portion of global economic wealth is generated there (Rochelle-Newall et al., 2005; Tol et al., 2008). This makes coastal protection, in the face of threatening sea- level rise and climatic hazards, a very valuable regulating service (Barbier et al., 2011). Unfortunately, coastal ecosystems are threatened due to human activities like urbanisation and recreation. This causes habitat loss, coastal squeeze, fragmentation, and dune stabilisation (Rochelle-Newall et al., 2005; Barbier et al., 2011). Therefore, the management of coastal ecosystems is an important topic and sustainable management is a must (Willis, 1989; Maes & Bonte, 2006; Portman et al., 2012).

A major player in European coastal dunes is marram grass, Ammophila arenaria. It shapes the physical environment of European coasts by trapping sand. Marram grass produces deep and extensive, long-living roots and can tolerate sand burial of around 1m a year. The viability of A. arenaria is determined by sand dynamics, causing a sparsely vegetated and thus patchy configuration in a matrix of bare sand. Human activities have been stabilising marram dunes to put a halt to the natural sand dynamics, causing, at first, higher aggregation of marram grass. Unfortunately, a direct effect of the cessation of sand dynamics and stabilisation of dunes is the degeneration of marram grass. This degeneration is strongly associated with increasing densities of antagonistic micro-organisms in the root environment (Willis, 1989; Keijsers et al., 2015). The ability to trap sand makes marram grass an ecosystem engineer and through the feedback of sand dynamics, it gets a self-organizational character (Rietkerk et al., 2008). As a result, marram dunes have adaptive capacity and resilience to disturbance (Tol et al., 2008; de Paoli et al., 2017). They naturally adapt to sea-level rise by migrating landwards. This is however blocked due to human-induced fragmentation and infrastructure. As a result of this coastal squeeze, they lose their ability to provide coastal protection and less area is available for diverse biota (Oppenheimer et al., 2019). Marram dunes are recognized as a Natura 2000 habitat as “Shifting dunes along the shoreline with Ammophila arenaria” (Natura 2000).

The normally resilient and adaptive capacity of marram dunes makes them desirable for coastal protection (Borsje et al., 2011). The current use of hard engineering structures like dams, storm surge barriers, etc. may, in the long run, increase rather than reduce the vulnerability of coastal societies, they are costly and unsustainable. The concept of ‘Building with Nature’ considers the role of A. arenaria as keystone species in coastal protection as for maintaining invertebrate diversity (van Slobbe et al., 2013).

1.6. The invertebrate fauna of marram dunes

Invertebrate diversity is gigantic and among invertebrates, arthropods account for the greatest faunal diversity in most terrestrial ecosystems and generally have greater biomass than vertebrates. Invertebrates’ small size and long history on Earth enabled them to colonise a broad array of niches. They play an

important role in maintaining ecosystems as facilitators of pollination, food web interactions, nutrient cycling, and changes in structure and fertility of soils (Wilson, 1987; Gaston, 1991; Blaum et al., 2009). Despite their size, they are also threatened by extinction because of human disturbances (Wilson, 1987). Little quantitative data exist for the status of global invertebrate diversity, but local studies in a wide range of habitats show a rapid decline of insect populations. These support the view of global invertebrate decline, with possible negative consequences for ecosystems (IPBES, 2019; Conrad et al., 2006). They are good early warning indicators due to their diversity, abundance, and short generation time (Maes & Bonte, 2006; Gerlach & Samways, 2013).

Invertebrates in marram dunes include many arthropods. Many species are restricted to the microclimate of sparsely vegetated marram patches in a matrix of warm, bare sand (Willis, 1989; Desender & Baert, 1995; Howe et al., 2010). The bare substrate between the marram tussock is exploited by thermophilic invertebrates for nesting, foraging, etc. Consequently, besides environmental influences, the spatial habitat configuration influences the viability and distribution of these species, based on their life-history traits (Goncalves-Souza et al., 2014). In Western and Northern Europe these sand dunes are almost the only habitat left for thermophilic species. With declining trends in marram dunes and increasing plant cover through stabilisation, the conservation of these specialist species and their habitat is a high priority. The aim should be to promote a dynamic dune system and natural sand movement, which would be highly beneficial to associated invertebrates, maintain habitat connectivity and improve habitat patch quality (Howe et al., 2010). This eventually brings us to the organisms of interest for this thesis project; the invertebrate diversity of marram dunes. Some more specific groups, of the order Coleoptera and class Arachnida, will be considered as well. As several species of these groups can have a pronounced habitat or microhabitat preference, it makes them interesting for conservation ecology. In this case, the focus is on those species with a distribution restricted to dunes or marram dunes (Desender & Baert, 1995).

2. Objectives

The main objective of this thesis is to understand what the influence of the spatial configuration, in terms of proportion and aggregation, of marram grass is on associated invertebrate communities and its implication for conservation.

First of all, there will be a general exploration of invertebrate diversity in marram dunes and the distribution of dune- and marram-specific species. Secondly, we will take a closer look at the influence of the spatial habitat configuration and vitality of marram grass on the invertebrate diversity and composition, regarding habitat specialisation. The spatial habitat configuration is seen as a combination of proportion (P) and aggregation (H) of marram grass.

With these objectives, the following hypotheses were addressed:

2.1. Hypothesis 1 Dune stabilisation, with increasing proportion and aggregation of marram grass, positively influences general, aboveground invertebrate diversity in accordance with the species-area relationship (Figure 1).

Figure 1: The species-area relationship for dune stabilisation (hypothesis 1)

2.2. Hypothesis 2 Dynamic marram-dunes in optimal conditions, indicated by high vitality, number of inflorescences, and length of marram tussocks, support a higher presence of marram-specific invertebrate species (Figure 2).

Figure 2: Optimal conditions support higher marram-specific invertebrate diversity (Hypothesis 2). The red pictograms represent marram-specific species, the blue more general species.

2.3. Hypothesis 3 The optimal spatial configuration of marram, in terms of proportion and aggregation, for generalist invertebrate species differs from the optimum for marram-specific, specialist invertebrate species (Figure 3).

Figure 3: The optimal spatial configuration for specialists (left) is different from the optimal for generalists (right) (Hypothesis 3)

3. Material and methods

3.1. Study area The study area for this thesis project covers coastal areas of England, France, Belgium, and the Netherlands. It is the 2 Seas area of the EU Interreg program connected by the Channel and the North Sea. This is a European Territorial Cooperation Programme, which finances the ENDURE project (Interreg2seas). The ENDURE project (Ensuring Dune Resilience against climate change) aims to improve the adaptive capacity of dunes to climate change by assessing the health and resilience of the remaining coastal dunes. The study area consists of marram-dominated, yellow dunes along the coastline of Northern France, Belgium, the Netherlands, and the South and East of England (Figure 4).

Figure 4: Map of the study area with the different sampling locations. Retrieved from Google Earth.

In the French area 9 locations were selected and at each location one or two transects (on different days), parallel to the sea, were sampled. The transects consist of multiple sampling sites, with a total of 184 sites in France. Similarly, in Belgium 15 locations were selected with a total of 214 sites, in the Netherlands 12 locations were selected with a total of 188 sites and in England 6 locations were selected with a total of 60 sites. This gives a total of 646 sites for the complete study area.

The sampling sites per location were chosen at least 20m from each other and aiming to maximize the range of different configuration levels of the surrounding marram grass patches. If two transects were sampled in the same location on different days, these are seen as separate in the analyses in order to account for differing weather conditions. The number of sampling sites (5-36) per transect depended on the length of

the marram dunes and the workforce on the field. Variability of marram grass vitality and number of inflorescences were also taken into consideration when choosing the sites per location.

3.2. Data collection Data was collected in July 2017 for the Belgian sites, during July, August and September 2018 for the French sites, during August and September 2018 and June 2019 for the Dutch sites and during July and August 2019 for the English sites. A central marram grass tussock was chosen per site. Data for each site consisted of a GPS-coordinate, a digital photograph and invertebrate sampling. Each site had a unique code as label.

GPS-coordinate: The GPS-coordinate was saved for each site’s exact location. For eight Belgian sites the GPS-coordinate was not saved. Consequently, no spatial data were available for these sites (see below).

Digital photograph: For the digital photograph, the marram grass tussock chosen as the site was photographed with a stick marked at 50cm as reference height. These photographs are used to measure some properties of marram (see below). For one site in France and three in the Netherlands no digital photograph was available. Consequently, marram properties could not be measured. For two sites in France and all the sites in Belgium the reference stick was absent, and length could not be measured. For 86 sites in France and 51 in the Netherlands the reference stick was placed upside-down. Here length was measured based on the width of the stick, possibly resulting in a higher error on these measurements. Missing data of marram properties are indicated in the data as ‘NA’ (not applicable) and were left out the analyses for that specific property.

Invertebrate sampling: The sampling of aboveground invertebrate diversity was performed by two standardized methods. The first method was sweep netting the marram grass tussock of each site for 15 seconds. This was done with a 40cm diameter butterfly net with fine mesh. This method was different for the Belgian sites, here the area within a 5m radius around the sampling site was sampled. The second method for sampling was the manual collection of aboveground invertebrates within the tussock of the site for 5 minutes. For both methods separately the collected individuals were put in 60 ml containers with 70% ethanol and labelled with the site’s unique code. The code consisted of the site number, location and the letter ‘N’ (net) for the first sampling method and the letter ‘M’ (manual) for the second sampling method.

3.3. Marram grass properties For each site 3 properties, based on the digital photographs taken, were estimated: abundance of inflorescences, the vitality and the height of the tussock. A random selection of twenty-five photographs per country were inspected in advance for an overview of existing variation.

The number of inflorescences was estimated by giving a score between 0-4 (Table 1, right). The vitality was estimated based on the visual appearance as marram grass tends to turn brown when degenerating. This property was also scored from 0-4 based on the percentage of green present (Table 1, left). The height, which can be used as an indicator of good environmental conditions (Moles et al., 2009), was measured based on the reference stick marked at 50cm in the digital photographs. To limit observer variability, the estimations of these properties were done by the same person (Goodenough et al., 2012; Van Maldeghem et al., 2019). Vitality Inflorescences

0 0-20% green 0 absent 1 20-40% green 1 few 2 40-60% green 2 some

3 60-80% green 3 many 4 80-100% green 4 very many Table 1: Scores for vitality (left) and number of inflorescences (right).

3.4. Spatial configuration of marram grass around the sampling sites Categorical maps, with different vegetation types as discrete patches within a matrix, are derived from aerial photographs of the sampled locations (Figure 5). The RGB and NIR photographs had a minimal spatial resolution of 0.4 m2 for Belgium, 0.5 m2 for France and England (UK), and 0.25 m2 for the Netherlands. For Belgium and the Netherlands, the different types of vegetation at the locations were distinguished based on certain red colour values of raster cells in combination with height data obtained from LiDAR, which had a resolution of 1m2. For the French and English sites information on height was not available, consequently the vegetation types were classified on colour values only.

Figure 5: Representation of the categorical map for a location at the Belgian coastline (light green represents the vegetation type of marram grass). The red dots represent sampling sites.

The spatial configuration of the surrounding marram of the site’s central tussock was approximated through the calculation of 2 landscape metrices. They were both calculated for four different landscape areas, determined by four circles with radius 5, 10, 20 and 50 meters around the sampling sites, resulting in landscape areas of respectively 78.54m2, 314.16m2, 1256.64m2 and 8753.98m2.

The proportion of marram grass in the area (P): This proportion was calculated as the ratio between the number of raster cells classified as marram grass (A) and the total number of raster cells in the area (T).

! � = "

The spatial autocorrelation of marram grass patches in the area (H): This autocorrelation was calculated based on the join-count statistic (Kabos & Csillag, 2002). It is a statistic used for binomial data (marram grass (1)/no marram grass (0)) and quantitatively determines the degree of clustering or dispersion of patches. It will calculate the sum of joins, thus were two raster cells share a boundary. The possible joins that can occur are: 1-1, 1-0 and 0-0 joins. In this case, for the spatial autocorrelation of marram grass, we are only interested in the sum of 1-1 joins. We calculate this latter as followed: 1 �� = ∗ � � (� ∗ � ∗ � ) 2 #$ # $

Where x is 1 or 0 when the raster cell is respectively marram grass or not, wij is the spatial weight and is equal to 1 or 0 when raster cell ‘i’ respectively shares or doesn’t share a common boundary with cell ‘j’.

For a measure of the spatial autocorrelation, which indicates the aggregation of marram grass, the z-score is calculated as followed:

�� − �� = �%&'()*(+

Where the observed sum of 1-1 joins (observed MM) is compared to the expected sum when patches would be randomly distributed in the area. The z-value ranges from negative, meaning patches are more dispersed than under a random distribution, to positive, meaning marram grass patches occur aggregated (Figure 6: Fortin & Dale, 2009).

Figure 6: Representation of spatial patterns for a positive spatial autocorrelation (a), no spatial autocorrelation characterised by randomness (b) and a negative spatial autocorrelation (c) (Fortin & Dale, 2009).

From these two landscape metrices other spatial metrices can be derived, due to their high correlation, like edge, number of patches etc. As an example, a higher proportion value (P) within a certain autocorrelation value corresponds to a higher amount of edge and higher autocorrelation values within a proportion value corresponds to a lower amount of edge (Van Maldeghem et al., 2019).

3.5. Species identification From all the collected arthropods the adult individuals were identified and immature individuals (nymphs and larvae) were excluded from the data. Identification of individuals was done to a certain taxonomic level, depending on the group (Appendix 10.1.). Individuals with a similar morphology but not possible to identify to the species level were classified as a morphospecies. Individuals from the order Diptera and Hymenoptera were often classified to higher order because of their complex and poorly known taxonomic classification. This leads to an underestimation of species richness. The identified species were also assessed on their habitat specialisation. They were classified as marram-specific, dune-specific or general.

3.6. Data analyses Data analysis was done using R Statistical Software (R Core Team, 2018). For diversity exploration, the observed data was coupled with classification and habitat specialisation of the corresponding species/morphospecies. This data was used to visualise distributions of diversity and habitat specialisation in the study area.

Models for the local environmental variables: Environmental data of the sampling sites with information on ordinal classes of number of inflorescences, ordinal classes of vitality, and height of the marram tussock, were put together with the spatial data. The spatial data contained continuous measures of marram proportion (P) and spatial autocorrelation (H) for each scale (radius of 5, 10, 20 and 50m) per site. The values of the spatial autocorrelation were rescaled on the maximum value per country, to allow better comparison between the different scales. This resulted in both P- and H-values ranging between 0 and 1. Environmental and spatial data was available for all the countries in the study area (France, Belgium, the Netherlands and England).

Each environmental variable (number of inflorescences, vitality and length) was used as dependent variable in a model with the independent variables of proportion and spatial autocorrelation. Within the data dependency is present for sites at the same location. Therefore, mixed models were used with location nested in country as a random variable (Chen & Dunson, 2003). Depending on the characteristic of the dependent variable, generalized (categorical) or linear mixed models (numeric) were used from the package ‘lme4’ (Bates et al., 2015). For categorical dependent variables overdispersion, where the variance is greater than the mean, was checked. When data was not overdispersed or underdispersed a generalized linear mixed model with poisson distribution was used. In case of overdispersion, a generalized linear mixed model with negative binomial distribution from the package ‘MASS’ was used (Venables & Ripley, 2002). For linear mixed models, skewness and other assumptions of data was checked and if necessary, transformations were performed.

Models for habitat specialisation: For analysing the relation of habitat configuration, in terms of proportion (P) and spatial autocorrelation (H), with habitat specialisation, subsets of the dataset were made. This resulted in three subsets, namely for general (G), dune-specific (D) and marram-specific (M) species. These subsets were transformed to a species-abundance matrix with sampling sites as rows and species as columns. Extra columns were added of the species richness and the Shannon index per site. These diversity measures were calculated by the use of the package ‘vegan’ (Oksanen et al., 2019). The Shannon index (H’) is a diversity measure which takes species richness as the relative abundance of each species into account.

- , � = − �# ∗ ln (�#) #./

Where pi is the proportion of species ‘i’ and S is the species richness (Oksanen, 2019).

The same subsets and corresponding species-abundance matrices were made for data of Coleoptera and Arachnida separately. For these two groups abundance was used as ‘diversity measure’, because species richness is often one due to the additional sub-setting. As a single species per site has a Shannon index value of zero, this was not useful.

Models were performed for the three habitat specialisations (G, D and M) per diversity measure available. The choice of the mixed model-type and structure of fixed and random effects are identical as explained for the environmental variables (see before). However, now the diversity measure (species richness, Shannon’s diversity index or abundance) was used as dependent variable. The analyses on diversity were performed twice. First on the data of France and Belgium, and a second time on the data of France, Belgium and the United Kingdom. This was done to see whether adding data from England (UK) had a big effect on

the results. While the models on the environmental data were performed on all four countries, these models were done with data from three of the four countries in the study area (France, Belgium and England), due to delays in data acquisition for the Netherlands caused by COVID-19.

Each model in the analyses was done four times, with the fixed variables each time at a different scale (5, 10, 20 and 50 m). We made sure these four models were performed on the same dataset, meaning that we excluded the rows with missing data for any of the four scales. To choose the best scale, the model with the lowest AIC-value (Akaike Information Criterion) was selected (Wagenmakers & Farrell, 2004).

The fixed, independent variables in all the models represent a parabolic function for both the term of proportion (P) as spatial autocorrelation (H). To analyse possible interacting effects, increasing interaction terms of proportion and spatial autocorrelation were added to the model. All models started from the same fixed and random effect structure (bivariate quadratic polynomial):

Dependent variable ~ P + H + P*H + P2 + H2 + P*H2 + P2*H + (1|Country: Location)

The model selected on the lowest AIC-value was run a second time with all data available and for this model backward selection of the fixed factors, starting from the full model, was performed if this was necessary to find the best model in explaining the variation (elimination of highest interaction term if p > 0.15). For each model, predictions of the dependent variable were made over the range of the spatial independent variable(s). These predictions were plotted with addition of the raw data to visualise the trends of the models. All graphics were made using the package ‘ggplot2’ (Wickham, 2016).

The term of marram proportion will from now on often be addressed as ‘P’ and the term of spatial autocorrelation as ‘aggregation of marram’ or ‘H’.

4. Results

4.1. Data exploration

A general exploration of collected individuals per country for the different classes (Figure 7), indicates that a total of 12.973 individuals were collected in Belgium, France and the United Kingdom (UK) together. Most individuals were collected in Belgium (8.199), which is logical as more locations were sampled and a larger area was sampled while sweep netting (see Material & methods). Looking at the distribution over the classes, most are insects (phylum Arthropoda). Arachnida (phylum Arthropoda) are also relatively well represented in all three countries. In Belgium and France, the Gastropods (phylum Mollusca) also make up a relatively large part.

Figure 7: Exploration of collected individuals in the study area.

An overview of the distribution over different orders/suborders (Figure 8) shows that the most represented, in individual counts, are in descending counts the suborder Brachycera from the order Diptera (class Insecta), the order Coleoptera (class Insecta), the order Araneae (class Arachnida), the order Stylommatophora (class Gastropoda), the order Hymenoptera (class Insecta) and the order Hemiptera (class Insecta) (Figure 8, left). Looking at the counted species per order/suborder, the dominating orders stay quite the same. Only the order of Stylommatophora has a much lower species count, indicating a high abundance of the present species (Figure 8, right).

Figure 8: Distribution over the different orders/suborders. With on the left the counts in individuals and on the right in species.

Over the different countries, the major part of collected individuals/species are not bound to a dune or marram habitat, indicated as ‘general’ (Figure 9). A total of 510 (morpho)species were identified for Belgium, France and the UK. These 510 species were distributed over 421 general species (G), 62 dune- specific species (D) and 27 marram-specific species (M). In Belgium a higher abundance of marram-specific species is observed, but less species than in France. In the UK few habitat specialists were observed.

Figure 9: Overview of the distribution over habitat-specialisation in counted individuals (left) and counted species (right).

The marram-specific individuals/species present are mainly from the classes Arachnida and Insecta. Dune- specific species mainly from the class Insecta and from the classes Arachnida and Gastropoda (Figure 10).

Figure 10: Distribution of habitat specialisation over classes, with counted individuals on the left and counted species on the right.

Most marram-specific individuals are found in the orders Araneae, Coleoptera, Diptera (suborder Brachycera), Hemiptera and Pseudoscorpiones (Figure 11, left). When comparing with counted species (Figure 11, right), the marram-specific species are mainly from the orders Araneae, Coleoptera and Hemiptera. Dune-specific individuals/species are found more distributed over the different orders/suborders, but we notice a high proportion in the order Stylommatophora.

Figure 11: Distribution of habitat specialisation over orders/suborders, with counted individuals on the left and counted species on the right.

Exploration of the spatial configuration of marram shows that the relationship between aggregation of marram (H) and proportion of marram (P), at different scales, is in general positive (Figure 12). This is intuitively logic, because with increasing proportion of marram (P) on the same area, it will aggregate (H) more. A low P and H determine a very dynamic environment, while a higher P and H determine fixation of marram and decreasing dynamics. With increasing scale, the variability becomes larger (Figure 12), as the observations are more scattered. The difference between de countries is not very clear. However, we can

see that mostly the observations of France cluster together at a higher value of H. At the biggest scale of 50m (Figure 12), the observations of the Netherlands cluster at a lower H-value, France clusters at higher H-values, Belgium is somewhere in between. England is scattered at 20m, however is mostly at higher P- and H-values for the other scales.

Figure 12: The relationship between P and H at the different scales.

4.2. Models for the local environmental variables

The environmental variables considered are the number of inflorescences, vitality and the height of the central marram tussock. For the number of inflorescences and vitality a generalized linear mixed model with a Poisson distribution was used and for the variable of height a linear mixed model. Selection of the model best explaining the influence of the spatial configuration of marram on the environmental variable was done based on the lowest AIC-criteria. This resulted in selection of the model on 10m for number of inflorescences, 50m for vitality and 10m for height (Appendix 10.2.1).

4.2.1. The influence of the spatial configuration on marram inflorescences

Final model: Inflorescences ~ P10 + H10 + P10*H10 + (P10)2 + (H10)2 + P10*(H10)2 + (P10)2*H10 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 0.05481 0.86265 (H10)2 -0.42841 0.75720 P10 5.98143 0.00729 ** P10*H10 -12.61467 0.00100 ** H10 0.062945 0.62554 (P10)2*H10 5.94348 0.05148 . (P10)2 -5.03608 0.02681 * P10*(H10)2 5.94974 0.02299 * Table 2: Overview of the estimates and p-values from the model of marram inflorescences at 10m radius scale.

Figure 13: Predictions for the number of inflorescences of marram in function of marram proportion (left) and aggregation (right) at 10m radius scale.

In figure 13 we can only interpret the (near-)significant fixed effects, which are indicated in table 2. The significance of the linear (P) and quadratic term (P2) of proportion indicate that there is mainly an effect of marram proportion on the number of inflorescences, and a maximum is observed at intermediate values of P (Figure 13, left). A negative interaction between P and H is visible, so when one term increases it will lower the absolute effect of the other term (Figure 13). In addition, the positive interaction between P2 and H (/H2 and P) will cause a shift in the curve, where an increasing value of H (/P) makes the curve in function of proportion (/aggregation) flatter (/steeper) (Figure 13). The optimal spatial configuration of marram for maximizing the number of inflorescences is an intermediate value of P and a low value of H. This corresponds to an intermediate-dense, patchy configuration, associated with an intermediate dynamic environment in marram dunes.

4.2.2. The influence of the spatial configuration on marram vitality

Final model: Vitality ~ H50 + (1|Country: Location)

Fixed effect Estimate p-value (intercept) 1.24521 <2e-16 *** H50 -0.26777 0.0208 * Table 3: Overview of the estimates and p-values from the model of marram vitality at 50m radius scale.

Figure 14: Predictions for vitality of marram in function of marram aggregation.

Table 3 and Figure 14 show that vitality has a negative relationship with the aggregation of marram (H). The more aggregated, the lower the vitality. This corresponds to degeneration of marram associated with the fixation/stabilisation of marram dunes.

4.2.3. The influence of the spatial configuration on marram height

Final model: Height (length) ~ P10 + H10 + (P10)2 + (H10)2 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 58.975 <2e-16 *** (P10)2 -17.584 0.0147 * P10 20.666 0.0125 * (H10)2 20.057 0.0541 . H10 -25.235 0.0469 * / Table 4: Overview of the estimates and p-values from the model of marram height at 10m radius scale.

Figure 15: Predictions for marram height in function of marram proportion (left) and aggregation (right) at 10m radius scale.

Table 4 and Figure 15 show that both proportion and aggregation of marram have an effect on the height of marram grass, but no effect is observed for their interactions. The effect of proportion and aggregation respectively reaches a maximum and minimum around intermediate values. Based on these predictions, length is maximised around intermediate P-values and lower H-values. This corresponds to an intermediate- dense and patchy configuration, associated with an intermediate dynamic environment in marram dunes.

4.3. Models for habitat specialisation

As mentioned in the section of ‘Material and methods’, we ran the models for habitat specialisation twice; once with only data from France and Belgium, and once with addition of the data from the UK. Here, we will consider the results of the models from the three countries, as similar results were obtained and to give the complete picture, unless explicitly mentioned differently. The results of the analyses based only on France and Belgium, which are not shown here, can be found in the appendix (Appendix 10.2.2).

For each habitat specialisation (general, dune-, and marram-specific) we performed 4 models, each time with a different ‘diversity measure’ as dependent variable. We used a ‘linear mixed effect model’ for the models on ‘species richness’, ‘Shannon’s diversity index’ and the model on marram-specific Coleoptera, and a ‘generalized linear mixed effect model’ for the remaining models on Coleoptera and Arachnida abundance. The distribution of the latter models depended on overdispersion of the data, ‘negative binomial’ when overdispersed and ‘Poisson’ when not (Appendix 10.2.2).

4.3.1. The influence of the spatial configuration of marram grass on general invertebrate diversity

For the models of general invertebrate diversity, selection obtained the model on 50m for species richness, 50m for the Shannon’s diversity index, 10m for Coleoptera abundance and 50m for Arachnida abundance (Appendix 10.2.2.1.). a. General invertebrate species richness:

Final model: Species richness (G) ~ P50 + H50 + P50*H50 + (P50)2 + (H50)2 + P50*(H50)2 + (P50)2*H50 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 2.1213 0.000553 *** (H50)2 2.2976 0.255711 P50 6.4654 0.041671 * P50*H50 -2.3617 0.643458 H50 -0.5964 0.769972 (P50)2*H50 13.8857 0.036967 * (P50)2 -11.4638 0.018532 * P50*(H50)2 -7.7045 0.084055 . Table 5: Overview of the estimates and p-values from the model of general species richness at 50m radius scale.

Figure 16: Predictions for general species richness in function of marram proportion (left) and aggregation (right) at 50m radius scale.

In Figure 16 we can only interpret the (near-)significant fixed effects, which are indicated in table 5. There is mainly an effect of marram proportion on general species richness, and a maximum for general species richness is seen at P-values between 0.25-0.5 (Figure 16, left). The effect of proportion (/aggregation) is influenced by the (near-)significant, positive (/negative) interaction effects interaction between P2 and H (/H2 and P). This is causing a shift in the curve, where an increasing value of H (/P) makes the curve in function of proportion (/aggregation) flatter (/reversed) (Figure 13, left (/right)). Consequently, for general

species richness we could say that lower to intermediate P-values, with interaction of higher H-values are associated with overall higher general species richness. This corresponds to larger patches of marram in a less dynamic environment. b. Shannon’s diversity index for general invertebrates:

Final model: Shannon’s index (G) ~ P50 + H50 + P50*H50 + (P50)2 + (H50)2 + (P50)2*H50 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 0.9782 0.0171 * (H50)2 -0.1414 0.8288 P50 5.0316 0.0389 * P50*H50 -6.7995 0.0428 * H50 1.3366 0.1158 (P50)2*H50 8.0079 0.0698 . (P50)2 -6.7734 0.0433 * / Table 6: Overview of the estimates and p-values from the model of the Shannon's diversity index for general species at 50m radius scale.

Figure 17: Predictions for the Shannon's diversity index of general species in function of marram proportion.

Likewise, mainly proportion has an effect on the Shannon’s diversity index of general invertebrates and the effect is influenced by the aggregation of marram (Table 6). At low H-values a maximum for the Shannon’s diversity index in function of P is reached between 0.25-0.5. The effect of proportion on the Shannon index is influenced by the negative interactions with H causing a weakened effect of P with increasing H and by the near-significant interaction between H and P2 causing a widening of the curve. Consequently, the Shannon’s diversity index for general invertebrates is higher for more aggregated marram patches, and the lower H, the higher P needs to be for reaching the maximum. Corresponding to larger patches or more marram area when H is low. This first one is associated with a less dynamic environment and corresponds

more or less to the results we obtained for general species richness. The second one indicates that more dynamic environments also allow a high Shannon index for general diversity. c. General Coleoptera abundance:

Final model: Coleoptera abundance (G) ~ P10 + H10 + (H10)2 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 0.7767 0.03517 * H10 2.1317 0.06362 . P10 -0.7916 0.00295 ** (H10)2 -1.6963 0.08637 . Table 7: Overview of the estimates and p-values from the model of general Coleoptera abundance at 10m radius scale.

Figure 18: Predictions for general Coleoptera abundance in function of marram proportion (left) and aggregation (right) at 10m radius scale.

Table 7 and Figure 18 show that general Coleoptera abundance shows a negative relationship with proportion and a parabolic relationship with aggregation of marram with a maximum around 0.75, but no effect of their interaction is observed. General Coleoptera abundance is thus the highest at lower proportion and higher aggregation, corresponding to larger patches of marram surrounded by bare sand in a less dynamic environment.

d. General Arachnida abundance:

Final model: Arachnida abundance (G) ~ P50 + H50 + (P50)2 + (H50)2 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) -0.1177 0.8234 (P50)2 -2.3429 0.0699 . P50 1.8870 0.0770 . (H50)2 -2.2070 0.0436 * H50 2.6322 0.0665 . / Table 8: Overview of the estimates and p-values from the model of general Arachnida abundance at 50m radius scale.

Figure 19: Predictions for general Arachnida abundance in function of marram proportion (left) and aggregation (right) at 50m radius scale.

For general Arachnida abundance, Table 8 and Figure 19 indicate that both P and H have an effect, but the interaction effect is not withheld. The relationship with both spatial parameters is described by a parabolic function, with maxima in function of P between 0.25-0.5 values and in function of H at higher values (0.5- 0.75). This corresponds to an environment similar to previous results for general diversity and thus a less dynamic environment.

4.3.2. The influence of the spatial configuration of marram grass on dune-specific invertebrate diversity

For the models of dune-specific invertebrate diversity, the lowest AIC-value was obtained for the model on 5m for species richness, 5m for the Shannon’s diversity index, 10m for Coleoptera abundance and 10m for Arachnida abundance (Appendix 10.2.2.2).

a. Dune-specific invertebrate species richness:

Final model: Species richness (D) ~ P5 + H5 + P5*H5 + (P5)2 + (H5)2 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 0.8302 0.01680 * (P5)2 -0.5859 0.14281 P5 3.3705 6.14e-06 *** (H5)2 1.8362 0.06672 . H5 -2.0320 0.08350 . P5*H5 -2.8747 0.00417 ** Table 9: Overview of the estimates and p-values from the model of dune-specific species richness at 5m radius scale.

Figure 20: Predictions for dune-specific species richness in function of marram proportion (left) and aggregation (right) at 5m radius scale.

Table 9 and Figure 20 indicate that both proportion and aggregation of marram have an effect on dune- specific species richness, but the effect of P is more significant (H near-significant). There is also a clear effect of the negative interaction. On the left (Figure 20) we see that the negative interaction with increasing value of H causes a decrease of the positive effect in function of P. On the right (Figure 20) we see that an increasing value of P causes a more negative or steeper effect of H. Consequently, dune-specific species richness is maximized at higher values of P with lower values of H, but decreases at a higher rate with increasing H. This corresponds to a dense and very patchy configuration, which is associated with a less dynamic environment. b. Shannon’s diversity index for dune-specific invertebrates:

Final model: Shannon’s index (D) ~ P5 + H5 + P5*H5 + (H5)2 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 0.6730 0.033942 * (H5)2 1.6551 0.064274 . P5 2.8937 2.08e-05 *** P5*H5 -3.1245 0.000117 *** H5 -1.6231 0.129663 / Table 10: Overview of the estimates and p-values from the model of the Shannon's diversity index for dune-specific species at 5m radius scale.

Figure 21: Predictions for the Shannon's diversity index of dune-specific species in function of marram proportion (left) and aggregation (right) at 5m radius scale.

Table 10 and Figure 21 show similar results as for dune-specific species richness, where a negative interaction between P and H has an effect on the Shannon’s diversity index for dune-specific invertebrates. The Shannon’s diversity index increases with increasing proportion of marram (P), but it is strongly negative affected by increasing aggregation (H). Likewise, corresponding to a dense and very patchy configuration, which is associated with a less dynamic environment.

c. Dune-specific Coleoptera abundance:

Final model: Coleoptera abundance (D) ~ H10 + (1|Country: Location)

Fixed effect Estimate p-value (intercept) -1.0222 0.04336 * H10 1.8803 0.00237 ** Table 11: Overview of the estimates and p-values from the model for dune-specific Coleoptera abundance at 10m radius scale.

Figure 22: Predictions for dune-specific Coleoptera abundance in function of marram aggregation at 10m radius scale.

Table 11 and Figure 22 indicate that dune-specific Coleoptera abundance, in contrary with the previous results of dune-specific species, shows a positive relationship with the aggregation of marram (H). This corresponds to a lower dynamic environment. d. Dune-specific Arachnida abundance:

The generalized linear mixed model (with Poisson distribution) showed the best fit for the model on 10m radius scale in explaining the influence of the spatial configuration of marram on the dune-specific Arachnida abundance. No significant effect of proportion or aggregation of marram grass was obtained. We also ran the models from the other scales, as all the AIC-values lie close together (Appendix 10.2.2.2.). These models showed the same result, indicating that there is no clear effect of proportion or aggregation on the abundance of dune-specific Arachnida.

4.3.3. The influence of the spatial configuration of marram grass on marram-specific invertebrate diversity

For the models of marram-specific invertebrate diversity, the lowest AIC-value was obtained for the model 50m for the Shannon’s diversity index, 10m for Coleoptera abundance and 50m for Arachnida abundance (Appendix 10.2.2.3.). For the model on marram-specific species richness, the addition of the data from the UK caused disappearance of the effect of spatial configuration. Here, we will present the results merely based on the data of Belgium and France (Appendix 10.2.3.3.).

a. Marram-specific invertebrate species richness:

Final model: Species richness (M) ~ P5 + H5 + P5*H5 + (P5)2 + (H5)2 + (P50)2*H50 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 1.4059 3.67e-15 *** (H5)2 0.7640 0.2369 P5 3.1534 0.0475 * P5*H5 -3.6755 0.0644 . H5 -0.5974 0.3633 (P5)2*H5 3.8256 0.0672 . (P5)2 -3.3449 0.0582 . / Table 12: Overview of the estimates and p-values from the model for marram-specific species richness at 5m radius scale.

Figure 23: Predictions for marram-specific species richness in function of proportion at 5m radius scale.

Table 12 and Figure 23 show that marram-specific species richness (Belgium and France) is maximised at intermediate values of proportion but is negatively affected by increasing values of aggregation. The optimal spatial configuration for marram-specific species richness is an intermediate-dense, patchy configuration. This corresponds to an intermediate dynamic environment. b. Shannon’s diversity index for marram-specific invertebrates:

Final model: Shannon’s diversity index (M) ~ P50 + H50 + P50*H50 + (P50)2 + (H50)2 + P50*(H50)2 + (P50)2*H50 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 0.5616 0.14615 (H50)2 -3.5721 0.01680 * P50 -4.3824 0.05449 . P50*H50 2.2484 0.51445 H50 2.3629 0.09082 . (P50)2*H50 -13.2215 0.00925 ** (P50)2 8.0746 0.02645 * P50*(H50)2 7.2567 0.02410 * Table 13: Overview of the estimates and p-values from the model of the Shannon's diversity index for marram-specific species at 50m radius scale.

Figure 24: Predictions for the Shannon's diversity index of marram-specific species in function of marram proportion (left) and aggregation (right) at 50m radius scale.

Table 13 and Figure 24 indicate an effect of both proportion and aggregation of marram on the Shannon’s diversity index of marram-specific invertebrates. We can clearly see a strong influence of the negative interaction between P2 and H, causing the minimum (positive coefficient) in function of P at low H to shift towards a maximum (negative coefficient) in function of P with a high value of H (Figure 24, left). In contrary, we see a strong positive interaction between H2 and P causing the maximum in function of H with low P to shift towards a minimum in function of H with a high value of P (Figure 24, right). From the figure we can thus observe that for a lower P a higher H is needed to reach a higher Shannon’s diversity index, but for a higher P a lower H is resulting in a higher Shannon’s index. Consequently, it is clear that marram-specific diversity needs a certain area of marram grass present in their environment and for the persistence of marram, dynamic environments are necessary. We see the highest predicted index at higher P and low H, corresponding to a dense, but very patchy configuration of marram, associated with a less dynamic environment. However, the datapoints, indicate higher values for lower P and higher H.

c. Marram-specific Coleoptera abundance:

Final model: Coleoptera abundance (M) ~ P10 + H10 + P10*H10 + (P10)2 + (H10)2 + (P10)2*H10 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) -0.1828 0.5369 (H10)2 -0.1432 0.8638 P10 3.6245 0.0560 . P10*H10 -4.1329 0.1207 H10 0.8212 0.3519 (P10)2*H10 4.5792 0.1013 (P10)2 -4.2924 0.0454 * / Table 14: Overview of the estimates and p-values from the model of marram-specific Coleoptera abundance at 10m radius scale.

Figure 25: Predictions for marram-specific Coleoptera abundance in function of marram proportion at 10m radius scale.

In Figure 25 we can only interpret the (near-)significant terms displayed in Table 14. These indicate that there is only an effect of proportion and the response is characterised by a parabolic function, with a maximum of marram-specific Coleoptera abundance around intermediate proportion. This is associated with an intermediate dynamic environment. d. Marram-specific Arachnida abundance:

Final model: Arachnida abundance (M) ~ P50 + H50 + P50*H50 + (1|Country: Location)

Fixed effect Estimate p-value Fixed effect Estimate p-value (intercept) 1.9287 0.00516 ** H50 -1.8166 0.07462 . P50 -3.5630 0.04046 * P50*H50 4.6758 0.05181 . Table 15: Overview of the estimates and p-values from the model of marram-specific Arachnida abundance at 50m radius scale.

Figure 26: Predictions for marram-specific Arachnida abundance in function of marram proportion (left) and aggregation (right) at 50m radius scale.

Table 15 and Figure 26 show an effect of proportion and aggregation of marram grass on marram-specific Arachnida abundance and their interaction. At the left (Figure 26) we see that the negative slope in function of P with a low H shifts to a less negative slope with increasing values H, due to the positive interaction. On the right (Figure 26) we see that the negative slope in function of H with a low P shifts to a positive slope due to the positive interaction between P and H. In contrary of the result for Coleoptera abundance, we see that higher Arachnida abundances are reached for combinations of low P and H or high P and H. Highest Arachnida abundance are predicted at low P and low H, associated with a very dynamic environment.

5. Discussion

Anthropogenic activities, in addition to climate change, are putting a lot of pressure on ecosystems and their ecological communities. This results in the loss of ecosystem services and associated biodiversity. A consequence of human disturbances is, among other things, shifts in the spatial configuration of habitat patches. Such a shift is obvious in European marram dunes, where human disturbances are stabilising the originally dynamic coastal environment. Coastal ecosystems have gained in conservation importance due to the many ecosystem services they provide and the rare ecological communities they maintain. Changes in the dynamics and natural spatial configuration threaten those species which are evolutionarily adapted, through species sorting, to the extreme and dynamic environmental conditions. These specialist species rely on the associated patchy and sparse configuration of marram grass in a matrix of bare sand. Therefore, we wanted to assess how the spatial configuration of marram grass, evaluated by the proportion and aggregation on different scales, influences the diversity of aboveground, invertebrate species in relation to their habitat specialisation. Understanding how the associated invertebrate diversity responds to habitat configuration is important for the conservation of the invertebrate diversity as the ecosystem as a whole since invertebrate diversity is a good early-warning indicator of the qualitative status of European marram dunes.

5.1. A dynamic environment for higher quality marram grass

In a first analysis, we looked at the response of local environmental variables to the spatial configuration of marram grass, expected to reflect higher quality marram grass. The results showed that the local environmental variables of the number of inflorescences, vitality, and the height of marram grass in European dunes do respond to the spatial configuration of marram grass. We found that the number of inflorescences is maximized at intermediate proportions but is negatively affected by increased aggregation. The vitality of marram grass showed a negative relationship with increasing aggregation and the height is maximised at intermediate proportion and low or high aggregation. These results indicate that higher-quality marram is obtained in an intermediate-dense, but patchy configuration, associated with an intermediate dynamic environment.

These results are in accordance with known literature where Hope-Simpson & Jefferies (1966) pointed out that the number of inflorescences is correlated with sand-accretion and thus a dynamic environment, but an optimal burial rate is expected around 0.31m/year (Nolet et al., 2018). However, they also stated that it is not uncommon for high-quality marram patches to occur without or lower flowering, suggesting that the number of inflorescences is not the most reliable indicator of marram quality. Besides, it is important to consider that for A. arenaria clonal spread is more important than seed reproduction (Huiskes, 1979). On the height of marram grass, results in literature are variable and depend on the speed

of sand deposition. When sand deposition is large on a short time interval, results indicate that marram grass will allocate resources to the root biomass, while gradual sand deposition is shown to have a positive relationship with marram height (Boudreau & Houle, 2001; Brown & Zinnert, 2018). The response we found of decreasing vitality with increased aggregation is also affirmed by literature, but a time-lag is present in the development of a negative plant-soil feedback (Van der Stoel et al., 2002). The initial development of dynamic dunes with A. arenaria needs high sand dynamics and once settled there will be a positive feedback at the local scale. A decrease in sand dynamics will at first allow an increase in vegetation cover due to a more even distribution of the shoots (Willis, 1989). Eventually, the invasion of pathogenic and parasitic micro-organisms in the root environment will have a negative effect on the vitality and degeneration starts. Marram grass needs continuous sand accumulation to stay vital and escape the colonization of the root environment (Van der Putten et al., 1988; Nolet et al., 2018).

5.2. Does the species-area relationship hold in the stabilisation of dunes?

Our first hypothesis states that dune stabilisation positively influences general, aboveground invertebrate diversity in accordance with the species-area relationship. Dune stabilisation is reflected by a higher proportion and aggregation of marram grass as a result of decreasing sand dynamics (Willis, 1989). This is expected to allow the increasing presence of generalist species, according to the species-area relationship (Preston, 1962; Attum et al., 2006).

The results of the response of general diversity to the spatial configuration of marram grass showed mainly an effect of proportion. All the diversity measures were maximised at lower to intermediate proportions. The lower the proportion the more positive the effect of increasing aggregation. Consequently, lower proportions in combination with increasing aggregation had an overall positive effect on general diversity. This indicates that general species are benefitting from larger, isolated marram patches, associated with a less dynamic and isolated environment. These results support the species-area relationship in such a way that general diversity is higher in more aggregated and thus larger patches. However, we see a contradictory response of decreasing general diversity with an increasing proportion of marram. Consequently, we do not have complete support for the hypothesis that general diversity increases with the stabilisation of marram dunes.

Despite the species-area relationship being universal in ecological research, results show that species richness is mostly correlated to area-based increases in habitat heterogeneity rather than to area per se. This puts the ‘SLOSS’ (Single Large or Several Small) thinking in perspective, as research has shown that small reserves can have higher heterogeneity than large reserves (Báldi, 2008). In the results, we found that higher proportions of marram have a negative effect on general diversity. This could be a consequence of the fact that increasing stabilisation of marram dunes reduces the heterogeneity in the area, and a

homogenous environment of low-quality marram grass cannot support high diversity. In further steps of stabilisation, where successional pioneers start to settle (Eldred & Maun, 1982), we could again expect an increase in habitat heterogeneity and thus general diversity. We did find a higher general diversity in more aggregated marram patches, indicating a less dynamic environment to allow higher aggregation. This could be explained by the fact that an increasing patch size enhances internal microhabitat heterogeneity (Bonte et al., 2002). Our finding that general diversity is higher in more isolated patches could be aligned with the findings of Horvath et al. (2013). They looked at species richness of spiders in sandy grasslands in Hungary and found a significant negative relationship between species richness and the grassland size, while the ratio of specialist species increases with patch size. They also found that the ratio of specialist species decreased, while the ratio of generalist species increased with increasing isolation. This can be explained by the fact that generalist species have higher probabilities of colonizing isolated fragments due to more source habitat in the surrounding and a broader tolerance to different environmental conditions (Horvath et al., 2013). Consequently, low-quality stabilised marram dunes are probably too homogenous to support a high general diversity, while other configurations benefit specialist species, the latter outcompeting generalists in their optimal habitat conditions (Nordberg & Schwarzkopf, 2018).

5.3. Does high-quality marram grass benefit marram-specific invertebrate species?

The second hypothesis states that dynamic marram-dunes in optimal conditions, reflected by high vitality, number of inflorescences, and length, support a higher presence of marram-specific invertebrate species.

The results of marram-specific responses to the spatial configuration of marram grass differed depending on the diversity measure considered. When looking at species richness and Coleoptera abundance we recognise the same response as the local environmental variables, indicating a higher marram-specific richness in the presence of higher quality marram grass. Based on these results we could say we found support for our hypothesis and in literature. If a disturbance increases the quality of a habitat feature used by a specialist, we expect the specialist to outcompete the generalist (Nordberg & Schwarzkopf, 2018). However, when looking at the Shannon’s diversity index, where abundance is accounted for, we see a better response to low proportion with higher aggregation or high proportion with lower aggregation. This indicates that a certain amount of marram grass is necessary for marram-specific invertebrates. Marram- specific Arachnida prefer a more dynamic environment with low proportion and aggregation, possibly reflecting the importance of dispersal ability. For example, the spider Clubiona frisia is a nocturnal hunter in the open sand matrix but hides during the day in sac-like retreats in the vegetation (Almquist, 1970). These variable results suggest that marram-specific species respond differently depending on other important life-history traits, like dispersal ability, not considered here.

In the analyses on the response of marram-specific diversity, we found some variation. The results presented were all based on data from Belgium, France, and the United Kingdom (UK), except the model on species richness for marram-specific diversity. For this model, we presented the results based on data from Belgium and France. The addition of data from the UK annulled the observed effect found in the model without data from the UK. We could explain this by the fact that little specialist species, and definitely marram-specific specialist species, were observed in the samples from the UK, and if present there was mostly only one species. Another explanation could be due to the way we rescaled the H-values, as the data exploration showed that the samples of the UK cluster more together at higher P- and H-values. Due to the rescaling a H-value of 0.3 in the UK is not the same as in France. Rescaling over all the countries together would thus be a better approach. The Shannon’s diversity index for marram-specific diversity showed that both proportion and aggregation have an effect and strongly interact with each other. Based on the predictions we could say that a dense and patchy configuration is optimal, however, the datapoints suggest more aggregated patches. This highlights the danger of extrapolation with model predictions and we need to be cautious with the interpretation. Consequently, to have a more reliable response it would be better to increase the sample size or to take other life-history traits, like dispersal ability, into consideration.

5.4. Does the response to spatial configuration depend on habitat specialisation?

The third hypothesis, which summarises our main interest, states that generalist and specialist species respond differently to the spatial configuration of marram grass.

The results of general diversity and marram-specific diversity have been addressed above. The results of the response of dune-specific invertebrate diversity showed a significant, positive response to the proportion of marram grass, which is strongly negatively affected by increasing aggregation of marram grass. These results were obtained for species richness and the Shannon’s diversity index, indicating that dune-specific species prefer a dense but patchy configuration of marram grass. This is associated with a less dynamic environment, where marram grass has enough possibility to grow, but some dynamics keep the patchy configuration intact. The results thus show that there is indeed a different response on the spatial configuration of marram grass between generalist species, dune specialists, and marram specialists and this confirms our hypothesis. In terms of proportion, we did however expect a different response. While general species prefer lower to intermediate proportion, dune specialists prefer high proportions of marram. For marram specialists, the results are more variable. In terms of aggregation, our hypothesis is confirmed. Generalist species benefit from higher aggregation, while this mostly has a negative effect on specialist species.

The explanation and link to known literature for the response of general diversity is already addressed above. For the observed response of dune-specific invertebrate diversity, we also find affirmation in

literature. Coastal dune ecosystems are a fine-scaled mosaic of suitable habitat patches in a matrix of bare sand. This spatial configuration provides habitat heterogeneity, arising from the interplay between wind dynamics and vegetation succession (Wouters et al., 2012). The associated fauna relies on this heterogenic character in a dynamic environment, where different types of habitat are used for thermoregulation, foraging, breeding, etc. (Maes & Bonte, 2006). Due to their restricted habitat, dispersal abilities are usually selected against in specialist species, as the probability of reaching suitable habitat is low. Therefore, they need a highly connected habitat configuration to reach viable populations (Horvath et al., 2013; Büchi & Vuilleumier, 2014).

5.5. Additional observations and findings

From the initial data exploration and the response of the general diversity to the spatial configuration of marram grass, we found that general species are dominantly present in marram dunes, despite the extreme environmental conditions. This could indicate the already high presence of anthropogenic disturbances as the presence of a spill-over effect. This effect occurs when species from abundant surrounding source populations end up in sink populations in neighbouring habitat, in this case, marram dunes (Blitzer et al., 2012).

In the analyses of the response of different habitat specialists to the spatial configuration of marram grass, we obtained some contradictory or unexpected responses. In the response of dune-specific Coleoptera abundance, there was a positive effect of increasing aggregation with Coleoptera abundance. This is contradictory to what we found for dune-specific species richness and the Shannon’s diversity index, where increasing aggregation of marram grass negatively affected diversity. This could be explained by the limited data available for the specific subset of dune-specific Coleoptera as the presence of one sample with a notable high abundance. This results in a lower reliability of the observed response and would be solved by increasing sample size. However, Comor et al. (2008) found that dune vegetation is of high importance for beetle abundance, as it provides shelter, a refuge against predators, food resources, etc. Another remarkable finding was that dune-specific Arachnida did not show a response to the spatial configuration of marram grass. This could be due to the low sample size or the fact that there is high variability in life- history traits within dune-specific Arachnida (Schirmel et al., 2012). There exists a high in foraging strategy of spiders, where some spiders are more active hunters and use the open habitat space (Lycosidae), while others are more sedentary, passive hunters and use their webs to catch prey (example; Neoscona adianta).

5.6. Importance and implications of the results

Our results tested and showed that species with different habitat affinities, generalist and specialist, have a different response to the spatial configuration of marram grass in terms of habitat size, isolation, and

heterogeneity. This also suggests that associated invertebrates can be used as early warning indicators of dune quality and stabilisation. Therefore, they should be considered separately when assessing conservation priorities. Our results provide support for the urgent need of conservation action in dynamic dune ecosystems to preserve the specialised associated invertebrate communities. Anthropogenic disturbances are modifying spatial patterns and dynamics in marram dunes, which has negative consequences for the associated diversity and increases the threat of functional homogenisation by generalist species (Van der Biest et al., 2017). Rejuvenation of relict marram, when subjected to increasing sand deposition, is known (Willis, 1989), but the long traditions of dune stabilisation throughout Europe will demand increasing efforts to undo because of the process of hysteresis (Provoost et al., 2011). In addition, the longer inaction, the more the effect of climate change will enforce dune stabilisation. The positive feedback of climate change is reflected in increased rainfall, rising temperatures, eutrophication, which all enhance vegetation growth (Osswald et al., 2019). Our results show that the incorporation of life- history traits in conservation management is crucial and necessary to rescue rare and endangered ecological communities. This is reflected in the variation of scale best explaining the responses. The goal is towards a dynamic coastal management, where habitat heterogeneity is sustained through the self- organisational character of marram dunes.

5.7. How do we proceed?

This thesis project shows that the role of life-history traits is becoming unneglectable in nature conservation. In future research, there is a need for more integration of multiple life-history traits to apply efficient conservation management. I would like to point out some limitations of this research, which may be accounted for in future research to improve our understanding of the responses of ecological communities to their environment.

In the identification of the collected individuals, we sometimes used morphospecies. These show a similar morphology but are challenging to identify to species level (Hymenoptera, Diptera, etc.). This has implications for the analyses since this is not an accurate representation of species richness and hides information on species-specific life-history traits. This brings us to another comment; we only made the distinction between general, dune-specific, and marram-specific. However, the species attributed to ‘general’ did not always represent generalist species. We expect this overestimates the proportion of generalist species and generalisation can blur hidden responses. Literature states that carabid beetles (Carabidae) and spiders (Araneae) are useful indicator-taxa to analyse shifts in terrestrial ecosystems (Schirmel et al., 2012). However, we looked separately to the response of Coleoptera and Arachnida abundance to the spatial configuration of marram grass since the subsets already resulted in very low sample sizes and thus increased the error on model predictions.

We only looked at the random effect of location nested within country in our models, but it is known that weather conditions and distance from the sea can have a big effect on the presence of aboveground invertebrates and if possible, should be considered as well (Bonte et al., 2002; Bruls et al., 2016). Lastly, as mentioned in the section of Material & Methods, the values for aggregation were rescaled on the maximum per country. However, this results in values between 0 and 1 which correspond to intervals when compared over the different countries, increasing the variation on the results obtained. In future research, it would be advised to rescale on the total maximum.

6. Conclusion

In conclusion, we did find supporting results for our overarching hypothesis that there is a different response between generalist and specialist species to the spatial configuration of marram grass. Our observations indicate that dune- and marram-specific diversity rely on the dynamic environmental conditions present in marram dunes. The sand dynamics create different patterns in spatial configuration which increase the habitat heterogeneity and meets the specific needs of specialist species, from more dynamic to less dynamic.

However, against our expectation we observed that dune stabilisation is not fully correlated with increased general diversity, as the need for habitat heterogeneity overrides the importance of area. The results for the specialist species showed mostly a reliance on the dynamic configuration in marram dunes, but variation in responses is present. This suggests that other life-history traits, besides niche breadth, can play a crucial role and should be taken into consideration for efficient conservation. This shows that sand dynamic, which can alter in intensity, is important in creating different patterns. Further research will be necessary to improve our understanding in the determining life-history traits of different specialist species.

All things considered; we can say that specialist species bound to the dynamic environment of (marram) dunes are severely threatened by dune stabilisation. A dynamic coastal management for the destabilization of dunes and restoration of sand dynamics is a must. Our findings are potentially useful for designing coastal management with optimal conservation strategies for the associated invertebrate communities. It has become clear that assessments in biodiversity conservation need to make a distinction of species depending on their habitat-specialisation. The conservation goal will not only protect us from flooding, it will safeguard the rare and vulnerable associated ecological communities and prevent a further functional homogenisation by generalist species and associated biodiversity loss.

7. Summaries

7.1. English summary

Anthropogenic activities in addition of climate change are putting a lot of pressure on ecosystems and their ecological communities. Many ecosystems are losing their ability to provide ecosystem services and associated biodiversity. One of the consequences of human disturbances is a shift in the spatial configuration of habitat patches. This shift is obvious in European marram dunes, where human disturbances have been and are stabilising the originally dynamic coastal environment through years of additional plantation of marram grass and the reduction of sand dynamics due to infrastructures. This dune stabilisation is resulting in larger areas of aggregated marram grass because of the loss in sand dynamics.

Marram dunes are important for coastal protection against a rising sea level caused by anthropogenic global warming. Besides, they harbour specific ecological communities, including specialist species which are evolutionarily adapted to the dynamic and extreme environmental conditions of the coast. The downside of being a specialist, however, is a low tolerance towards disturbance. Consequently, the change in the dynamics and spatial configuration in marram dunes threatens those species which rely on the associated patchy configuration of marram grass in a matrix of bare sand. Therefore, marram dunes have gained in conservation importance. The increased stabilisation is expected to be beneficial for generalist species. They outcompete specialist species in disturbed environments and add to the global problem of functional homogenisation, a severe loss in ecological value.

For efficient conservation and restoration of marram dunes and ensuring all its ecosystem services, including the provision of habitat for invertebrate communities, we need a thorough understanding of how these ecological communities respond to their environment. Therefore, we assessed how the spatial configuration of marram grass, evaluated by the proportion and aggregation on different scales, influenced the diversity of aboveground, invertebrate species in relation to their habitat specialisation. These insights are important towards the conservation of the associated invertebrate diversity as the ecosystem as a whole.

The aim of this thesis project is thus to look at the response of species with different habitat affinities to the spatial configuration of marram grass. First of all, this will enable a more efficient restoration and conservation of dynamic marram dunes and their biodiversity. Secondly, it will allow the use of invertebrate diversity as early-warning indicators on the status of European marram dunes. To look at the different responses, we made a distinction between general species (no habitat-specialisation), dune-specific species (bound to dune ecosystems), and marram-specific species (bound to marram dune ecosystems). We

hypothesized that specialist and generalist species show a different response to the spatial configuration of marram grass. General diversity is expected to increase with dune stabilisation, while specialists are benefitting from a more dynamic environment.

We found that higher-quality marram grass is obtained in intermediate dynamic environments, with an intermediate-dense and patchy configuration of marram grass. This corresponded to the response of marram-specific species richness, indicating that vital and high-quality marram grass does increase the presence of these specialist species. However, other measures of marram-specific diversity showed more variability. This could suggest that other life-history traits, for example, dispersal ability, could also be important in explaining the influence of the spatial configuration of marram grass on their presence.

For dune-specific diversity, we found that richness and diversity were maximized in a dense but very patchy configuration of marram grass. This indicates their preference for a heterogenous, mosaic-like environment, where they use both the marram and bare sand for foraging, breeding, etc. Dune-specialists seem to be strongly affected by increasing aggregation or loss of marram patches. This shows that they rely on some dynamics in the environment for the maintenance of a patchy habitat configuration.

The results for the general invertebrate diversity don’t fully support our hypothesis. We hypothesized an increasing general diversity with dune stabilisation. However, we found higher diversity for aggregated but isolated marram patches. This could be explained by the more important effect of habitat heterogeneity rather than area per se on species richness. Stabilised dunes, before further succession, are probably too homogenous as it becomes a large area of low-quality marram grass. The link with more isolated patches was linked to a higher dispersal ability in generalist species, and a broader tolerance for a variety of habitat types.

The observed results do confirm our main research question that there is a different response to the spatial configuration of marram grass between generalist and specialist species. Specialist species adapted to the dynamic environments of coastal ecosystems rely on sand dynamics for an optimal spatial configuration, from high to lower dynamics. While generalist species are more likely to occur in patches where competition with specialist species is low, but the environment is not too homogenous.

These results support that dune- and marram-specific species are threatened by dune stabilisation, as they rely on different spatial configuration patterns resulting from sand dynamics. Therefore, coastal management needs to restore the dynamics in marram dunes to ensure all ecosystem service-delivery. With these insights on the response of species with a different habitat affinity to the spatial configuration in marram dunes, we showed the importance of the distinction of habitat-specialisation in conservation

strategies. This will support more efficient conservation action and will safeguard these unique ecosystems and their biodiversity.

7.2. Nederlandstalige samenvatting

Ecosystemen wereldwijd staan onder grote druk door de dominante aanwezigheid van de mens. De continue uitputting van ecosystemen leidt tot een vermindering in ecosysteemdiensten en sterke achteruitgang van biodiversiteit. Eén van de gevolgen van het menselijk ingrijpen is een wijziging in de ruimtelijke configuratie van geschikt habitat. Hiervan zijn helmduinen in Europa een duidelijk voorbeeld. De mens heeft jarenlang geprobeerd de dynamische omgeving aan de kust te overmeesteren door aanplantingen en de bouw van harde infrastructuren die moeten dienen als barrière met de zee. Dit heeft geleid tot de stabilisatie van de oorspronkelijk dynamische helmduinen, waarbij de mozaïek-configuratie van helmgras is overgegaan naar grote, aaneengesloten gebieden helmgras van lage kwaliteit.

Met de toenemende dreiging van klimaatsopwarming en daarmee gepaarde zeespiegelstijging, neemt de rol van helmduinen in kustbescherming sterk toe. Door het proces van stabilisatie verliest dit ecosysteem echter zijn resiliënte karakter, wat van belang is in kustbescherming. Ecosystemen aan de kust krijgen steeds meer belangstelling in natuurbescherming. Naast hun belangrijke rol in kustbescherming, bieden helmduinen ook nog een thuis aan heel specifieke fauna. De extreme en dynamische omgevingscondities in helmduinen heeft doorheen de jaren soorten gefilterd op bepaalde kenmerken. Deze specialistische soorten zijn sterk aangepast aan hun bepaalde omgeving, maar dit maakt hen meteen ook kwetsbaarder voor verstoringen. Bijgevolg bedreigt de stabilisatie van helmduinen de habitat-specifieke gemeenschappen van ongewervelden en zorgt voor een vervanging door meer algemene soorten, welke een lagere ecologische waarde hebben.

Voor een efficiënt herstel en het behoud van helmduinen en de instandhouding van de geleverde ecosysteemdiensten, inclusief habitat-voorziening voor ongewervelde gemeenschappen, is er nood aan inzichten over hoe deze ecologische gemeenschappen reageren op hun omgeving. In deze masterproef werd daarom onderzocht wat effect is van de ruimtelijke configuratie van helmgras op de diversiteit van bovengronds, ongewervelden in relatie tot hun habitat-specificiteit. De ruimtelijke configuratie werd hier geëvalueerd op basis van bedekking en aggregatie van helmgras op een bepaalde schaal. We hebben een onderscheid gemaakt tussen algemene soorten (geen habitat-specificiteit), duin-specifieke soorten en helmgras-specifieke soorten. We stelden de hypothese dat specialistische en generalistische soorten verschillend reageren op de ruimtelijke configuratie van helmgras. Daarnaast verwachtten we ook dat algemene diversiteit zal toenemen met toenemende duinstabilisatie, terwijl de specialistische soorten voorkeur hebben voor meer dynamische omgevingen.

De bekomen resultaten hebben aangetoond dat de kwaliteit van helmgras hoger is in gematigd dynamische omgevingen, overeenstemmend met een gemiddelde bedekking en gefragmenteerd helmgrasgebied. Dit kwam overeen met de optimale reactie van helmgras-specifieke soortenrijkdom. Dit duidt op een mogelijk

verband tussen kwaliteit van helmgras en de aanwezigheid van helmgras-specifieke soorten. Variërende resultaten werden echter bekomen voor andere diversiteitsmetingen van helmgras-specifieke soorten. Dit zou kunnen duiden op het belang van andere life-history traits naast habitat affiniteit, bvb. migratie- capaciteit, voor de aanwezigheid van helmgras-specifieke ongewervelden.

De resultaten voor duin-specifieke diversiteit tonen een duidelijk positief effect van bedekking in combinatie met lage aggregatie. Dit duidt op een voorkeur voor een heterogene, mozaïek-achtige configuratie van helmgras, waarvoor een bepaalde zanddynamiek noodzakelijk is. Een sterk negatief effect van aggregatie duidt op de afhankelijkehdi van deze specialisten van zowel de vegetatie als de onbedekte zandgrond voor voortplanting, foerageren, etc.

In onze hypothese hadden we gesteld dat algemene diversiteit zou toenemen met de stabilisatie van helmduinen, maar onze resultaten ondersteunen deze hypothese niet volledig. Voor de algemene diversiteit vonden we een optimale ruimtelijke configuratie van aaneengesloten, maar geïsoleerde helmgras-patches. Dit valt te verklaren door de nood aan voldoende habitat-heterogeniteit. De toenemende duinstabilisatie zorgt voor een meer homogene omgeving van aaneengesloten helmgras van lagere kwaliteit. Het positief effect van isolatie op algemene diversiteit valt te linken aan de betere migratie- capaciteit van algemene soorten en hogere tolerantie voor een groter aanbod aan habitat-types.

De resultaten die aantonen dat er een verschil is in reactie op de ruimtelijke configuratie van helmgras tussen algemene en specialistische soorten, bevestigen onze onderzoeksvraag. De habitat-specifieke soorten zijn evolutionair aangepast aan de dynamische omgeving. Deze soorten zijn zelfs afhankelijk van de zanddynamiek voor het behoud van een optimale configuratie. Algemene soorten komen eerder voor waar de omgevingscondities stabieler zijn en competitie met specialistische soorten lager. Ze hebben echter wel nood aan bepaalde heterogeniteit, welke afneemt bij toenemende bedekking en aggregatie van helmgras (stabilisatie).

Dat de stabilisatie van helmduinen een bedreiging vormt voor duin- en helmgras-specifieke diversiteit, wordt door de resultaten ondersteunt. Hun optimale habitat configuratie verdwijnt namelijk als gevolg van de dalende zanddynamiek. Bijgevolg is het van groot belang dat het kustmanagement en de natuurbescherming zich focust op het herstellen van zanddynamieken in helmduinen. Dit zal niet alleen de resiliëntie van helmduinen ten opzichte van klimaatsverandering verhogen, maar ook het habitat voor kust- specifieke soorten waarborgen. Deze inzichten tonen aan dat het van belang is om een onderscheid te maken in habitat-specialisatie voor biodiversiteitsbehoud. Het ondersteunt een efficiënter natuurbehoud van deze unieke ecosystemen en hun biodiversiteit.

7.3. Laymen summary

The worldwide degradation of natural ecosystems is resulting in a reduction of ecosystem services and associated biodiversity. One consequence of human disturbances is the alteration in the spatial configuration of suitable habitat patches. The spatial configuration is defined as a certain pattern of habitat- patches in an area. In European marram dunes, a human-caused stabilisation process is present, which negatively affects the quality of marram grass. This stabilisation process causes a severe shift in the spatial configuration from a sparse, patchy to a large, aggregated configuration of marram grass. Marram dunes provide important ecosystem services, like coastal protection, and harbour specific ecological communities with high conservation value. These ecological communities include species that are specifically adapted to the extreme environmental conditions of the coast. This habitat-specialisation, however, makes them extremely vulnerable to change in their natural habitat, as is the case with dune stabilisation. With the loss of specialist species and the stabilisation of the environmental conditions in marram dunes, general species take the opportunity and invade these disturbed environments. For efficient restoration and conservation of marram dunes and ensuring the ecosystem services, including maintaining specific diversity, we need a thorough understanding of how these ecological communities respond to their environment.

Therefore, we assessed how the spatial configuration of marram grass influenced the diversity of aboveground, invertebrate species in relation to their habitat-specialisation. The observed results confirm that there is a different response to the spatial configuration of marram grass between specialist and generalist species. Specialist species, adapted to the dynamic environments of coastal ecosystems, rely on sand dynamics for an optimal spatial configuration, from high to lower dynamics. While generalist species are more likely to occur in patches where competition with specialist species is low, but the environment is not too homogenous or low-quality.

These results support that dune- and marram-specific species are threatened by dune stabilisation, as they rely on different spatial configuration patterns resulting from sand dynamics. Therefore, coastal management needs to restore the dynamics in marram dunes to ensure ecosystem service delivery, including the maintenance of habitat for the associated ecological communities. With these insights on the response of species with a different habitat affinity to the spatial configuration in marram dunes, we will be able to assess much quicker what the status of our coastal ecosystems is. This will allow more efficient conservation action and safeguard these unique ecosystems and their biodiversity.

8. Acknowledgement

I would like to thank all the people who contributed to this thesis project. First of all, I want to thank my supervisor, professor dr. Dries Bonte, for guiding me through the development of this thesis project and providing insightful feedback. I also want to thank my co-supervisor, dr. Martijn Vandegehuchte, for the critical and instructive feedback. I want to thank my scientific tutor, Ruben Van De Walle, for all the help during the different steps of this thesis, for safely driving us to the sample locations in England, the barbecues, etc.

Secondly, I want to thank all the people involved in the data acquisition. I want to thank Ruben Van De Walle, Alexander Larter, Melanie Gillings, and all other people from the ENDURE-project for their help. I also want to thank Pauline Vanhauwere, Gillis Sanctobin and Laurian Van Maldeghem for data acquisition and identification work in previous years. Also a lot of gratitude for others who helped with the identification work: professor dr. Dries Bonte for the identification of the Arachnida, Ruben Van De Walle for the identification of the Hemiptera, Fons Verheyde for the identification of the Hymenoptera and Pieter Vantieghem for the identification of the Diptera and many more and his help with my identification struggles. I want to thank Hans Matheve for the acquisition and calculations of the spatial data and Ruben Van De Walle for the environmental data.

Lastly, I would like to thank my family and friends in supporting me through this challenging and unique period and pepping me up when needed.

9. References

Almquist, S. (1970). Thermal tolerances and preferences of some dune-living spiders. Oikos. 21: 230-235. Attum, O., Eason, P., Cobbs, G., & Baha El Din, M. (2006). Response of a desert lizard community to habitat degradation: do ideas about habitat specialists/generalists hold? Biological Conservation. 133: 52-62. Auffret, A. G., Plue, J., & Cousins, A. O. (2015). The spatial and temporal components of functional connectivity in fragmented landscapes. AMBIO. 44(Suppl. 1): S51-S59. Báldi, A. (2008). Habitat heterogeneity overrides the species-area relationship. Journal of Biogeography. 35: 675-681. Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., & Silliman, B.R. (2011). The value of estuarine and coastal ecosystem services. Ecological Monographs. 81(2): 169-193. Barnosky, A.D., Matzke, N., Tomiya, S., Wogan, G.O.U., Swartz, B., Quental, T.B., Marshall, C., McGuire, J.L., Lindsey, E.L., Maguire, K.C., Mersey, B., & Ferrer, E.A. (2011). Review: Has the Earth’s sixth mass extinction already arrived? NATURE. 471: 51-57. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2015). Fitting Linear Mixed-Effects Models using lme4. Journal of Statistical Software. 67(1): 1-48. Bellard, C., Berelsmeier, C., Leadley, P., Thuiller, W., & Courdchamp, F. (2012). Impacts of climate change on the future of biodiversity. Ecology Letters. 15(4): 365-377. Blaum, N., Seymour, C., Rossmanith, E., Schwager, M., & Jeltsch, F. (2009). Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodiversity Conservation. 18: 1187-1199. Blitzer, E.J., Dormann, C.F., Holzschuh, A., Klein, A., Ran, T.A., & Tscharntke, T. (2012). Spillover of functionally important organisms between managed and natural habitats. Agric. Ecosyst. Environ. 146: 34-43. Bonnell, T.R., Ghai, R.R., Goldberg, T.L., Sengupta, R., & Chapman, C.A. (2018). Spatial configuration becomes more important with increasing habitat loss: a simulation study of environmentally- transmitted parasites. Landscape Ecology. 33(8): 1259-1272. Bonte, D., Baert, L., & Maelfait, J.P. (2002). Spider assemblage structure and stability in a heterogenous coastal dune system (Belgium). The Journal of Arachnology. 30(2): 331-343. Borsje, B.W., van Wesenbeeck, B.K., Dekker, F., Paalvast, P., Bouma, T.J., van Katwijk, M.M., & de Vries, M.B. (2011). How ecological engineering can serve in coastal protection. Ecological engineering. 37: 113-122. Boudreau, S. & Houle, G. (2001). The Ammophila decline: A field experiment on the effects of mineral nutrition. Ecoscience. 8(3): 392-398. Brown, J.K. & Zinnert, J.C. (2018). Mechanisms of surviving burial: Dune grass interspecific differences drive resource allocation after sand deposition. Ecosphere. 9(3): e02162. 10.1002/ecs2.2162. Bruls, A., Hermens, J., Huizing, E., Tanson, M., & Verstraeten, L. (2016). Factors influencing dune growth and the vitality of dune building Ammophila arenaria. Büchi, L. & Vuilleumier, S. (2014). Coexistence of specialist and generalist species is shaped by dispersal and environmental factors. The American Naturalist. 183(5): 612-624. Cardinale, B.J., Duffy J.E., Gonzalez, A., Hooper, D.U., Perrings, C., Venail, P., Narwani, A., Mace, G.M., Tilman, D., Wardle, D.A., Kinzig, A.P., Daily, G.C., Loreau, M., Grace, J.B., Larigauderie, A.,

Srivastava, D.S., & Naeem, S. (2012). Review: Biodiversity loss and its impact on humanity. NATURE. 486(7401): 59-67. Ceballos, G., Ehrlich, P.R., Barnosky, A.D., Garcia, A., Pringle, R.B., & Palmer, T.M. (2015). Accelerated modern human-induced species losses: Entering the sixth mass extinction. Science advances. 1(5). Chen, Z. & Dunson, D.B. (2003). Random effects selection in Linear Mixed Models. Biometrics. 59: 762- 769. Clavel, J., Julliard, R., & Devictor, V. (2011). Worldwide decline of specialist species: toward a global function homogenization? Frontiers in Ecology and the Environment. 9(4): 222-228. Comor, V., Orgeas, J., Ponel, P., Rolando, C., & Delettre, Y.R. (2008). Impact of anthropogenic disturbances on beetle communities of French Mediterranean coastal dunes. Biodiversity Conservation. 17: 1837-1852. Conrad, K.F., Warren, M.S., Fox, R., Parsons, M.S., & Woiwod, I.P. (2006). Rapid declines of common, widespread British moths provide evidence of an insect biodiversity crisis. Biological conservation. 132: 279-291. Dawson T.P., Jackson, S.T., House, J.I., Prentice, I.C., & Mace, G.M. (2011). Beyond predictions: Biodiversity Conservation in a Changing Climate. Science, 332(6025): 53-58. De la Peña, E., Bonte, D., & Moens, M. (2009). Evidence of population differentiation in the dune grass Ammophila arenaria and its associated root-feeding nematodes. Plant Soil. 324: 307-316. De Paoli, H., van der Heide, T., van den Berg, A., Silliman, B.R., Herman, P.M.J., & van de Koppel, J. (2017). Behavioral self-organization underlies the resilience of a coastal ecosystem. PNAS. 114(30): 8035- 8040. Desender, K., & Baert, L. (1995). Carabid beetles as bio-indicators in Belgian coastal dunes: a long term monitoring project. Entomologie. 65: 35-54. Diaz, S., Fargione, J., Chapin III, F.S., & Tilman, D. (2006). Biodiversity loss threatens human well-being. PLOS BIOLOGY. 4(8): 1300-1305. Dupre, C. & Ehrlen, J. (2002). Habitat configuration, species traits and plant distributions. Journal of Ecology. 90: 796-805. Eldred, R.A. & Maun, M.A. (1982). A multivariate approach to the problem of decline in vigour of Ammophila. Can. J. Bot. 60: 1371-1380. Fortin, M.J. & Dale, M.R.T. (2009) Chapter 6: Spatial Autocorrelation in The SAGE Handbook of Spatial Analyses (89-104). SAGE Publications Ltd London. Gaston, K.J. (1991). The magnitude of global insect species richness. Conservation Biology. 5(3): 283-296. Gerlach, J. & Samways, M. (2013). Terrestrial invertebrates as bioindicators: an overview of available taxonomic groups. Journal of Insect Conservation. 17: 831-850. Gonçalves -Souza, T., Romero, G.Q., & Cottenie, K. (2014). Metacommunity versus biogeography: A case study of two groups of neotropical vegetation-dwelling arthropods. PloS ONE. 9(12): 1-20. Goodenough, A.E., Smith, A.L., Stubbs, H., Williams, R., & Hart, A.G. (2012). Observer Variability in Measuring Animal Biometrics and Fluctuating Asymmetry When Using Digital Analysis of Photographs. Annales Zoologici Fennici. 49(1–2): 81–92. Granek, E.F., Polasky, S., Kappel, C.V., Reed, D.J., Stoms, D.M., Koch, E.W., Kennedy, C.J., Cramer, L.A., Hacker, S.D., Barbier, E.B., Aswani, S., Ruckelshaus, M., Perillo, G.M.E., Silliman, B.R., Muthiga, N., Bael, D., & Wolanski, E. (2010). Ecosystem Services as a common language for coastal ecosystem- based management. Conservation Biology, 24(1): 207-216. Hanski, I. (1999). Metapopulation Ecology. Oxford University Press, New York.

Hooper, D.U., Chapin, F.S., Ewel, III J.J, Hector, A., Inchausti, P., Lavorel, S., Lawton, J.H., Lodge, D.M., Loreau, M., Naeem, S., Schmid, B., Setälä, H., Symstad, A.J., Vandermeer, J., & Wardle, D.A. (2005). Effects of Biodiversity on Ecosystem Functioning: A consensus of current knowledge. Ecological Monographs, 75(1): 3-35. Hope-Simpson, J.F. & Jefferies, R.L. (1966). Observations relating to vigour and debility in marram grass (Ammophila arenaria (L.) Link). Journal of Ecology. 54(1): 271-274. Horváth, R., Magura, T., Szinetár, C., Eichardt, J., & Tóthmérész, B. (2013). Large and least isolated fragments preserve habitat specialist spiders best in dry sandy grasslands in Hungary. Biodiversity Conservation. 22:2139-2150. Howe, M.A., Knight, G.T., & Clee, C. (2010). The importance of coastal sand dunes for terrestrial invertebrates in Wales and the UK, with particular reference to aculeate Hymenoptera (bees, wasps & ants). J. Coastal Conservation. 14: 91-102. Hutchinson, G. E. (1957). Concluding remarks. Cold Spring Harbor Symposium on Quantitative Biology. 22: 415-427. Huiskes, A.H.L. (1979). Ammophila arenaria (L.) Link (Psamma Arenaria (L.) Roem. et Schult.: Calamgrostis arenaria (L.) Roth). Journal of Ecology. 67(1): 363-382. Interreg2seas. Retrieved from https://www.interreg2seas.eu/en/content/about-programme. IPBES (2019): Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental. Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 pages. Jordan, F. (2009). Keystone species and food webs. Philisophical Transactions of the Royal Rociety B- Biological Sciences. 364(1524): 1733-1741. Kabos, S. & Csillag, F. (2002). The analysis of spatial association on a regular lattice by join-count statistics without the assumption of first-order heterogeneity. Computers & Geosciences. 28: 901-910. Kadmon, R. & Allouche, O. (2007). Integrating the effects of area, isolation, and habitat heterogeneity on species diversity: a unification of island biogeography and niche theory. The American Naturalist. 170(3): 443- 454. Keijsers, J.G.S., Giardino, A., Poortinga, A., Mulder, J.P.M., Riksen, M.J.P.M., Santinelli, G. (2015). Adaptation strategies to maintain dunes as flexible coastal flood defense in The Netherlands. Mitig. Adapt. Strateg. Glob. Change. 20: 913-928. Lambeck, R.J. (1997). Focal species: A multi-species umbrella for nature conservation. Conservation Biology. 11(4): 849-856. Levins, R. (1969). Some demographic and genetic consequences of environmental heterogeneity for biological control. Bull. Entomol. Soc. Am. 15, 237-240. Liquete, C., Piroddi, C., Drakou, E.G., Gurney, L., Katsanevakis, S., Charef, A., Egoh, B. (2013). Current status and future prospects for the assessment of marine and coastal ecosystem services: a systematic review. PloS ONE, 8(7): e67737. Loke, L.H.L., Chisholm, R.A., & Todd, P.A. (2019). Effects of habitat area and spatial configuration on biodiversity in an experimental intertidal community. Ecology. 100(8): e02757.10.1002/ecy.2757. MacArthur, R. H., and Wilson E. O. (1967) The theory of island biogeography. Princeton university Press, Princeton, NJ.

Maes, D. & Bonte, D. (2006). Using distribution patterns of five threatened invertebrates in a highly fragmented dune landscape to develop a multispecies conservation approach. Biological Conservation. 133: 490-499. Maes, D., Ghesquiere, A., Logie, M., & Bonte, D. (2006). Habitat use and mobility of two threatened coastal dune insects: implications for conservation. Journal of Insect Conservation. 10: 105-115. Massol, F., Dubart, M., Calcagno, V., Cazelles, K., Jacquet, C., Kéfi, S., & Gravel, D. (2017). Island biogeography of food webs. Advances in Ecological Research. 56: 183-262. Mills, L.S., Soulé, M.E., & Doak, D.F. (1993). The keystone-species concept in ecology and conservation. Bioscience. 43(4): 219-224. Moles, A.T., Warton, D.I., Warman, L., Swenson, N.G., Laffan, S.W., Zanne, A.E., Pitman, A., Hemmings, F.A., & Leishman, M.R. (2009). Global patterns in plant height. Journal of Ecology. 97: 923-932. Natura 2000. Retrieved from http://natura2000.eea.europa.eu/. Nolet, C., van Puijenbroek, M., Suomalainen, J., Limpens, J., & Riksen, M. (2018). UAV-imaging to model growth response of marram grass to sand burial: Implications for coastal dune development. Aeolian Research. 31: 50-61. Nordberg, E.J. & Schwarzkopf, L. (2018). Reduced competition may allow generalist species to benefit from habitat homogenization. Journal of Applied Ecology. 56: 305-318. Ockinger, E., Schweigen, O., Crist, T.O., Debinski, D.M., Krauss, J., Kuussaai, M., Petersen J.D., Pöyry, J., Settele, J., Summerville, K.S., & Bommarco, R. (2010). Life-history traits predict species responses to habitat area and isolation: a cross-continental synthesis. Ecology Letters. 13: 969-979. Oksanen, J. (2019). Vegan: ecological diversity. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, H.H., Szoecs, E., & Wagner, H. (2019). Vegan: Community Ecology Package. R package version 2.5-6. Oppenheimer, M., B.C. Glavovic, J. Hinkel, R. van de Wal, A.K. Magnan, A.Abd-Elgawad, R. Cai, M. Cifuentes-Jara, R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari. (2019). Sea Level Rise and Implications for Low-lying Islands, Coasts and Communities. In: IPPCC Special Report on the Ocean and Cryosphere in a Changing Climate. In press. Osswald, F., Dolch, T., & Reise, K. (2019). Remobilizing stabilized island dunes for keeping up with sea level rise? Journal of Coastal Conservation. 23: 675-687. Portman, M.E., Esteves, L.S., Le, X.Q., Khan, A.Z. (2012). Improving integration for integrated coastal zone management: an eight country study. Science of the Total Environment. 439: 194-201. Preston, F.W. (1962). The canonical distribution of commonness and rarity: Part I. Ecology. 43(2): 185-215. Provoost, S., Jones, M.L.M., & Edmondson, S.E. (2011). Changes in landscape and vegetation of coastal dunes in northwest Europe: a review. Journal of Coastal Conservation. 15: 207-226. R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Rietkerk, M., Dekker, S.C., De Ruiter, P.C. & van de Koppel, J. (2008). Regular Pattern Formation in Real Ecosystems. Trends in Ecology and Evolution. 23(3): 169-175. Rochelle-Newall, E., Klein, R.J.T., Nicholls, R.J., Barrett, K., Behrendt, H., Bresser, T.H.M., Cieslak, A., de Bruin, E.F.L.M., Edwards, T., Herman, P.M.J., Laane, R.P.W.M., Ledoux, L., Lindeboom, H., Lise, W., Moncheva, S., Moschella, P.S., Stive, M.J.F., & Vermaat, J.E. (2005). Group report: Global change and the european coast – climate change and economic development. In Managing European Coasts: Past, Present, and Future (239-254).

Schirmel, J., Blindow, I., & Buchholz, S. (2012). Life-history trait and functional diversity patterns of ground beetles and spiders along a coastal heathland successional gradient. Basic and Applied Ecology. 13: 606-614. Schulze, E.D., and Mooney, H.A. (1994). Biodiversity and Ecosystem Function. Chapter Keystone Species (W.J. Bond). 99: 237-253. Publisher: Springer, Berling, Heidelberg. Doi: 10.1007/978-3-642-58001- 7. Sun, X., Zhao, L., Zhao, D., Huo, Y., Tan, W. (2020). Keystone species can be identified based on motif centrality. Ecological indicators. 110: 105877. Tews, J., Brose, U., Grimm, V., Tielbörger, K., Wichmann, M.C., Schwager, M., & Jeltsch, F. (2004). Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. Journal of Biogeography. 31: 79-92. Tol, R.S.J., Klein, R.J.T., & Nicholls, R.J. (2008). Towards successful adaption to SLR along Europe’s coast. Journal of Coastal Research. 24(2): 432-442. Van Bergen, E., Dallas, T., DiLeo, M. F., Kahilainen, A., Mattila A. L. K., Luoto, M., Saastamoinen, M. (2020). The effect of summer drought on the predictability of local extinctions in a butterfly metapopulation. Conservation Biology. Doi: 10.1101/863795. Van der Biest, K., De Nocker, L., Provoost, S., Boerema, A., Staes, J., & Meire, P. (2017). Dune dynamics safeguard ecosystem services. Ocean & Coastal Management. 149: 148-158. Van der Plas, F., Janzen, T., Ordonez, A., Fokkema, W., Reinders, J., Etienne, R.S., & Olff, H. (2015). A new modeling approach estimates the relative importance of different community assembly processes. Ecology. 96(6): 1502-1515. Van der Putten, W.H., van Dijk, C., & Troelstra, S.R. (1988). Biotic soil factors affecting the growth and development of Ammophila arenaria. Oecologia. 76(2): 313-320. Van der Stoel, C.D., Van der Putten, W.H., & Duyts, H. (2002). Development of a negative plant-soil feedback in the expansion zone of the clonal grass Ammophila arenaria following root formation and nematode colonization. Journal of Ecology. 90: 978-988. Van Maldeghem, L. (2019). The invertebrate diversity of marram dunes (Unpublished master’s dissertation). Ghent University, Research Group Terrestrial Ecology, Belgium. Van Slobbe, E., de Vriend, H.J., Aarninkhof, S., Lulofs, K., de Vries, M., Dircike, P. (2013). Building with Nature: in search of resilient storm surge protection strategies. Nat. Hazards. 66: 1461-1480. Venables, W.N. & Ripley, B.D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. Villard, M.A. & Metzger, J.P. (2014). Beyond the fragmentation debate: a conceptual model to predict when habitat configuration really matters. Journal of Applied Ecology. 51: 309-318. Wagenmakers, E.J. & Farrell, S. (2004). AIC model selection using Akaike weights. Psychonomic Bulletin & Review. 11: 192-196. Wickham, H. (2016). Ggplot2: Elegant Graphics for Data Analysis. Springer-Verslag New York. Willis, A.J. (1989). Coastal sand dunes as biological systems. Proceedings of the Royal Society of Edinburgh. 96B: 17-36. Wilson, E.O. (1987). The little things that run the world (the importance and conservation of invertebrates). Conservation Biology. 1(4): 344-346. Wilson, E.O. & Willis, A.J. (1975). Applied biogeography. 522-534.

Wouters, B., Nijssen, M., Geerling, G., Van Kleef, H., Remke, E., & Verberk, W. (2012). The effects of shifting vegetation mosaics on habitat suitability for coastal dune fauna – a case study on sand lizards (Lacerta agilis). Journal of Coastal Conservation. 16: 89-99. Zimmerman, B.L. & Bierregaard, R.O. (1986). Relevance of the equilibrium theory of island biogeography and species-area relationship to conservation with a case from Amazonia. Journal of Biogeography. 13(2): 133-143.

10. Appendix

10.1. Classification (* dune-specific/ ** marram-specific) Kingdom Animalia Phylum Arthropoda Class Arachnida (Super)order Family (Morpho)species Author Acariformes Acariformes sp.1 (Zakhvatkin, 1952) Acariformes sp.2 (Zakhvatkin, 1952) Acariformes sp.3 (Zakhvatkin, 1952) Acariformes sp.4 (Zakhvatkin, 1952) Acariformes sp.5 (Zakhvatkin, 1952) Acariformes sp.6 (Zakhvatkin, 1952) Acariformes sp.7 (Zakhvatkin, 1952) Trombidiidae Trombidiidae sp. 1 (Leach, 1815) Trombidiidae sp. 2 (Leach, 1815) Araneae Agelenidae Tegenaria campestris (Koch, 1834) Araneidae Araneidae sp. (Simon, 1895) Cercidia prominens (Westring, 1851) Mangora acalypha (Walckenaer, 1802) Neoscona adianta * (Walckenaer, 1802) Zygiella sp. (Pickard-Cambridge, 1902) Zygiella x-notata (Clerck, 1757) Clubionidae Clubiona comta (Koch, 1839) Clubiona frisia ** (Wunderlich&Schuett, 1995) Clubiona sp. (Latreille, 1804) Clubiona subtilis * (Koch, 1867) Corinnidae Phrurolithus festivus (Koch, 1835) Gnaphosidae Drassodes sp. (Westring, 1851) Haplodrassus signifer (Koch, 1839) Micaria dives (Lucas, 1846) Zelotes pedestris * (Koch, 1866) Zelotes serotinus * (Koch, 1866) Linyphiidae Baryphyma maritimum ** (Crocker & Parker, 1970) Bathyphantes gracilis (Blackwall, 1841) Bolyphantes alticeps (Sundevall, 1833) Ceratinopsis romana ** (Cambridge, 1872) Entelecara erythropus (Westring, 1851) Erigone arctica (White, 1852) Erigone sp. (Audouin, 1826) Floronia bucculenta (Clerck, 1757)

Lepthyphantes sp. (Menge, 1866) Maso gallicus (Simon, 1894) Metopobactrus prominulus (Cambridge, 1872) Neriene clathrata (Sundevall, 1830) Neriene peltata (Wider, 1834) Oedothorax apicatus (Blackwall, 1850) Oedothorax retusus (Westring, 1851) Ostearius melanopygius (Cambridge, 1879) Pocadicnemis juncea (Locket & Millidge, 1953) Stemonyphantes lineatus (Linnaeus, 1758) Tenuiphantes tenuis (Blackwall, 1852) Liocranidae Agroeca cuprea * (Menge, 1873) Lycosidae Arctosa perita * (Latreille, 1799) Piratula latitans (Blackwall, 1841) Xerolycosa miniata * (Koch, 1834) Philodromidae Philodromus fallax ** (Sundevall, 1833) Philodromus sp. (Walckenaer, 1826) Thanatus striatus * (Koch, 1845) Tibellus maritimus (Menge, 1875) Tibellus sp. (Simon, 1875) Salticidae Attulus saltator * (Cambridge, 1868) Euophrys frontalis (Walckenaer, 1802) Marpissa nivoyi ** (Lucas, 1846) Myrmarachne formicaria (De Geer, 1778) Synageles venator ** (Lucas, 1836) Tetragnathidae Tetragnatha extensa (Linnaeus, 1758) Theridiidae Anelosimus vittatus (Koch, 1836) Enoplognatha ovata (Clerck, 1757) Enoplognatha sp. (Pavesi, 1880) Robertus lividus (Blackwall, 1836) Steatoda sp. (Sundevall, 1833) Theridion bimaculatum (Linnaeus, 1767) Theridion sp. (Walckenaer, 1805) Thomisidae Ozyptila sp. (Simon, 1864) Xysticus sabulosus * (Hahn, 1832) Xysticus sp. (Koch, 1835) Ixodida Ixodida sp. (Leach, 1815) Pseudoscorpiones Pseudoscorpiones indet. (de Geer, 1778) Dactylochelifer latreillii ** (Leach, 1817) Opiliones Opiliones sp. (Sundevall, 1833) Phalangiidae Mitopus morio (Fabricius, 1799) Phalangium opilio (Linnaeus, 1758)

Class Collembola Collembola sp. (Lubbock, 1870) Class Diplopoda Julida Julidae Allajulus nitidus (Verhoeff, 1891) Cylindroiulus latestriatus * (Curtis, 1845) Ommatoiulus sabulosus (Linnaeus, 1758)

Class Insecta Blattodea Blattelidae Capraiellus panzeri (Stephens, 1835) Ectobius pallidus (Olivier, 1789) Coleoptera Coleoptera indet. (Linnaeus, 1758) Anthicidae Anthicus antherinus (Linnaeus, 1760) Anthicus bimaculatus. * (Illiger, 1801) Cyclodinus constrictus * (Curtis, 1838) Notoxus monoceros (Linnaeus, 1760) Cantharidae Cantharis lateralis (Linnaeus, 1758) Rhagonycha fulva (Scopoli, 1763) Carabidae Amara lucida * (Duftschmid, 1812) Amara spreta ** (Dejean, 1831) Bembidion quadripustulatum (Audinet-Serville, 1821) Bembidion sp. (Latreille, 1802) Bembidion varium (Olivier, 1795) Bradycellus verbasci (Duftschmid, 1812) Broscus cephalotes * (Linnaeus, 1758) Calathus erratus * (Sahlberg, 1827) Calathus mollis ** (Marsham, 1802) Demetrias atricapillus (Linnaeus, 1758) Demetrias imperialis (Germar, 1824) Demetrias monostigma ** (Samouelle, 1819) Dicheirotrichus gustavii (Crotch, 1781) Dromius agilis (Fabricius, 1787) Microlestes maurus (Sturm, 1827) Notiophilus biguttatus (Fabricius, 1779) Paradromius linearis ** (Olivier, 1795) Platyderus depressus (Audinet-Serville, 1821) Pterostichus spec. (Bonelli, 1810) Trechus quadristriatus * (Schrank, 1781) Chrysomelidae Aphthona euphorbiae (Schrank, 1781) Bruchus rufipes (Herbst, 1783) Chaetocnema sp. (Stephens, 1831) Crepidodera plutus (Latreille, 1804) Crioceris asparagi (Linnaeus, 1758)

Cryptocephalus pusillus (Fabricius, 1777) Leptinotarsa decemlineata (Say, 1824) Longitarsus jacobaeae (Waterhouse, 1858) Longitarsus luridus (Scopoli, 1763) Longitarsus ochroleucus (Marsham, 1802) Neocrepidodera ferruginea (Scopoli, 1763) Oulema gallaeciana ** (Heyden, 1879) Oulema melanopus (Linnaeus, 1758) Psylliodes chrysocephala (Linnaeus, 1758) Sermylassa halensis (Linnaeus, 1767) Chrysomelidae sp. (Latreille, 1802) Coccinellidae Coccinellidae indet. (Latreille, 1807) Anatis ocellata (Linnaeus, 1758) Coccidula rufa (Herbst, 1783) Coccinella septempunctata (Linnaeus, 1758) Coccinella undecimpunctata (Linnaeus, 1758) Harmonia axyridis (Pallas, 1773) Hippodamia variegata (Goeze, 1777) Platynaspis luteorubra (Goeze, 1777) Propylea quatuordecimpunctata (Linnaeus, 1758) Psyllobora vigintiduopunctata (Linnaeus, 1758) Rhyzobius chrysomeloides (Herbst, 1792) Tytthaspis sedecimpunctata (Linnaeus, 1760) Cryptophagidae Micrambe ulicis (Stephens, 1830) Curculionidae Baris cuprirostris (Fabricius, 1787) Ceutorhynchus obstrictus (Marsham, 1802) Glocianus spec. (Reitter, 1916) Larinus planus (Fabricius, 1793) Magdalis memnonia (Gyllenhal, 1837) Microplontus rugulosus (Herbst, 1795) Nedyus quadrimaculatus (Linnaeus, 1758) Otiorhynchus atroapterus ** (De Geer, 1775) Otiorhynchus ovatus * (Linnaeus, 1758) Philopedon plagiatum ** (Schaller, 1783) Pissodes castaneus (De Geer, 1775) Sitona hispidulus (Fabricius, 1777) Sitona lepidus (Gyllenhal, 1834) Sitona lineatus (Linnaeus, 1758) Snuitkever 10 Elateridae Agriotes sputator (Linnaeus, 1758) Histeridae Hypocaccus metallicus * (Herbst, 1791) Latridiidae Cortinicara gibbosa (Herbst, 1793)

Melyridae Anthocomus rufus (Herbst, 1784) Clanoptilus marginellus (Olivier, 1790) Clanoptilus strangulatus (Abeille, 1885) Psilothrix viridicoeruleus (Geoffroy, 1785) Nitidulidae Epuraea sp. (Erichson, 1843) Meligethes atramentarius (Förster, 1849) Meligethes sp. 1 (Stephens, 1829) Meligethes sp. 2 (Stephens, 1829) Nitidulidae sp. (Latreille, 1802) Oedemeridae Oedemera lurida/virescens Oedemera nobilis (Scopoli, 1763) Phalacridae Olibrus sp. (Erichson, 1845) Stilbus sp. (Seidlitz, 1872) Scarabaeidae Aegialia arenaria ** (Fabricius, 1787) Phyllopertha horticola (Linnaeus, 1758) Staphylinidae Aleochara bipustulata (Linnaeus, 1760) Aleochara sp. 1 (Gravenhorst, 1802) Aleochara sp. 2 (Gravenhorst, 1802) Aleochara sp. 3 (Gravenhorst, 1802) Aleocharinae indet. 1 (Fleming, 1821) Leptacinus sp. (Erichson, 1839) Ocypus ophthalmicus (Scopoli, 1763) Philonthus marginatus (Müller, 1764) Platystethus alutaceus (Thomson, 1861) Platystethus nodifrons (Mannerheim, 1830) Stenus sp. (Latreille, 1797) Tachyporus hypnorum (Fabricius, 1775) Tachyporus nitidulus (Fabricius, 1781) Staphylinidae sp. (Lameere, 1900) Xantholinus tricolor (Fabricius, 1787) Tenebrionidae Cteniopus sulphureus * (Linnaeus, 1758) Lagria hirta (Linnaeus, 1758) Melanimon tibialis * (Fabricius, 1781) Phylan gibbus * (Fabricius, 1775) Tribolium destructor (Uyttenboogaart, 1933) Xanthomus pallidus * (Curtis, 1830) Tenebrionidae sp. (Latreille, 1802) Dermaptera Forficulidae Forficula auricularia (Linnaeus, 1758) Diptera (Brachycera) Calyptrata indet. Diptera indet. (Linnaeus, 1758) Agromyzidae Agromyzidae indet. (Fallén, 1810) Pseudonapomyza indet. (Hendel, 1920)

Anthomyiidae Anthomyiidae indet. (Latreille, 1829) Anthomyzidae Anthomyza gracilis (Fallén, 1823) Anthomyza spec. (Fallen, 1810) Asilidae Philonicus albiceps * (Meigen, 1820) Asteiidae Asteia concinna (Meigen, 1830) Calliphoridae Calliphora vicina (Robineau-Desvoidy, 1830) Calliphoridae indet. (Hough, 1899) Lucilia indet. (Robineau-Desvoidy, 1830) Canacidae Tethina grisea * (Fallén, 1823) Tethina illota * (Haliday, 1838) Xanthocanace ranula * (Loew, 1874) Chamaemyiidae Chamaemyia indet. (Meigen, 1803) Leucopis indet. (Meigen, 1803) Chloropidae Chloropidae indet. (Rondani, 1856) Chlorops calceatus (Meigen, 1830) Colliniella meijerei (Duda, 1932) Conioscinella indet. (Duda, 1929) Dicraeus fennicus (Duda, 1933) Eutropha fulvifrons * (Haliday, 1833) Meromyza indet. (Meigen, 1830) Meromyza nigriventris (Macquart, 1835) Meromyza pratorum ** (Meigen, 1830) Meromyza rohdendorfi (Fedoseeva, 1974) Oscinella nitidissima (Meigen, 1838) Oscinella spec. (Becker, 1909) Pseudopachychaeta approximatonervis (Zetterstedt, 1848) Thaumatomyia glabra (Meigen, 1830) Thaumatomyia notata (Meigen, 1830) Dolichopodidae Dolichopodidae indet. (Latreille, 1809) Hercostomus chrysozygos (Wiedemann, 1817) Medetera indet. (Fischer von Waldheim, 1819) Medetera truncorum (Meigen, 1824) Sciapus maritimus * (Becker, 1918) Sciapus wiedemanni (Fallén, 1823) Drosophilidae Drosophilidae indet. (Róndani, 1856) Dryomyzidae Dryomyza indet. (Fallén, 1820) Ephydridae Ephydridae indet. (Zetterstedt, 1837) Coenia palustris (Fallén, 1823) Hecamede albicans * (Meigen, 1830) Hydrellia maura (Meigen, 1838) Paracoenia fumosa (Stenhammar, 1844)

Philygria flavipes (Fallén, 1823) Philygria punctatonervosa * (Fallén, 1813) Scatella indet. (Robineau-Desvoidy, 1830) Scatella tenuicosta (Collin, 1930) Fanniidae Fanniidae indet. (Schnabl & Dziedzicki, 1911) Helcomyzidae Helcomyza ustulata * (Curtis, 1825) Heleomyzidae Heleomyzidae indet. (Westwood, 1840) Hybotidae Hybotidae indet. (Macquart, 1823) Chersodromia incana * (Walker, 1851) Platypalpus indet. (Macquart, 1827) Tachydromia indet. (Meigen, 1803) Lauxaniidae Lauxaniidae indet. (Macquart, 1835) Calliopum spec. (Strand, 1928) Meiosimyza indet. (Hendel, 1925) Minettia indet. (Robineau-Desvoidy, 1830) Sapromyza quadricincta (Becker, 1895) Sapromyza quadripunctata (Linnaeus, 1767) Sapromyzosoma quadripunctata (Linnaeus, 1767) Lonchaeidae Lonchaeidae indet. (Loew, 1861) Lonchopteridae Lonchoptera bifurcata (Fallén, 1810) Lonchoptera lutea (Panzer, 1809) Lonchopteridae indet. (Curtis, 1839) Milichiidae Phyllomyza indet. (Fallén, 1810) Muscidae Muscidae indet. (Latreille, 1802) Neomyia indet. (Walker, 1859) Opomyzidae Geomyza indet. (Fallén, 1810) Geomyza tripunctata (Fallén, 1823) Opomyza indet. (Fallén, 1820) Opomyza germinationis (Linnaeus, 1758) Opomyza florum (Fabricius, 1794) Opomyza punctata (Haliday, 1833) Phoridae Phoridae indet. (Curtis, 1833) Piophilidae Piophilidae indet. (Macquart, 1835) Pipunculidae Eudorylas indet. (Aczél, 1940) Pipunculidae indet. (Walker, 1834) Tomosvaryella indet. (Aczél, 1939) Rhinophoridae Rhinophoridae sp. (Robineau-Desvoidy, 1863) Sarcophagidae Sarcophagidae indet. (Macquart, 1834) Scathophagidae Scathophaga litorea * (Fallén, 1819) Scathophaga stercoraria (Linnaeus, 1758) Spaziphora hydromyzina (Fallén, 1819) Sciomyzidae Coremacera marginata (Fabricius, 1775)

Dichetophora obliterata (Fabricius, 1805) Euthycera fumigata (Scopoli, 1763) Pherbellia cinerella (Fallén, 1820) Sepsidae Saltella sphondylii (Schrank, 1803) Sepsidae indet. (Walker, 1833) Sepsis indet. (Fallén, 1810) Sepsis cynipsea (Linnaeus, 1758) Sepsis thoracica (Robineau-Desvoidy, 1830) Themira minor (Haliday, 1833) Sphaeroceridae Sphaeroceridae indet. (Macquart, 1835) Chaetopodella scutellaris (Haliday, 1836) Leptocera fontinalis (Fallén, 1823) Leptocera nigra (Olivier, 1813) Opacifrons humida (Haliday, 1836) Pteremis fenestralis (Fallén, 1820) Rachispoda lutosoidea (Duda, 1938) Spelobia clunipes (Meigen, 1830) Spelobia nana (Rondani, 1880) Stratiomyidae Chloromyia formosa (Scopoli, 1763) Nemotelus notatus (Zetterstedt, 1842) Pachygaster atra (Panzer, 1798) Syrphidae Eupeodes corollae (Fabricius, 1794) Eupeodes luniger (Meigen, 1822) Episyrphus balteatus (De Geer, 1776) Melanostoma mellarium (Meigen, 1822) Neoascia tenur (Harris, 1780) Platycheirus indet. (Le Peletier & Serville, 1828) Sphaerophoria indet. (Le Peletier & Serville, 1828) Tachinidae Siphona indet. (Meigen, 1803) Tachinidae indet. (Robineau-Desvoidy, 1830) Tephritidae Campiglossa plantaginis * (Haliday, 1833) Campiglossa producta (Loew, 1844) Dioxyna bidentis (Robineau-Desvoidy, 1830) Ensina sonchi (Linnaeus, 1767) Rhagoletis batava (Hering, 1938) Sphenella marginata (Fallén, 1814) Tephritis neesii (Meigen, 1830) Tephritis praecox (Loew, 1844) Tephritis formosa (Loew, 1844) Tephritis vespertina (Loew, 1844) Trupanea stellata (Fuessly, 1775) Urophora quadrifasciata (Meigen, 1826)

Therevidae Acanthiophilus helianthi (Rossi, 1790) Acrosathe annulata * (Fabricius, 1805) Thereva unica (Harris, 1780) Thereva indet. (Latreille, 1796) Thereva cinifera * (Meigen, 1830) Thereva nobilitata * (Fabricius, 1775) Therevidae sp. (Newman, 1834) Ulidiidae Herina palustris * (Meigen, 1826) Tetanops myopina * (Fallén, 1820) Diptera (Nematocera) Nematocera sp. (Latreille, 1825) Anisopodidae Sylvicola spec. (Harris, 1780) Bibionidae Dilophus febrilis (Linnaeus, 1758) Cecidomyiidae Cecidomyiidae sp. (Newman, 1834) Ceratopogonidae Ceratopogonidae sp. (Newman, 1834) Culicoides indet. (Latreille, 1809) Chironomidae Chironomidae sp. (Newman, 1834) Culicidae Culicidae indet. (Meigen, 1818) Limoniidae Limoniidae sp. (Rondani, 1856) Dicranomyia spec. (Stephens, 1829) Symplecta stictica (Meigen, 1818) Mycetophilidae Mycetophilidae sp. (Newman, 1834) Psychodidae Psychodidae sp. (Newman, 1834) Scatopsidae Scatopsidae indet. (Newman, 1834) Sciaridae Sciaridae sp. (Billberg, 1820) Simuliidae Simuliidae indet. (Newman, 1834) Tipulidae Nigrotipula nigra (Linnaeus, 1758) Nephrotoma cornicina * (Linnaeus, 1758) Nephrotoma quadristriata (Schummel, 1833) Tipula indet. (Linnaeus, 1758) Hemiptera (Auchenorrhyncha) cicade 25 cicade 31 cicade 50 cicade 52 Aphrophoridae Neophilaenus lineatus (Linnaeus, 1758) Philaenus spumarius (Linnaeus, 1758) Cicadellidae Cicadellidae indet. (Latreille, 1802) Conosanus obsoletus (Kirschbaum, 1858) Doratura impudica * (Horvath, 1897) Eupterix filicum (Newman, 1853) Euscelis incisus (Kirschbaum, 1858) Mocydiopsis attenuata (Germar, 1821)

Psammotettix maritimus ** (Perris, 1857) Psammotettix sabulicola ** (Curtis, 1837) Rhytistylus proceps (Kirschbaum, 1868) Tremulicerus fulgidus (Fabricius, 1775) Zyginidia viaduensis (Wagner, 1941) Delphacidae Delphacidae indet. (Leach, 1815) Chloriona sp. (Fieber, 1866) Gravesteiniella boldi ** (Scott, 1870) Javesella pellucida (Fabricius, 1794) Kelisia sabulicola * (Wagner, 1952) Mirabella albifrons (Fieber, 1866) Muirodelphax aubei * (Perris, 1857) Stenocranus major (Kirschbaum, 1868) Deltocephalinae Psammotettix species. (Haupt, 1929) Issidae Issus coleoptratus (Fabricius, 1781) Hemiptera (Heteroptera) Anthocoridae Anthocoris sp. (Fallén, 1814) Cardiastethus fasciiventris (Garbiglietti, 1869) Orius laevigatus (Fieber, 1860) Cydnidae Byrsinus flavicornis * (Fabricius, 1794) Lygaeidae Graptopeltus lynceus * (Fabricius, 1775) Ischnodemus sabuleti (Fallén, 1826) Kleidocerys resedae (Panzer, 1797) Metopoplax fuscinervis (Stål, 1872) Nysius thymi (Wolff, 1804) Peritrechus geniculatus (Hahn, 1832) Scolopostethus affinis (Schilling, 1829) Stygnocoris sp. (Douglas & Scott, 1865) Trapezonotus arenarius * (Linnaeus, 1758) Miridae Adelphocoris sp. (Reuter, 1896) Deraeocoris ruber (Linnaeus, 1758) Lygus sp. (Hahn, 1833) Megaloceroea recticornis (Geoffroy, 1785) Notostira elongata (Geoffroy, 1785) Stenodema calcarata (Fallén, 1807) Stenodema laevigata (Linnaeus, 1758) Stenodema trispinosa (Reuter, 1904) Nabidae Himacerus major * (Costa, 1842) Nabis ferus (Linnaeus, 1758) Nabis lineatus (Dahlbom, 1851) Nabis rugosus (Linnaeus, 1758) Pentatomidae Aelia acuminata ** (Linnaeus, 1758)

Sciocoris cursitans * (Fabricius, 1794) Rhopalidae Chorosoma schillingii ** (Schilling, 1829) Rhopalus parumpunctatus * (Schilling, 1829) Stictopleurus punctatonervosus (Goeze, 1778) Stenocephalidae Dicranocephalus agilis * (Scopoli, 1763) Hemiptera (Sternorrhyncha) Aphidoidea indet. ** (Linnaeus, 1758) Aphidoidea sp.3 ** (Linnaeus, 1758) Aphidoidea sp.4 ** (Linnaeus, 1758) Aphididae Laingia psammae ** (Theobald, 1922) Schizaphis rufula ** (Walker, 1849) Psyllidae Baeopelma foersteri (Flor, 1861) Tropiduchidae Tettigometra laeta ** (Herrich-Schäffer, 1835) Hymenoptera Hymenoptera sp. (Linnaeus, 1758) (Apoidea) Crabronidae Crabronidae sp. (Latreille, 1802) (Chrysidoidea) Bethylidae Bethylidae sp. (Halliday, 1839) Braconidae Aleiodes sp. (Wesmael, 1838) Bracon sp. (Fabricius, 1804) Braconidae sp. (Nees, 1811) Chelonus sp. (Panzer, 1806) (Chalcidoidea) Chalcidoidea sp. (Latreille, 1817) Chalcididae Brachymeria sp. (Westwood, 1829) Torymidae Toryminae sp. (Walker, 1833) (Cynipoidea) Cynipidae Cynipidae sp. (Latreille, 1802) (Formicoidea) Formicidae Formica cunicularia * (Latreille, 1798) Formicidae sp. (Latreille, 1809) Lasius spec. (Fabricius, 1804) Lasius emarginatus (Olivier, 1792) Lasius platythorax (Seiffert, 1991) Myrmica rugulosa (Nylander, 1849) Myrmica sabuleti * (Meinert, 1861) Myrmica specioides (Bondroit, 1918) Tetramorium caespitum * (Linnaeus, 1758) (Ichneumonoidea) Ichneumonoidea sp. (Latreille, 1802) Ichneumonidae Campopleginae sp. (Förster, 1868) Cryptinae sp. (Kirby, 1837) Ctenopelmatinae sp. (Förster, 1869) Diplazon laetatorius (Fabricius, 1781)

Diplazontinae sp. (Viereck, 1918) Dusona sp. (Cameron, 1901) Gelis areator (Panzer, 1804) Gelis sp. (Thunberg, 1827) Ichneumonidae sp. (Latreille, 1802) Itoplectis alternans (Gravenhorst, 1829) Itoplectis maculator (Fabricius, 1775) Lissonota sp. (Gravenhorst, 1829) Metopiinae sp. (Förster, 1869) Phygadeuontinae sp. (Viereck, 1918) Pimplinae sp. (Wesmael, 1845) Scambus sp. (Hartig, 1838) Virgichneumon maculicauda (Perkins, 1953) Xenolytus sp. (Förster, 1869) (Pompiloidea) Pompilidae Caliadurgus fasciatellus * (Spinola, 1808) Episyron rufipes * (Linnaeus, 1758) Pompilidae sp. (Latreille, 1805) (Proctotrupoidea) Proctotrupoidea indet. (Tenthredinoidea) Tenthredinidae Athalia rosae (Linnaeus, 1758) Nematus sp. (Panzer, 1801) Tenthredinidae sp. (Latreille, 1802) Vespoidae Vespoidae sp. (Latreille, 1802) Tiphiidae Tiphiidae sp. (Leach, 1815) Mecoptera Panorpidae Panorpidae sp. (Linnaeus, 1758) Lepidoptera Lepidoptera sp. (Linnaeus, 1758) Erebidae Tyria jacobaeae (Linnaeus, 1758) Gelechiidae Gelechiidae indet. (Stainton, 1854) Noctuidae Mesapamea spec. (Heinicke, 1959) Plutellidae Plutella xylostella (Linnaeus, 1758) Microlepidoptera indet. Neuroptera Neuroptera indet. (Linnaeus, 1758) Chrysopidae Chrysopa abbreviata * (Curtis, 1834) Chrysoperla carnea s.l. (Stephens, 1836) Chrysopidae sp. (Schneider, 1851) Nothochrysa capitata (Fabricius, 1793) Orthoptera Acrididae Chorthippus sp. (Fieber, 1852) Chorthippus biguttulus (Linnaeus, 1758) Chorthippus brunneus (Thunberg, 1815) Tettigoniidae Conocephalus discolor (Thunberg, 1815) Conocephalus dorsalis * (Latreille, 1804)

Conocephalus fuscus (Fabricius, 1793) Psocoptera Psocoptera sp. (Copland, 1957) Peripsocidae/Ectopsocidae sp. Elipsocidae Elipsocidae sp. (Kolbe, 1882) Thysanoptera Thysanoptera sp.1 (Haliday, 1836) Thysanoptera sp.2 (Haliday, 1836) Thysanoptera sp.3 (Haliday, 1836) Zygentoma Zygentoma indet (Börner, 1904) Class Malacostraca Isopoda Armadillidiidae Armadillidium vulgare (Latreille, 1804) Ligiidae Ligidium hypnorum (Cuvier, 1792) Porcellionidae Porcellio scaber (Latreille, 1804)

Kingdom Animalia Phylum Mollusca Class Gastropoda Ellobiida Ellobiidae Myosotella myosotis (Draparnaud, 1801) Panpulmonata Geomitridae Candidula intersecta (Poiret, 1801) Candidula unifasciata (Poiret, 1801) Helicidae Cepaea hortensis (Müller, 1774) Cepaea nemoralis (Linnaeus, 1758) Cornu aspersum (Müller, 1774) Theba pisana * (Müller, 1774) Stylommatophora Cochlicopidae Cochlicopa lubrica (Müller, 1774) Euconulidae Euconulus fulvus (Müller, 1774) Geomitridae Cernuella sp. * (Schlüter, 1838) Cochlicella acuta * (Müller, 1774) Cochlicella barbara * (Linnaeus, 1758) Pristilomatidae Vitrea contracta (Westerlund, 1871) Pupillidae Lauria cylindracea (Da Costa, 1778) Pupilla muscorum (Linnaeus, 1758) Vitrinidae Vitrina pellucida (Müller, 1774)

10.2. Output of models 10.2.1. Models for the local environmental variables

AIC-values: Model AIC-value Model AIC-value Model AIC-value Inflorescence (5m) 1489.277 Vitality (5m) 1670.989 Height (5m) 2528.226 Inflorescence (10m) 1480.274 Vitality (10m) 1674.255 Height (10m) 2522.845 Inflorescence (20m) 1483.621 Vitality (20m) 1674.612 Height (20m) 2528.496 Inflorescence (50m) 1492.216 Vitality (50m) 1669.988 Height (50m) 2528.748

Generalized linear mixed model (Poisson distribution) for number of inflorescences: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 0.05481 0.31684 0.173 0.86265 P10 5.98143 2.22925 2.683 0.00729 ** H10 0.062945 1.28983 0.488 0.62554 (P10)2 -5.03608 2.27436 -2.214 0.02681 * (H10)2 -0.42841 1.38466 -0.309 0.75720 P10*H10 -12.61467 3.83466 -3.290 0.00100 ** (P10)2*H10 5.94348 3.05186 1.947 0.05148 . P10*(H10)2 5.94974 2.61687 2.274 0.02299 *

Generalized linear mixed model (Poisson distribution) for vitality: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 1.24521 0.07961 15.641 <2e-16 *** H50 -0.26777 0.11582 -2.312 0.0208 *

Linear mixed model for height: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 58.975 3.838 320.113 15.367 <2e-16 *** P10 20.666 8.234 361.974 2.510 0.0125 * H10 -25.235 12.657 359.466 -1.994 0.0469 * (P10)2 -17.584 7.172 359.003 -2.452 0.0147 * (H10)2 20.057 10.381 360.579 1.932 0.0541 .

10.2.2. Models for diversity (France, Belgium and the United Kingdom)

10.2.2.1. Models for general diversity

AIC-values: Models - general AIC-value Models - general AIC-value Species richness (5m) 913.4917 Shannon’s index (5m) 704.2117 Species richness (10m) 918.0539 Shannon’s index (10m) 702.1163 Species richness (20m) 923.8506 Shannon’s index (20m) 701.1837 Species richness (50m) 915.6100 Shannon’s index (50m) 697.5129 Coleoptera abund. (5m) 1151.001 Arachnida abund. (5m) 1127.460 Coleoptera abund. (10m) 1148.403 Arachnida abund. (10m) 1129.878 Coleoptera abund. (20m) 1149.454 Arachnida abund. (20m) 1131.837 Coleoptera abund. (50m) 1152.444 Arachnida abund. (50m) 1124.838

Linear mixed model for general species richness: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 2.1213 0.6097 440.7289 3.479 0.000553 *** P50 6.4654 3.1649 436.2043 2.043 0.041671 * H50 -0.5964 2.0383 424.4794 -0.293 0.769972 (P50)2 -11.4638 4.8499 434.7018 -2.364 0.018532 * (H50)2 2.2976 2.0188 423.0468 1.138 0.255711 P50*H50 -2.3617 5.0989 440.0806 -0.463 0.643458 (P50)2*H50 13.8857 6.6358 437.3242 2.093 0.036967 * P50*(H50)2 -7.7045 4.4492 427.2518 -1.732 0.084055 .

Linear mixed model for the Shannon’s diversity index of general species: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 0.9782 0.4087 439.5773 2.393 0.0171 * P50 5.0316 2.4290 441.9943 2.071 0.0389 * H50 1.3366 0.8438 437.9067 1.576 0.1158 (P50)2 -6.7734 3.3426 441.8832 -2.026 0.0433 * (H50)2 -0.1414 0.6536 439.8116 -0.216 0.8288 P50*H50 -6.7995 3.3466 441.7378 -2.032 0.0428 * (P50)2*H50 8.0079 4.4056 437.4775 1.818 0.0698 .

Generalized linear mixed model (Negative Binomial distribution) for general Coleoptera abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 0.7767 0.3687 2.106 0.03517 * P10 -0.7916 0.2663 -2.972 0.00295 **

H10 2.1317 1.1493 1.855 0.06362 . (H10)2 -1.6963 0.9891 -1.715 0.08637 .

Generalized linear mixed model (Negative Binomial distribution) for general Arachnida abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) -0.1177 0.5273 -0.223 0.8234 P50 1.8870 1.0672 1.768 0.0770 . H50 2.6322 1.4343 1.835 0.0665 . (P50)2 -2.3429 1.2928 -1.812 0.0699 . (H50)2 -2.2070 1.0938 -2.018 0.0436 *

10.2.2.2. Models for dune-specific diversity

AIC-values: Models – dune AIC-value Models - dune AIC-value Species richness (5m) 358.3850 Shannon’s index (5m) 494.6894 Species richness (10m) 371.6685 Shannon’s index (10m) 508.5557 Species richness (20m) 363.8594 Shannon’s index (20m) 497.2519 Species richness (50m) 371.6404 Shannon’s index (50m) 502.5619 Coleoptera abund. (5m) 287.5563 Arachnida abund. (5m) 180.4876 Coleoptera abund. (10m) 283.1089 Arachnida abund. (10m) 179.1385 Coleoptera abund. (20m) 283.1539 Arachnida abund. (20m) 180.7387 Coleoptera abund. (50m) 269.0027 Arachnida abund. (50m) 180.2550

Linear mixed model for dune-specific species richness: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 0.8302 0.3455 348.9941 2.403 0.01680 * P5 3.3705 0.7336 335.4932 4.595 6.14e-06 *** H5 -2.0320 1.1706 340.2384 -1.736 0.08350 . (P5)2 -0.5859 0.3989 342.4263 -1.469 0.14281 (H5)2 1.8362 0.9982 337.5806 1.839 0.06672 . P5*H5 -2.8747 0.9963 331.8382 -2.885 0.00417 **

Linear mixed model for the Shannon’s diversity index of dune-specific species: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 0.6730 0.3161 348.9704 2.129 0.033942 * P5 2.8937 0.6702 335.3455 4.318 2.08e-05 *** H5 -1.6231 1.0685 339.4406 -1.519 0.129663 (H5)2 1.6551 0.8916 334.8573 1.856 0.064274 . P5*H5 -3.1245 0.8016 336.3853 -3.898 0.000117 ***

Generalized linear mixed model (Negative Binomial distribution) for dune-specific Coleoptera abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) -1.0222 0.5060 -2.020 0.04336 * H10 1.8803 0.6186 3.039 0.00237 **

Model for dune-specific Arachnida abundance showed no effect.

10.2.2.3. Models for marram-specific diversity

AIC-values: Models - marram AIC-value Models - marram AIC-value Species richness (5m) 247.3719 Shannon’s index (5m) 400.1877 Species richness (10m) 248.5162 Shannon’s index (10m) 400.5927 Species richness (20m) 248.9801 Shannon’s index (20m) 401.2336 Species richness (50m) 244.3384 Shannon’s index (50m) 393.3707 Coleoptera abund. (5m) 190.5881 Arachnida abund. (5m) 465.3063 Coleoptera abund. (10m) 187.6972 Arachnida abund. (10m) 466.6231 Coleoptera abund. (20m) 193.7532 Arachnida abund. (20m) 465.6311 Coleoptera abund. (50m) 189.8083 Arachnida abund. (50m) 461.8134

Model for species richness showed no effect.

Linear mixed model for the Shannon’s diversity index of marram-specific species: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 0.5616 0.3855 327.1312 1.457 0.14615 P50 -4.3824 2.2706 318.8494 -1.930 0.05449 . H50 2.3629 1.3931 325.5050 1.696 0.09082 . (P50)2 8.0746 3.6214 324.8285 2.230 0.02645 * (H50)2 -3.5721 1.4862 324.6645 -2.403 0.01680 * P50*H50 2.2484 3.4449 307.3013 0.653 0.51445 (P50)2*H50 -13.2215 5.0493 322.9602 -2.618 0.00925 ** P50*(H50)2 7.2567 3.2022 322.5551 2.266 0.02410 *

Linear mixed model for marram-specific Coleoptera abundance: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) -0.1828 0.2945 157.7573 -0.619 0.5369 P10 3.6245 1.8825 156.3958 1.925 0.0560 . H10 0.8212 0.8796 159.9775 0.934 0.3519 (P10)2 -4.2924 2.1280 158.8258 -2.017 0.0454 * (H10)2 -0.1432 0.8339 159.9934 -0.172 0.8638 P10*H10 -4.1329 2.6483 155.8594 -1.561 0.1207

(P10)2*H10 4.5792 2.7779 156.7645 1.648 0.1013

Generalized linear mixed model (Poisson distribution) for marram-specific Arachnida abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 1.9287 0.6896 2.797 0.00516 ** P50 -3.5630 1.7388 -2.049 0.04046 * H50 -1.8166 1.0189 -1.783 0.07462 . P50*H50 4.6758 1.945 1.945 0.05181 .

10.2.3. Models for diversity (France and Belgium)

10.2.3.1. Models for general diversity

AIC-values: Models - general AIC-value Models - general AIC-value Species richness (5m) 806.2381 Shannon’s index (5m) 620.6884 Species richness (10m) 811.0860 Shannon’s index (10m) 619.3867 Species richness (20m) 816.8019 Shannon’s index (20m) 618.9135 Species richness (50m) 805.0732 Shannon’s index (50m) 610.1409 Coleoptera abund. (5m) 986.6772 Arachnida abund. (5m) 976.6467 Coleoptera abund. (10m) 987.7082 Arachnida abund. (10m) 978.4722 Coleoptera abund. (20m) 987.7250 Arachnida abund. (20m) 980.0995 Coleoptera abund. (50m) 988.5623 Arachnida abund. (50m) 975.3058

Linear mixed model for general species richness: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 1.59819 0.55484 382.97823 2.880 0.00419 ** P50 8.38032 4.12530 380.73981 2.031 0.04290 * H50 1.62813 1.24339 381.99805 1.309 0.19117 (P50)2 -11.46390 5.75598 373.80638 -1.992 0.04714 * (H50)2 0.06048 1.12981 380.83178 0.054 0.95730 P50*H50 -10.12527 5.89926 378.52885 -1.716 0.08691 . (P50)2*H50 12.88483 8.04873 372.54609 1.601 0.11026

Suppl. Figure 1: Prediction for species richness (G) at 50m radius scale in function of P.

Linear mixed model for the Shannon’s diversity index of general species: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 1.6859 0.1496 226.5412 11.271 >2e-16 *** P50 1.0024 0.7631 377.0688 1.313 0.1898 (P50)2 -2.1773 0.9892 376.0304 -2.201 0.0283 *

Suppl. Figure 2: Prediction for the Shannon's diversity index (G) at 50m radius scale in function of P.

Generalized linear mixed model (Negative Binomial distribution) for general Coleoptera abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 0.3649 0.4931 0.740 0.4592 H5 3.1767 1.4972 2.122 0.0339 * (H5)2 -2.9088 1.1645 -2.498 0.0125 *

Suppl. Figure 3: Predictions for Coleoptera abundance (G) at 5m radius scale in function of H.

Generalized linear mixed model (Negative Binomial distribution) for general Arachnida abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) -0.1859 0.6526 -0.285 0.7757 P50 1.8612 1.2237 1.521 0.1283 H50 2.7966 1.8021 1.552 0.1207 (P50)2 -2.2937 1.5654 -1.465 0.1429 (H50)2 -2.2934 1.3455 -1.704 0.0883

Suppl. Figure 4: Predictions for Arachnida abundance (G) at 50m radius scale in function of H.

10.2.3.2. Models for dune-specific diversity

AIC-values: Models – dune AIC-value Models - dune AIC-value Species richness (5m) 327.9666 Shannon’s index (5m) 445.9205 Species richness (10m) 342.1679 Shannon’s index (10m) 460.4068 Species richness (20m) 333.9252 Shannon’s index (20m) 448.1387 Species richness (50m) 344.1001 Shannon’s index (50m) 455.9352 Coleoptera abund. (5m) 276.9725 Arachnida abund. (5m) 178.3915 Coleoptera abund. (10m) 272.9109 Arachnida abund. (10m) 177.0379 Coleoptera abund. (20m) 274.1217 Arachnida abund. (20m) 178.7023 Coleoptera abund. (50m) 259.9303 Arachnida abund. (50m) 178.1609

Linear mixed model for dune-specific species richness: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 1.7345 0.3570 313.9857 4.859 1.86e-06 *** P5 2.5740 0.5996 295.6558 4.293 2.40e-05 *** H5 -1.9125 1.1784 310.5756 -1.623 0.1056 (P5)2 -0.6131 0.3134 306.1642 -1.956 0.0513 . (H5)2 1.5434 0.9514 309.1149 1.622 0.1058 P5*H5 -1.8769 0.7972 295.9064 -2.355 0.0192 *

Suppl. Figure 5: Predictions for species richness (D) at 5m radius scale in function of P (left) and H (right).

Linear mixed model for the Shannon’s diversity index of dune-specific species: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 0.1669 0.2184 305.5663 0.764 0.445291 P5 2.1439 0.5253 304.1061 4.081 5.37e-05 *** H5 0.4019 0.2864 307.4420 1.403 0.161597 P5*H5 -2.1844 0.6268 304.3411 -3.485 0.000564 ***

Suppl. Figure 6: Predictions for the Shannon's diversity index (D) at 5m radius scale in function of P.

Generalized linear mixed model (Negative Binomial distribution) for dune-specific Coleoptera abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 7.269 8.233 0.883 0.3773 P50 1.036 25.712 0.040 0.9678 H50 -30.482 21.664 -1.407 0.1594 (P50)2 -34.056 23.685 -1.438 0.1505 (H50)2 29.769 14.480 2.056 0.0398 * P50*H50 39.614 56.808 0.697 0.4856 (P50)2*H50 52.467 29.439 1.782 0.0747 . P50*(H50)2 -62.364 35.437 -1.760 0.0784 .

Suppl. Figure 7: Predictions for Coleoptera abundance (D) at 50m radius scale in function of P (left) and H (right).

Models for dune-specific Arachnida abundance showed no effect.

10.2.3.3. Models for marram-specific diversity

AIC-values: Models - marram AIC-value Models - marram AIC-value Species richness (5m) 234.7286 Shannon’s index (5m) 376.2960 Species richness (10m) 236.5974 Shannon’s index (10m) 377.3028 Species richness (20m) 238.1542 Shannon’s index (20m) 378.8550 Species richness (50m) 235.5330 Shannon’s index (50m) 372.4933 Coleoptera abund. (5m) 183.6771 Arachnida abund. (5m) 460.1432 Coleoptera abund. (10m) 181.1000 Arachnida abund. (10m) 461.6937 Coleoptera abund. (20m) 188.5282 Arachnida abund. (20m) 460.0922 Coleoptera abund. (50m) 182.4907 Arachnida abund. (50m) 456.9709

Linear mixed model for marram-specific species richness: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 1.4059 0.1694 296.8241 8.301 3.67e-15 *** P5 3.1534 1.5841 287.6202 1.991 0.0475 * H5 -0.5974 0.6560 283.7194 -0.911 0.3633 (P5)2 -3.3449 1.7591 289.8212 -1.902 0.0582 . (H5)2 0.7640 0.6446 284.2928 1.185 0.2369 P5*H5 -3.6755 1.9793 285.1487 -1.857 0.0644 . (P5)2*H5 3.8256 2.0824 288.2206 1.837 0.0672 .

Suppl. Figure 8: Predictions for species richness (M) at 5m radius scale in function of P.

Linear mixed model for the Shannon’s diversity index of marram-specific species: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) 0.5958 0.3933 307.9937 1.515 0.1309 P50 -4.6547 2.9348 305.6298 -1.586 0.1138 H50 2.3748 1.4764 308.3830 1.609 0.1087 (P50)2 8.5637 4.6090 309.6741 1.858 0.0641 . (H50)2 -3.7322 1.6212 308.5092 -2.302 0.0220 * P50*H50 2.9890 4.3675 307.7127 0.684 0.4943 (P50)2*H50 -14.3167 6.5995 308.4420 -2.169 0.0308 * P50*(H50)2 7.3998 7.3998 300.7841 2.260 0.0246 *

Suppl. Figure 9: Predictions for the Shannon's diversity index (M) at 50m radius scale in function of P (left) and H (right).

Linear mixed model for marram-specific Coleoptera abundance: Fixed effect Estimate Std. Error df t-value Pr(>|t|) (intercept) -0.23494 0.30092 150.56954 -0.781 0.4362 P10 4.17436 1.97214 149.39369 2.117 0.0359 * H10 0.77681 0.88350 152.96930 0.879 0.3806 (P10)2 -4.84011 2.24198 152.09635 -2.159 0.0324 * (H10)2 -0.01615 0.84624 152.99985 -0.019 0.9848 P10*H10 -4.69964 2.73584 148.70965 -1.718 0.0879 . (P10)2*H10 5.08306 2.89090 150.26878 1.758 0.0807 .

Suppl. Figure 10: Predictions for Coleoptera abundance (M) at 10m radius scale in function of P.

Generalized linear mixed model (Poisson distribution) for marram-specific Arachnida abundance: Fixed effect Estimate Std. Error z-value Pr(>|z|) (intercept) 1.9531 0.6866 2.845 0.00445 ** P50 -3.5065 1.7219 -2.036 0.04171 * H50 -1.8747 1.0171 -1.843 0.06530 . P50*H50 4.6892 2.3855 1.966 0.04933 *

Suppl. Figure 11: Predictions for Arachnida abundance (M) at 50m radius scale in function of P (left) and H (right).

10.3. R-code Noëmie Van den Bon

############################### ##### Preparation of data ##### ###############################

### Load required packages ### library(permute) library(lattice) library(vegan) library(dplyr) library(tidyr) library(ggplot2) library(labdsv) library(data.table) library(lme4) library(lmerTest) library(moments) library(scales) library(RVAideMemoire) library(MASS)

### Load data ### species <- read.table(file = "Species_FBUK.csv", header = T, sep = ";") species$Species <- as.character(species$Species) classification <- read.table(file = "Class_FBUK_Stat.csv", header = T, sep = ";") classification$Species <- as.character(classification$Species) env_marram <- read.table(file="Env_FBNUK.csv", header = T, dec = ",", sep = ";") spat_marram <- read.table(file = "Spat_FBNUK.csv", header = T, dec = ",", sep= ";") spat_marram$distance <- as.factor(spat_marram$distance)

# For the models without UK, a very similar script was used, with some changes and # different results as indicated in the appendix.

### Edit data for proper use ###

# Extend species data with classification + specialisation species_classification <- dplyr::left_join(species, classification, by =c("Species")) species_classification$site <- as.character(species_classification$site) species_classification$code <- paste(species_classification$site, "_", species_classif ication$Country)

# Make subsets per country for species classification species_classification_F <- filter(species_classification, Country == "France") species_classification_B <- filter(species_classification, Country == "Belgium") species_classification_E <- filter(species_classification, Country == "England")

# Make subsets per country for env data env_marram_F <- filter(env_marram, Country == "France") env_marram_F$site <- as.factor(env_marram_F$site) env_marram_B <- filter(env_marram, Country == "Belgium")

env_marram_B$site <- as.factor(env_marram_B$site) env_marram_N <- filter(env_marram, Country == "Netherlands") env_marram_N$site <- as.factor(env_marram_N$site) env_marram_E <- filter(env_marram, Country == "England") env_marram_E$site <- as.factor(env_marram_E$site)

# Data of sites and corresponding location site_loc_F <- env_marram_F[, c(1, 7)] site_loc_F$site <- as.character(site_loc_F$site) site_loc_B <- env_marram_B[, c(1, 7)] site_loc_B$site <- as.character(site_loc_B$site) site_loc_N <- env_marram_N[, c(1, 7)] site_loc_N$site <- as.character(site_loc_N$site) site_loc_E <- env_marram_E[, c(1, 7)] site_loc_E$site <- as.character(site_loc_E$site)

# Add column of corresponding location to species subsets species_classification_F <- dplyr::left_join(species_classification_F, site_loc_F, by = c("site")) species_classification_B <- dplyr::left_join(species_classification_B, site_loc_B, by = c("site")) species_classification_E <- dplyr::left_join(species_classification_E, site_loc_E, by = c("site"))

# Combine species data subsets species_classification <- rbind(species_classification_F, species_classification_B, sp ecies_classification_E)

# Transform spatial data structure spatial_F <- filter(spat_marram, Country == "France") spatial_F$site <- as.factor(spatial_F$site) spatial_F <- spatial_F[, -2] # Remove country-column, otherwise twice spat_F <- spatial_F %>% gather(key, value, -distance, -site) %>% unite(combined, dista nce, key, sep = ".") %>% spread(combined, value) spat_F <- spat_F %>% dplyr::rename('P_10' = '10.prop.landscape') spat_F <- spat_F %>% dplyr::rename('H_10' = '10.STD_DEVIATE') spat_F <- spat_F %>% dplyr::rename('P_20' = '20.prop.landscape') spat_F <- spat_F %>% dplyr::rename('H_20' = '20.STD_DEVIATE') spat_F <- spat_F %>% dplyr::rename('P_5' = '5.prop.landscape') spat_F <- spat_F %>% dplyr::rename('H_5' = '5.STD_DEVIATE') spat_F <- spat_F %>% dplyr::rename('P_50' = '50.prop.landscape') spat_F <- spat_F %>% dplyr::rename('H_50' = '50.STD_DEVIATE') spat_F$H_5 <- rescale(spat_F$H_5) spat_F$H_10 <- rescale(spat_F$H_10) spat_F$H_20 <- rescale(spat_F$H_20) spat_F$H_50 <- rescale(spat_F$H_50) spatial_B <- filter(spat_marram, Country == "Belgium") spatial_B$site <- as.factor(spatial_B$site) spatial_B <- spatial_B[-c(182:185), -2] # double sample spat_B <- spatial_B %>% gather(key, value, -distance, -site) %>% unite(combined, dista nce, key, sep = ".") %>% spread(combined, value) spat_B <- spat_B %>% dplyr::rename('P_10' = '10.prop.landscape') spat_B <- spat_B %>% dplyr::rename('H_10' = '10.STD_DEVIATE') spat_B <- spat_B %>% dplyr::rename('P_20' = '20.prop.landscape')

spat_B <- spat_B %>% dplyr::rename('H_20' = '20.STD_DEVIATE') spat_B <- spat_B %>% dplyr::rename('P_5' = '5.prop.landscape') spat_B <- spat_B %>% dplyr::rename('H_5' = '5.STD_DEVIATE') spat_B <- spat_B %>% dplyr::rename('P_50' = '50.prop.landscape') spat_B <- spat_B %>% dplyr::rename('H_50' = '50.STD_DEVIATE') spat_B$H_5 <- rescale(spat_B$H_5) spat_B$H_10 <- rescale(spat_B$H_10) spat_B$H_20 <- rescale(spat_B$H_20) spat_B$H_50 <- rescale(spat_B$H_50) spatial_N <- filter(spat_marram, Country == "Netherlands") spatial_N$site <- factor(spatial_N$site) spatial_N <- spatial_N[, -2] spat_N <- spatial_N %>% gather(key, value, -distance, -site) %>% unite(combined, dista nce, key, sep = ".") %>% spread(combined, value) spat_N <- spat_N %>% dplyr::rename('P_10' = '10.prop.landscape') spat_N <- spat_N %>% dplyr::rename('H_10' = '10.STD_DEVIATE') spat_N <- spat_N %>% dplyr::rename('P_20' = '20.prop.landscape') spat_N <- spat_N %>% dplyr::rename('H_20' = '20.STD_DEVIATE') spat_N <- spat_N %>% dplyr::rename('P_5' = '5.prop.landscape') spat_N <- spat_N %>% dplyr::rename('H_5' = '5.STD_DEVIATE') spat_N <- spat_N %>% dplyr::rename('P_50' = '50.prop.landscape') spat_N <- spat_N %>% dplyr::rename('H_50' = '50.STD_DEVIATE') spat_N$H_5 <- rescale(spat_N$H_5) spat_N$H_10 <- rescale(spat_N$H_10) spat_N$H_20 <- rescale(spat_N$H_20) spat_N$H_50 <- rescale(spat_N$H_50) spatial_E <- filter(spat_marram, Country == "England") spatial_E$site <- factor(spatial_E$site) spatial_E <- spatial_E[, -2] spat_E <- spatial_E %>% gather(key, value, -distance, -site) %>% unite(combined, dista nce, key, sep = ".") %>% spread(combined, value) spat_E <- spat_E %>% dplyr::rename('P_10' = '10.prop.landscape') spat_E <- spat_E %>% dplyr::rename('H_10' = '10.STD_DEVIATE') spat_E <- spat_E %>% dplyr::rename('P_20' = '20.prop.landscape') spat_E <- spat_E %>% dplyr::rename('H_20' = '20.STD_DEVIATE') spat_E <- spat_E %>% dplyr::rename('P_5' = '5.prop.landscape') spat_E <- spat_E %>% dplyr::rename('H_5' = '5.STD_DEVIATE') spat_E <- spat_E %>% dplyr::rename('P_50' = '50.prop.landscape') spat_E <- spat_E %>% dplyr::rename('H_50' = '50.STD_DEVIATE') spat_E$H_5 <- rescale(spat_E$H_5) spat_E$H_10 <- rescale(spat_E$H_10) spat_E$H_20 <- rescale(spat_E$H_20) spat_E$H_50 <- rescale(spat_E$H_50)

# Join all environmental + spatial data per country and combine everything env_spat_F <- left_join(env_marram_F, spat_F, by = "site") env_spat_B <- left_join(env_marram_B, spat_B, by = "site") env_spat_N <- left_join(env_marram_N, spat_N, by = "site") env_spat_E <- left_join(env_marram_E, spat_E, by = "site") env_spat <- rbind(env_spat_F, env_spat_B, env_spat_N, env_spat_E) env_spat <- env_spat %>% dplyr::rename(Length = Length..cm.)

# Make a 'G', 'D' and 'M' subset. generalists_classification <- filter(species_classification, SPECIFICITY == "G") specialists_D_classification <- filter(species_classification, SPECIFICITY == "D") specialists_M_classification <- filter(species_classification, SPECIFICITY == "M")

# Make species matrix + G + M + D. species_matrix <- matrify(dplyr::select(species_classification, c(code, Species, Amoun t))) generalists_matrix <- matrify(dplyr::select(generalists_classification, c(code, Specie s, Amount))) specialists_D_matrix <- matrify(dplyr::select(specialists_D_classification, c(code, Sp ecies, Amount))) specialists_M_matrix <- matrify(dplyr::select(specialists_M_classification, c(code, Sp ecies, Amount)))

# Make Coleoptera subsets coleoptera_classification <- filter(species_classification, ORDER == "Coleoptera ") coleoptera_classification_G <- filter(coleoptera_classification, SPECIFICITY == "G") coleoptera_classification_D <- filter(coleoptera_classification, SPECIFICITY == "D") coleoptera_classification_M <- filter(coleoptera_classification, SPECIFICITY == "M") coleoptera_G_matrix <- matrify(dplyr::select(coleoptera_classification_G, c(code, Spec ies, Amount))) coleoptera_D_matrix <- matrify(dplyr::select(coleoptera_classification_D, c(code, Spec ies, Amount))) coleoptera_M_matrix <- matrify(dplyr::select(coleoptera_classification_M, c(code, Spec ies, Amount)))

# Make Arachnida subsets arachnida_classification <- filter(species_classification, CLASS == "Arachnida ") arachnida_classification_G <- filter(arachnida_classification, SPECIFICITY == "G") arachnida_classification_D <- filter(arachnida_classification, SPECIFICITY == "D") arachnida_classification_M <- filter(arachnida_classification, SPECIFICITY == "M") arachnida_G_matrix <- matrify(dplyr::select(arachnida_classification_G, c(code, Specie s, Amount))) arachnida_D_matrix <- matrify(dplyr::select(arachnida_classification_D, c(code, Specie s, Amount))) arachnida_M_matrix <- matrify(dplyr::select(arachnida_classification_M, c(code, Specie s, Amount)))

############################# ### DIVERSITY EXPLORATION ### #############################

### Exploratory barplots over the study area totals <- subset(species_classification, !is.na(Amount)) %>% dplyr::group_by(Country) %>% dplyr::summarize(total = sum(Amount)) barplot_counted <- ggplot(subset(species_classification, !is.na(CLASS)), aes(x = Count ry, y = Amount, fill = CLASS)) + geom_bar(stat = "identity") + labs(title = "Overview of counted individuals per Class\nfor the different countries in the study area", x = "Country", y = "Counted individuals", fill = "Classes") barplot_counted + theme(plot.title = element_text(face = "bold")) + geom_text(aes(Coun try, total + 200, label = total, fill = NULL), data = totals) # Unique (morpho)species species_classification_unique <- species_classification %>% distinct(Species, .keep_al

l = T)

# Distribution over Classes, Orders and habitat specificity barplot_classes <- ggplot(species_classification, aes(x = CLASS, fill = CLASS)) + geom _bar() + labs(title = "Overview counted individuals per Class in the study area", x = "Classes", y = "Counted individuals") barplot_classes + theme(legend.position = "none", plot.title = element_text(face = "bo ld")) barplot_classes_spec <- ggplot(species_classification_unique, aes(x = CLASS, fill = CL ASS)) + geom_bar() + labs(title = "Overview of species per Class in the study area", x = "Classes", y = "Counted species") barplot_classes_spec + theme(legend.position = "none", plot.title = element_text(face = "bold")) barplot_orders <- ggplot(subset(species_classification, !is.na(ORDER)), aes(x = reorde r(ORDER, desc(ORDER)), fill = ORDER)) + geom_bar() + labs(title = "Overview of counted individuals per Order (+ Suborder)\nin the study area", x = "Orders (+ Suborder)", y = "Counted individuals") barplot_orders + coord_flip() + theme(legend.position = "none", plot.title = element_t ext(face = "bold")) barplot_orders_spec <- ggplot(subset(species_classification_unique, !is.na(ORDER)), ae s(x = reorder(ORDER, desc(ORDER)), fill = ORDER)) + geom_bar() + labs(title = "Overvie w of number of species per Order (+ Suborder)\nin the study area", x = "Orders (+ Subo rder)", y = "Counted species") barplot_orders_spec + coord_flip() + theme(legend.position = "none", plot.title = elem ent_text(face = "bold")) barplot_specificity <- ggplot(species_classification, aes(x = SPECIFICITY, fill = SPEC IFICITY)) + geom_bar() + labs(title = "Overview counted individuals per habitat specif icity in the study area", x = "Specificity", y = "Counted individuals") barplot_specificity + theme(legend.position = "none", plot.title = element_text(face = "bold")) barplot_specificity_spec <- ggplot(species_classification_unique, aes(x = SPECIFICITY, fill = SPECIFICITY)) + geom_bar() + labs(title = "Overview counted species per habitat specificity in the study area", x = "Specificity", y = "Counted species") barplot_specificity_spec + theme(legend.position = "none", plot.title = element_text(f ace = "bold"))

# Habitat specifity differences between the countries species_classification$SPECIFICITY <- factor(species_classification$SPECIFICITY, level s = c("M", "D", "G")) barplot_specificity_country <- ggplot(subset(species_classification, !is.na(SPECIFICIT Y)), aes(x = Country, fill = SPECIFICITY)) + geom_bar() + labs(title = "Overview of habitat specificity per country", x = "Countr y", y = "Counted individuals", fill = "Habitat specificity") barplot_specificity_country + theme(plot.title = element_text(face = "bold")) + scale_ fill_manual(values = c("green", "orange", "black"), labels = c("Marram-specific", "Dun e-specific", "General")) species_classification_unique$SPECIFICITY <- factor(species_classification_unique$SPEC IFICITY, levels = c("M", "D", "G")) barplot_specificity_country_spec <- ggplot(subset(species_classification_unique, !is.n a(SPECIFICITY)), aes(x = Country, fill = SPECIFICITY)) + geom_bar() + labs(title = "Overview of habitat specificity per country", x = "Countr y", y = "Counted species", fill = "Habitat specificity") barplot_specificity_country_spec + theme(plot.title = element_text(face = "bold")) + s

cale_fill_manual(values = c("green", "orange", "black"), labels = c("Marram-specific", "Dune-specific", "General"))

# Habitat specificity distribution per class and for the orders barplot_specificity_class <- ggplot(subset(species_classification, !is.na(SPECIFICITY) ), aes(x = CLASS, fill = SPECIFICITY)) + geom_bar() + labs(title = "Overview of habitat specificity per Class", x = "Classes" , y = "Counted individuals", fill = "Habitat specificity") barplot_specificity_class + theme(plot.title = element_text(face = "bold")) + scale_fi ll_manual(values = c("green", "orange", "black"), labels = c("Marram-specific", "Dune- specific", "General")) barplot_specificity_class_spec <- ggplot(subset(species_classification_unique, !is.na( SPECIFICITY)), aes(x = CLASS, fill = SPECIFICITY)) + geom_bar() + labs(title = "Overview of habitat specificity per Class", x = "Classes" , y = "Counted species", fill = "Habitat specificity") barplot_specificity_class_spec + theme(plot.title = element_text(face = "bold")) + sca le_fill_manual(values = c("green", "orange", "black"), labels = c("Marram-specific", " Dune-specific", "General")) barplot_specificity_orders <- ggplot(subset(species_classification, !is.na(ORDER)), ae s(x = reorder(ORDER, desc(ORDER)), fill = SPECIFICITY)) + geom_bar() + labs(title = "Overview of habitat specificity per Order (+ Suborder)", x = "Orders (+ Suborder)", y = "Counted individuals", fill = "Habitat specificity") barplot_specificity_orders + coord_flip() + theme(legend.position = "none", plot.title = element_text(face = "bold")) + scale_fill_manual(values = c("green", "orange", "black"), labels = c("Marram-specifi c", "Dune-specific", "General")) barplot_specificity_orders_spec <- ggplot(subset(species_classification_unique, !is.na (ORDER)), aes(x = reorder(ORDER, desc(ORDER)), fill = SPECIFICITY)) + geom_bar() + lab s(title = "Overview of habitat specificity per Order (+ Suborder)", x = "Orders (+ Sub order)", y = "Counted species", fill = "Habitat specificity") barplot_specificity_orders_spec + coord_flip() + theme(legend.position = "none", plot. title = element_text(face = "bold")) + scale_fill_manual(values = c("green", "orange", "black"), labels = c("Marram-specifi c", "Dune-specific", "General"))

# P ~ H plot_PH_5 <- ggplot(env_spat, aes(x = P_5, y = H_5)) + geom_point(aes(colour = Country )) + geom_smooth() plot_PH_5 + labs(title = "Relationship between aggregation (H) and proportion (P) at 5 m radius scale", x = "Proportion of marram (P)", y = "Aggregation of marram (H)") plot_PH_10 <- ggplot(env_spat, aes(x = P_10, y = H_10)) + geom_point(aes(colour = Coun try)) + geom_smooth() plot_PH_10 + labs(title = "Relationship between aggregation (H) and proportion (P) at 10m radius scale", x = "Proportion of marram (P)", y = "Aggregation of marram (H)") plot_PH_20 <- ggplot(env_spat, aes(x = P_20, y = H_20)) + geom_point(aes(colour = Coun try)) + geom_smooth() plot_PH_20 + labs(title = "Relationship between aggregation (H) and proportion (P) at 20m radius scale", x = "Proportion of marram (P)", y = "Aggregation of marram (H)") plot_PH_50 <- ggplot(env_spat, aes(x = P_50, y = H_50)) + geom_point(aes(colour = Coun try)) + geom_smooth() plot_PH_50 + labs(title = "Relationship between aggregation (H) and proportion (P) at 50m radius scale", x = "Proportion of marram (P)", y = "Aggregation of marram (H)")

##################################################### ### Diversity Measures for specialisation subsets ### #####################################################

### For all data

# Fix to allow left_join based on code (as in species matrices) env_spat$code <- paste(env_spat$site, "_", env_spat$Country)

# Diversity measures for 'general' species (G) generalists_richness <- specnumber(generalists_matrix) generalists_shannon <- diversity(generalists_matrix) generalists_simpson <- diversity(generalists_matrix, index = "simpson") generalists_alpha <- data.frame(richness = generalists_richness, shannon = generalists _shannon, simpson = generalists_simpson) generalists_alpha <- setnames(setDT(generalists_alpha, keep.rownames= TRUE),1,"code") generalists_alpha <- left_join(generalists_alpha, env_spat, by = "code")

# Diversity measures for D - specialists specialists_D_richness <- specnumber(specialists_D_matrix) specialists_D_shannon <- diversity(specialists_D_matrix) specialists_D_simpson <- diversity(specialists_D_matrix, index = "simpson") specialists_D_alpha <- data.frame(richness = specialists_D_richness, shannon = special ists_D_shannon, simpson = specialists_D_simpson) specialists_D_alpha <- setnames(setDT(specialists_D_alpha, keep.rownames = TRUE), 1, " code") specialists_D_alpha <- left_join(specialists_D_alpha, env_spat, by = "code")

# Diversity measures for M - specialists specialists_M_richness <- specnumber(specialists_M_matrix) specialists_M_shannon <- diversity(specialists_M_matrix) specialists_M_simpson <- diversity(specialists_M_matrix, index = "simpson") specialists_M_alpha <- data.frame(richness = specialists_M_richness, shannon = special ists_M_shannon, simpson = specialists_M_simpson) specialists_M_alpha <- setnames(setDT(specialists_M_alpha, keep.rownames = TRUE), 1, " code") specialists_M_alpha <- left_join(specialists_M_alpha, env_spat, by = "code")

# Richness for Coleoptera coleoptera_G_abundance <- rowSums(coleoptera_G_matrix) coleoptera_D_abundance <- rowSums(coleoptera_D_matrix) coleoptera_M_abundance <- rowSums(coleoptera_M_matrix) coleoptera_G_alpha <- data.frame(abundance = coleoptera_G_abundance) coleoptera_G_alpha <- setnames(setDT(coleoptera_G_alpha, keep.rownames = TRUE), 1, "co de") coleoptera_G_alpha <- left_join(coleoptera_G_alpha, env_spat, by = "code") coleoptera_D_alpha <- data.frame(abundance = coleoptera_D_abundance) coleoptera_D_alpha <- setnames(setDT(coleoptera_D_alpha, keep.rownames = TRUE), 1, "co de") coleoptera_D_alpha <- left_join(coleoptera_D_alpha, env_spat, by = "code") coleoptera_M_alpha <- data.frame(abundance = coleoptera_M_abundance) coleoptera_M_alpha <- setnames(setDT(coleoptera_M_alpha, keep.rownames = TRUE), 1, "co de") coleoptera_M_alpha <- left_join(coleoptera_M_alpha, env_spat, by = "code")

# Richness for Arachnida arachnida_G_abundance <- rowSums(arachnida_G_matrix) arachnida_D_abundance <- rowSums(arachnida_D_matrix) arachnida_M_abundance <- rowSums(arachnida_M_matrix) arachnida_G_alpha <- data.frame(abundance = arachnida_G_abundance) arachnida_G_alpha <- setnames(setDT(arachnida_G_alpha, keep.rownames= TRUE),1,"code") arachnida_G_alpha <- left_join(arachnida_G_alpha, env_spat, by = "code") arachnida_D_alpha <- data.frame(abundance = arachnida_D_abundance) arachnida_D_alpha <- setnames(setDT(arachnida_D_alpha, keep.rownames= TRUE),1,"code") arachnida_D_alpha <- left_join(arachnida_D_alpha, env_spat, by = "code") arachnida_M_alpha <- data.frame(abundance = arachnida_M_abundance) arachnida_M_alpha <- setnames(setDT(arachnida_M_alpha, keep.rownames= TRUE),1,"code") arachnida_M_alpha <- left_join(arachnida_M_alpha, env_spat, by = "code")

################################################ ### MODELS FOR LOCAL ENVIRONMENTAL VARIABLES ### ################################################

# Make new dataset without NA's so that the same for each model env_spat_no_NA <- env_spat env_spat_no_NA <- env_spat_no_NA %>% drop_na(P_10, H_10, P_20, H_20, P_5, H_5, P_50, H _50)

#### INFLORESCENCES ####

# Check for overdispersion (when var > mean --> Poisson not appropriate) mean(na.omit(env_spat$Florescence)) ## [1] 1.23869 var(na.omit(env_spat$Florescence)) ## [1] 1.291376 glmer_Florescence_5 <- glmer(Florescence ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = env_spat_no_NA, family = "p oisson") overdisp.glmer(glmer_Florescence_5) # ratio 0.956 à ok. glmer_Florescence_10 <- glmer(Florescence ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_ 10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = env_spat_no_NA, family = "poisson") overdisp.glmer(glmer_Florescence_10) # ratio 0.938 à ok. glmer_Florescence_20 <- glmer(Florescence ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_ 20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = env_spat_no_NA, family = "poisson") overdisp.glmer(glmer_Florescence_20) # ratio 0.949 à ok. glmer_Florescence_50 <- glmer(Florescence ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_ 50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = env_spat_no_NA, family = "poisson") overdisp.glmer(glmer_Florescence_50) # ratio 0.968 à ok.

# Choose the best model for number of inflorescences AIC(glmer_Florescence_5, glmer_Florescence_10, glmer_Florescence_20, glmer_Florescence _50)

# We redo the model on 10m with all data available for those fixed variables. glmer_Florescence_10_best <- glmer(Florescence ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) +

I(H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = env_spat, f amily = "poisson") overdisp.glmer(glmer_Florescence_10_best) # ratio 0.953 à ok. summary(glmer_Florescence_10_best)

# Predictions for P newdf_predicted_Fl_P10 <- data.frame(Florescence = NA, H_10 = NA, P_10 = rep(seq(0, 1, 0.001), 3)) length_Fl_P10 <- nrow(newdf_predicted_Fl_P10) newdf_predicted_Fl_P10$H_10[1:length_Fl_P10/3] <- 0.25 newdf_predicted_Fl_P10$H_10[(length_Fl_P10/3+1):(2*length_Fl_P10/3)] <- 0.5 newdf_predicted_Fl_P10$H_10[(2*length_Fl_P10/3+1):length_Fl_P10] <- 0.75 newdf_predicted_Fl_P10$Florescence <- predict(glmer_Florescence_10_best, newdata = new df_predicted_Fl_P10, re.form = NA, type = "response") newdf_predicted_Fl_P10$H_10 <- as.factor(as.character(newdf_predicted_Fl_P10$H_10)) plot_predicted_Fl_P10 <- ggplot(data = env_spat, aes(x = P_10, y = Florescence)) + geo m_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_Fl_P10, aes(x = P_10, y = Florescence, colour = H _10)) plot_predicted_Fl_P10 + labs(title = "Fitted values of Florescence in function of\nmar ram proportion (P) at 10m radius scale", x = "Proportion of marram (P)", y = "Categori cal amount of florescence", colour = "Aggregation\nof marram (H)") + theme(plot.title = element_text(face = "bold"))

# Decide what is the best x-range for H lines --> 0.25-1 plot(env_spat$H_10, env_spat$Florescence) # Predictions for H newdf_predicted_Fl_H10 <- data.frame(Florescence = NA, P_10 = NA, H_10 = rep(seq(0.2, 1, 0.001), 3)) length_Fl_H10 <- nrow(newdf_predicted_Fl_H10) newdf_predicted_Fl_H10$P_10[1:length_Fl_H10/3] <- 0.25 newdf_predicted_Fl_H10$P_10[(length_Fl_H10/3+1):(2*length_Fl_H10/3)] <- 0.5 newdf_predicted_Fl_H10$P_10[(2*length_Fl_H10/3+1):length_Fl_H10] <- 0.75 newdf_predicted_Fl_H10$Florescence <- predict(glmer_Florescence_10_best, newdata = new df_predicted_Fl_H10, re.form = NA, type = "response") newdf_predicted_Fl_H10$P_10 <- as.factor(as.character(newdf_predicted_Fl_H10$P_10)) plot_predicted_Fl_H10 <- ggplot(data = env_spat, aes(x = H_10, y = Florescence)) + geo m_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_Fl_H10, aes(x = H_10, y = Florescence, colour = P _10)) plot_predicted_Fl_H10 + labs(title = "Fitted values of Florescence in function of\nmar ram aggregation (H) at 10m radius scale", x = "Aggregation of marram (H)", y = "Catego rical amount of florescence", colour = "Proportion\nof marram (P)") + theme(plot.title = element_text(face = "bold"))

#### VITALITY ####

# Check for overdispersion mean(na.omit(env_spat$Vitality)) ## [1] 2.896875 var(na.omit(env_spat$Vitality)) ## [1] 0.7060935 glmer_Vitality_5 <- glmer(Vitality ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5 ^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = env_spat_no_NA, family = "poisso n")

glmer_Vitality_10 <- glmer(Vitality ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = env_spat_no_NA, famil y = "poisson") glmer_Vitality_20 <- glmer(Vitality ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = env_spat_no_NA, famil y = "poisson") glmer_Vitality_50 <- glmer(Vitality ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = env_spat_no_NA, famil y = "poisson")

# Choose the best model for Vitality AIC(glmer_Vitality_5, glmer_Vitality_10, glmer_Vitality_20, glmer_Vitality_50) # We redo the model on 50m with all data glmer_Vitality_50_best <- glmer(Vitality ~ H_50 + (1|Country:Location), data = env_spa t, family = "poisson") # P_50 + I(P_50^2) + I(H_50^2) + P_50*H_50 + P_50*I(H_50^2) + I (P_50^2)*H_50

# Decide what is the best x-range --> 0.1-1 plot(env_spat$H_50, env_spat$Vitality) # Predications for H newdf_predicted_Vi_H50 <- data.frame(Vitality = NA, H_50 = seq(0.15, 1, 0.001)) newdf_predicted_Vi_H50$Vitality <- predict(glmer_Vitality_50_best, newdata = newdf_pre dicted_Vi_H50, re.form = NA, type = "response") plot_predicted_Vi_H50 <- ggplot(data = env_spat, aes(x = H_50, y = Vitality)) + geom_p oint(colour = "darkgrey") + geom_smooth(data = newdf_predicted_Vi_H50, aes(x = H_50, y = Vitality), colour = "bl ue") plot_predicted_Vi_H50 + labs(title = "Fitted values of vitality in function\nof marram aggregation (H) at 50m radius scale", x = "Aggregation of marram (H)", y = "Categorica l vitality") + theme(plot.title = element_text(face = "bold"))

#### LENGTH ####

# Skewness indicates it is approximately symmetric (0.3214499 < 0.5) skewness(env_spat_no_NA$Length, na.rm = TRUE) ## [1] 0.3214499

# Models model_Length_5 <- lmer(Length ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^2)*H _5 + P_5*I(H_5^2) + (1|Country:Location), data = env_spat_no_NA, REML = FALSE) plot(model_Length_5) qqnorm(resid(model_Length_5)) qqline(resid(model_Length_5)) hist(resid(model_Length_5)) # Assumptions OK

model_Length_10 <- lmer(Length ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P _10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = env_spat_no_NA, REML = FAL SE) # Assumptions OK model_Length_20 <- lmer(Length ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20^2) + I(P _20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = env_spat_no_NA, REML = FAL SE) # Assumptions OK model_Length_50 <- lmer(Length ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50^2) + I(P _50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = env_spat_no_NA, REML = FAL SE) # Assumptions OK

# Choose the best model for Length AIC(model_Length_5, model_Length_10, model_Length_20, model_Length_50)

# We redo the model on 10m with all data available for those fixed variables. model_Length_10_best <- lmer(Length ~ P_10 + H_10 + I(P_10^2) + I(H_10^2) + (1|Country :Location), data = env_spat, REML = FALSE) # + P_10*H_10 + I(P_10^2)*H_10 + P_10*I(H_1 0^2) plot(model_Length_10_best) qqnorm(resid(model_Length_10_best)) qqline(resid(model_Length_10_best)) hist(resid(model_Length_10_best)) # Assumptions OK summary(model_Length_10_best)

# Scale for H --> 0-1 plot(env_spat$H_10, env_spat$Length) # Predictions for H newdf_predicted_Le_H10 <- data.frame(Length = NA, P_10 = 0.5, H_10 = seq(0, 1, 0.001)) newdf_predicted_Le_H10$Length <- predict(model_Length_10_best, newdata = newdf_predict ed_Le_H10, re.form = NA) plot_predicted_Le_H10 <- ggplot(data = env_spat, aes(x = H_10, y = Length)) + geom_poi nt(colour = "darkgrey") + geom_smooth(data = newdf_predicted_Le_H10, aes(x = H_10, y = Length), colour = "red" ) plot_predicted_Le_H10 + labs(title = "Fitted values of length in function\nof marram a ggregation (H) at 10m radius scale", x = "Aggregation of marram (H)", y = "Length of m arram (cm)") + theme(plot.title = element_text(face = "bold"))

# Predictions for P newdf_predicted_Le_P10 <- data.frame(Length =NA, H_10 = 0.5, P_10 = seq(0, 1, 0.001)) newdf_predicted_Le_P10$Length <- predict(model_Length_10_best, newdata = newdf_predict ed_Le_P10, re.form = NA) plot_predicted_Le_P10 <- ggplot(data = env_spat, aes(x = P_10, y = Length)) + geom_poi nt(colour = "darkgrey") + geom_smooth(data = newdf_predicted_Le_P10, aes(x = P_10, y = Length), colour = "red" ) plot_predicted_Le_P10 + labs(title = "Fitted values of length in function\nof marram p roportion (P) at 10m radius scale", x = "Proportion of marram (P)", y = "Length of mar ram (cm)") + theme(plot.title = element_text(face = "bold"))

################################### ## MODELS FOR DIVERSITY MEASURES ## ###################################

# Make new datasets without NA's so that the same for each model alpha_no_NA <- alpha alpha_no_NA <- alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_20, P_5, H_5, P_50, H_50) generalists_alpha_no_NA <- generalists_alpha generalists_alpha_no_NA <- generalists_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_20, P_5, H_5, P_50, H_50) specialists_D_alpha_no_NA <- specialists_D_alpha specialists_D_alpha_no_NA <- specialists_D_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H _20, P_5, H_5, P_50, H_50) specialists_M_alpha_no_NA <- specialists_M_alpha specialists_M_alpha_no_NA <- specialists_M_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H _20, P_5, H_5, P_50, H_50)

#### RICHNESS ####

# General species (G)

# skewness skewness(generalists_alpha_no_NA$richness, na.rm = TRUE) ## [1] 1.089782 # Right skewed so, we will transform with sqrt generalists_alpha_no_NA$richness_tr <- sqrt(generalists_alpha_no_NA$richness) skewness(generalists_alpha_no_NA$richness_tr, na.rm = TRUE) ## [1] 0.2883896

# Models model_richness_5_G <- lmer(richness_tr ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I (P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = generalists_alpha_no_NA, REM L = FALSE) # Assumptions OK model_richness_10_G <- lmer(richness_tr ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10 ^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = generalists_alpha _no_NA, REML = FALSE) # Assumptions OK model_richness_20_G <- lmer(richness_tr ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20 ^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = generalists_alpha _no_NA, REML = FALSE) # Assumptions OK model_richness_50_G <- lmer(richness_tr ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50 ^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = generalists_alpha _no_NA, REML = FALSE) # Assumptions OK

# Choose the best model for richness AIC(model_richness_5_G, model_richness_10_G,model_richness_20_G, model_richness_50_G)

# skewness skewness(generalists_alpha$richness, na.rm = TRUE) ## [1] 1.048844 # Right skewed so, we will transform with sqrt generalists_alpha$richness_tr <- sqrt(generalists_alpha$richness)

skewness(generalists_alpha$richness_tr, na.rm = TRUE) ## [1] 0.2655672

# We redo the model on 50m with all data available for those fixed variables. model_richness_50_G_best <- lmer(richness_tr ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I (H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = generalists_ alpha, REML = FALSE) # plot(model_richness_50_G_best) qqnorm(resid(model_richness_50_G_best)) qqline(resid(model_richness_50_G_best)) hist(resid(model_richness_50_G_best)) # Assumptions OK summary(model_richness_50_G_best)

# Decide what is the best x-range --> 0.2-1 plot(generalists_alpha$H_50, generalists_alpha$richness) plot(generalists_alpha$P_50, generalists_alpha$richness)

# Predictions for P newdf_predicted_rich_P50 <- data.frame(richness = NA, H_50 = NA, P_50 = rep(seq(0, 0.9 , 0.001), 3)) length_rich_P50 <- nrow(newdf_predicted_rich_P50) newdf_predicted_rich_P50$H_50[1:length_rich_P50/3] <- 0.25 newdf_predicted_rich_P50$H_50[(length_rich_P50/3+1):(2*length_rich_P50/3)] <- 0.50 newdf_predicted_rich_P50$H_50[(2*length_rich_P50/3+1):length_rich_P50] <- 0.75 newdf_predicted_rich_P50$richness <- predict(model_richness_50_G_best, newdata = newdf _predicted_rich_P50, re.form = NA) newdf_predicted_rich_P50$H_50 <- as.factor(as.character(newdf_predicted_rich_P50$H_50) ) newdf_predicted_rich_P50$richness <- (newdf_predicted_rich_P50$richness)^2 plot_predicted_rich_P50 <- ggplot(data = generalists_alpha, aes(x = P_50, y = richness )) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_rich_P50, aes(x = P_50, y = richness, linetype = H_50), colour = "black") plot_predicted_rich_P50 + labs(title = "Fitted values of richness for general species\ nin function of marram proportion (P) at 50m radius scale", x = "Proportion of marram (P)", y = "General species richness", linetype = "Aggregation\nof marram (H)") + theme (plot.title = element_text(face = "bold")) + xlim(0, 1)

# Predictions for H newdf_predicted_rich_H50 <- data.frame(richness = NA, P_50 = NA, H_50 = rep(seq(0, 1, 0.001), 3)) length_rich_H50 <- nrow(newdf_predicted_rich_H50) newdf_predicted_rich_H50$P_50[1:length_rich_H50/3] <- 0.25 newdf_predicted_rich_H50$P_50[(length_rich_H50/3+1):(2*length_rich_H50/3)] <- 0.5 newdf_predicted_rich_H50$P_50[(2*length_rich_H50/3+1):length_rich_H50] <- 0.75 newdf_predicted_rich_H50$richness <- predict(model_richness_50_G_best, newdata = newdf _predicted_rich_H50, re.form = NA) newdf_predicted_rich_H50$P_50 <- as.factor(as.character(newdf_predicted_rich_H50$P_50) ) newdf_predicted_rich_H50$richness <- (newdf_predicted_rich_H50$richness)^2 plot_predicted_rich_H50 <- ggplot(data = generalists_alpha, aes(x = H_50, y = richness )) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_rich_H50, aes(x = H_50, y = richness, linetype = P_50), colour = "black") plot_predicted_rich_H50 + labs(title = "Fitted values of richness for general species\ nin function of marram aggregation (H) at 50m radius scale", x = "Aggregation of marra

m (H)", y = "General species richness", linetype = "Proportion\nof marram (P)") + theme(plot.title = element_text(face = "bold")) + xlim (0, 1)

# For D specialists

# skewness skewness(specialists_D_alpha_no_NA$richness, na.rm = TRUE) ## [1] 1.218321 # Right skewed so, we will transform with sqrt specialists_D_alpha_no_NA$richness_tr <- sqrt(specialists_D_alpha_no_NA$richness) skewness(specialists_D_alpha_no_NA$richness_tr, na.rm = TRUE) ## [1] 0.5737264

# models model_richness_5_D <- lmer(richness_tr ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I (P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = specialists_D_alpha_no_NA, R EML = FALSE) # Assumptions OK model_richness_10_D <- lmer(richness_tr ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10 ^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = specialists_D_alp ha_no_NA, REML = FALSE) # Assumptions OK model_richness_20_D <- lmer(richness_tr ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20 ^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = specialists_D_alp ha_no_NA, REML = FALSE) # Assumptions OK model_richness_50_D <- lmer(richness_tr ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50 ^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = specialists_D_alp ha_no_NA, REML = FALSE) # Assumptions OK

# Choose the best model for richness AIC(model_richness_5_D,model_richness_10_D, model_richness_20_D, model_richness_50_D)

# skewness skewness(specialists_D_alpha$richness, na.rm = TRUE) ## [1] 1.251853 # Right skewed so, we will transform with sqrt specialists_D_alpha$richness_tr <- log(specialists_D_alpha$richness) skewness(specialists_D_alpha$richness_tr, na.rm = TRUE) ## [1] 0.1045493

# We redo the model on 5m with all data available for those fixed variables. model_richness_5_D_best <- lmer(richness_tr ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2 ) + (1|Country:Location), data = specialists_D_alpha, REML = FALSE) # + P_5*I(H_5^2) + I(P_5^2)*H_5 plot(model_richness_5_D_best) qqnorm(resid(model_richness_5_D_best)) qqline(resid(model_richness_5_D_best)) hist(resid(model_richness_5_D_best)) # Assumptions OK summary(model_richness_5_D_best)

## We will only look at the plot for P at the scale of 5m. plot(specialists_D_alpha$H_5, specialists_D_alpha$richness) plot(specialists_D_alpha$P_5, specialists_D_alpha$richness)

# Predictions for P newdf_predicted_rich_P5_D <- data.frame(richness = NA, H_5 = NA, P_5 = rep(seq(0, 1, 0 .001), 3)) length_rich_P5_D <- nrow(newdf_predicted_rich_P5_D) newdf_predicted_rich_P5_D$H_5[1:length_rich_P5_D/3] <- 0.25 newdf_predicted_rich_P5_D$H_5[(length_rich_P5_D/3+1):(2*length_rich_P5_D/3)] <- 0.50 newdf_predicted_rich_P5_D$H_5[(2*length_rich_P5_D/3+1):length_rich_P5_D] <- 0.75 newdf_predicted_rich_P5_D$richness <- predict(model_richness_5_D_best, newdata = newdf _predicted_rich_P5_D, re.form = NA) newdf_predicted_rich_P5_D$H_5<-as.factor(as.character(newdf_predicted_rich_P5_D$H_5)) newdf_predicted_rich_P5_D$richness <- exp(newdf_predicted_rich_P5_D$richness) plot_predicted_rich_P5_D <- ggplot(data = specialists_D_alpha, aes(x = P_5, y = richne ss)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_rich_P5_D, aes(x = P_5, y = richness, linetype = H_5), colour = "orange") plot_predicted_rich_P5_D + labs(title = "Fitted values of richness for dune-specific s pecies\nin function of marram proportion (P) at 5m radius scale", x = "Proportion of m arram (P)", y = "Species richness", linetype = "Aggregation\nof marram (H)") + theme(p lot.title = element_text(face = "bold"))

# Predictions for H newdf_predicted_rich_H5_D <- data.frame(richness = NA, P_5 = NA, H_5 = rep(seq(0.3, 1, 0.001), 3)) length_rich_H5_D <- nrow(newdf_predicted_rich_H5_D) newdf_predicted_rich_H5_D$P_5[1:length_rich_H5_D/3] <- 0.25 newdf_predicted_rich_H5_D$P_5[(length_rich_H5_D/3+1):(2*length_rich_H5_D/3)] <- 0.50 newdf_predicted_rich_H5_D$P_5[(2*length_rich_H5_D/3+1):length_rich_H5_D] <- 0.75 newdf_predicted_rich_H5_D$richness <- predict(model_richness_5_D_best, newdata = newdf _predicted_rich_H5_D, re.form = NA) newdf_predicted_rich_H5_D$P_5<-as.factor(as.character(newdf_predicted_rich_H5_D$P_5)) newdf_predicted_rich_H5_D$richness <- exp(newdf_predicted_rich_H5_D$richness) plot_predicted_rich_H5_D <- ggplot(data = specialists_D_alpha, aes(x = H_5, y = richne ss)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_rich_H5_D, aes(x = H_5, y = richness, linetype = P_5), colour = "orange") plot_predicted_rich_H5_D + labs(title = "Fitted values of richness for dune-specific s pecies\nin function of marram aggregation (H) at 5m radius scale", x = "Aggregation of marram (H)", y = "Species richness", linetype = "Proportion\nof marram (P)")+ theme(pl ot.title = element_text(face = "bold"))

# Marram species

# skewness skewness(specialists_M_alpha_no_NA$richness, na.rm = TRUE) ## [1] 0.9520364 # Right skewed so, we will transform with sqrt specialists_M_alpha_no_NA$richness_tr <- sqrt(specialists_M_alpha_no_NA$richness) skewness(specialists_M_alpha_no_NA$richness_tr, na.rm = TRUE) ## [1] 0.436489 model_richness_5_M <- lmer(richness_tr ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I (P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = specialists_M_alpha_no_NA, R EML = FALSE) # Assumptions OK model_richness_10_M <- lmer(richness_tr ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10 ^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = specialists_M_alp

ha_no_NA, REML = FALSE) # Assumptions OK model_richness_20_M <- lmer(richness_tr ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20 ^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = specialists_M_alp ha_no_NA, REML = FALSE) # Assumptions OK model_richness_50_M <- lmer(richness_tr ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50 ^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = specialists_M_alp ha_no_NA, REML = FALSE) # Assumptions OK

# Choose the best model for richness AIC(model_richness_5_M, model_richness_10_M, model_richness_20_M,model_richness_50_M)

# skewness skewness(specialists_M_alpha$richness, na.rm = TRUE) ## [1] 0.9874244 # Right skewed so, we will transform with sqrt specialists_M_alpha$richness_tr <- sqrt(specialists_M_alpha$richness) skewness(specialists_M_alpha$richness_tr, na.rm = TRUE) ## [1] 0.4517116

# We redo the model on 50m with all data available for those fixed variables. model_richness_50_M_best <- lmer(richness_tr ~ P_50 + (1|Country:Location), data = spe cialists_M_alpha, REML = FALSE) # + H_50 + I(P_50^2) + I(H_50^2) + P_50*H_50 + P_50*I( H_50^2) + I(P_50^2)*H_50 plot(model_richness_50_M_best) qqnorm(resid(model_richness_50_M_best)) qqline(resid(model_richness_50_M_best)) hist(resid(model_richness_50_M_best)) # Assumptions OK summary(model_richness_50_M_best) # No significant effect

#### SHANNON ####

# General species (G)

# skewness skewness(generalists_alpha_no_NA$shannon, na.rm = TRUE) ## [1] -0.5450969

# Models model_shannon_5_G <- lmer(shannon ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^ 2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = generalists_alpha_no_NA, REML = F ALSE) # Assumptions OK model_shannon_10_G <- lmer(shannon ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = generalists_alpha_no_NA , REML = FALSE) # Assumptions OK model_shannon_20_G <- lmer(shannon ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = generalists_alpha_no_NA , REML = FALSE) # Assumptions OK model_shannon_50_G <- lmer(shannon ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = generalists_alpha_no_NA

, REML = FALSE) # Assumptions OK

# Choose the best model for richness AIC(model_shannon_5_G, model_shannon_10_G, model_shannon_20_G, model_shannon_50_G) # skewness skewness(generalists_alpha$shannon, na.rm = TRUE) ## [1] -0.5470432

# We redo the model on 50m with all data available for those fixed variables. model_shannon_50_G_best <- lmer(shannon ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50 ^2) + I(P_50^2)*H_50 + (1|Country:Location), data = generalists_alpha, REML = FALSE) # + P_50*I(H_50^2) plot(model_shannon_50_G_best) qqnorm(resid(model_shannon_50_G_best)) qqline(resid(model_shannon_50_G_best)) hist(resid(model_shannon_50_G_best)) # Assumptions OK summary(model_shannon_50_G_best)

# Decide what is the best x-range --> 0.2-1 plot(generalists_alpha$H_50, generalists_alpha$shannon) plot(generalists_alpha$P_50, generalists_alpha$shannon)

# Predictions for P newdf_predicted_shan_P50 <- data.frame(richness = NA, H_50 = NA, P_50 = rep(seq(0, 0.9 , 0.001), 3)) length_shan_P50 <- nrow(newdf_predicted_shan_P50) newdf_predicted_shan_P50$H_50[1:length_shan_P50/3] <- 0.25 newdf_predicted_shan_P50$H_50[(length_shan_P50/3+1):(2*length_shan_P50/3)] <- 0.50 newdf_predicted_shan_P50$H_50[(2*length_shan_P50/3+1):length_shan_P50] <- 0.75 newdf_predicted_shan_P50$shannon <- predict(model_shannon_50_G_best, newdata = newdf_p redicted_shan_P50, re.form = NA) newdf_predicted_shan_P50$H_50<-as.factor(as.character(newdf_predicted_shan_P50$H_50)) plot_predicted_shan_P50 <- ggplot(data = generalists_alpha, aes(x = P_50, y = shannon) ) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_shan_P50, aes(x = P_50, y = shannon, linetype = H _50), colour = "black") plot_predicted_shan_P50 + labs(title = "Fitted values of Shannon's index for general s pecies\nin function of marram proportion (P) at 50m radius scale", x = "Proportion of marram (P)", y = "Shannon's index", linetype = "Aggregation\nof marram (H)") + theme(p lot.title = element_text(face = "bold"))

# Dune species

# skewness skewness(specialists_D_alpha_no_NA$shannon, na.rm = TRUE) ## [1] 0.2056058

# Models model_shannon_5_D <- lmer(shannon ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^ 2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = specialists_D_alpha_no_NA, REML = FALSE) # Assumptions OK model_shannon_10_D <- lmer(shannon ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = specialists_D_alpha_no_ NA, REML = FALSE)

100

# Assumptions OK model_shannon_20_D <- lmer(shannon ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = specialists_D_alpha_no_ NA, REML = FALSE) # Assumptions OK model_shannon_50_D <- lmer(shannon ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = specialists_D_alpha_no_ NA, REML = FALSE) # Assumptions OK

# Choose the best model for richness AIC(model_shannon_5_D, model_shannon_10_D, model_shannon_20_D, model_shannon_50_D) # skewness skewness(specialists_D_alpha$shannon, na.rm = TRUE) ## [1] 0.230894

# We redo the model on 5m with all data available for those fixed variables. model_shannon_5_D_best <- lmer(shannon ~ P_5 + H_5 + P_5*H_5 + I(H_5^2) + (1|Country:L ocation), data = specialists_D_alpha, REML = FALSE) # + I(P_5^2) + I(P_5^2)*H_5 + P_5* I(H_5^2) plot(model_shannon_5_D_best) qqnorm(resid(model_shannon_5_D_best)) qqline(resid(model_shannon_5_D_best)) hist(resid(model_shannon_5_D_best)) # Assumptions OK summary(model_shannon_5_D_best)

## Look at range --> 0.3-1 plot(specialists_D_alpha$H_5, specialists_D_alpha$shannon) plot(specialists_D_alpha$P_5, specialists_D_alpha$shannon)

# Predictions for P newdf_predicted_shan_P5_D <- data.frame(shannon = NA, H_5 = NA, P_5 = rep(seq(0, 1, 0. 001), 3)) length_shan_P5_D <- nrow(newdf_predicted_shan_P5_D) newdf_predicted_shan_P5_D$H_5[1:length_shan_P5_D/3] <- 0.25 newdf_predicted_shan_P5_D$H_5[(length_shan_P5_D/3+1):(2*length_shan_P5_D/3)] <- 0.5 newdf_predicted_shan_P5_D$H_5[(2*length_shan_P5_D/3+1):length_shan_P5_D] <- 0.75 newdf_predicted_shan_P5_D$shannon <- predict(model_shannon_5_D_best, newdata = newdf_p redicted_shan_P5_D, re.form = NA) newdf_predicted_shan_P5_D$H_5 <- as.factor(as.character(newdf_predicted_shan_P5_D$H_5) ) plot_predicted_shan_P5_D <- ggplot(data = specialists_D_alpha, aes(x = P_5, y = shanno n)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_shan_P5_D, aes(x = P_5, y = shannon, linetype = H _5), colour = "orange") plot_predicted_shan_P5_D + labs(title = "Fitted values of Shannon's index for dune-spe cific species\nin function of marram proportion (P) at 5m radius scale", x = "Proporti on of marram (P)", y = "Shannon's index", linetype = "Aggregation\nof marram (H)") + t heme(plot.title = element_text(face = "bold"))

# Predictions for H newdf_predicted_shan_H5_D <- data.frame(shannon = NA, P_5 = NA, H_5 = rep(seq(0.3, 1, 0.001), 3)) length_shan_H5_D <- nrow(newdf_predicted_shan_H5_D) newdf_predicted_shan_H5_D$P_5[1:length_shan_H5_D/3] <- 0.25

101

newdf_predicted_shan_H5_D$P_5[(length_shan_H5_D/3+1):(2*length_shan_H5_D/3)] <- 0.5 newdf_predicted_shan_H5_D$P_5[(2*length_shan_H5_D/3+1):length_shan_H5_D] <- 0.75 newdf_predicted_shan_H5_D$shannon <- predict(model_shannon_5_D_best, newdata = newdf_p redicted_shan_H5_D, re.form = NA) newdf_predicted_shan_H5_D$P_5<-as.factor(as.character(newdf_predicted_shan_H5_D$P_5)) plot_predicted_shan_H5_D <- ggplot(data = specialists_D_alpha, aes(x = H_5, y = shanno n)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_shan_H5_D, aes(x = H_5, y = shannon, linetype = P _5), colour = "orange") plot_predicted_shan_H5_D + labs(title = "Fitted values of Shannon's index for dune-spe cific species\nin function of marram aggregation (H) at 5m radius scale", x = "Aggrega tion of marram (H)", y = "Shannon's index", linetype = "Proportion\nof marram (P)") + theme(plot.title = element_text(face = "bold"))

# Marram species

# skewness skewness(specialists_M_alpha_no_NA$shannon, na.rm = TRUE) ## [1] 0.1237683

# Models model_shannon_5_M <- lmer(shannon ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^ 2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = specialists_M_alpha_no_NA, REML = FALSE) # Assumptions OK model_shannon_10_M <- lmer(shannon ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = specialists_M_alpha_no_ NA, REML = FALSE) # Assumptions OK model_shannon_20_M <- lmer(shannon ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = specialists_M_alpha_no_ NA, REML = FALSE) # Assumptions OK model_shannon_50_M <- lmer(shannon ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = specialists_M_alpha_no_ NA, REML = FALSE) # Assumptions OK

# Choose the best model for richness AIC(model_shannon_5_M, model_shannon_10_M, model_shannon_20_M, model_shannon_50_M) # skewness skewness(specialists_M_alpha$shannon, na.rm = TRUE) ## [1] 0.1334384

# We redo the model on 50m with all data available for those fixed variables. model_shannon_50_M_best <- lmer(shannon ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50 ^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = specialists_M_alp ha, REML = FALSE) # plot(model_shannon_50_M_best) qqnorm(resid(model_shannon_50_M_best)) qqline(resid(model_shannon_50_M_best)) hist(resid(model_shannon_50_M_best)) # Assumptions OK summary(model_shannon_50_M_best)

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## Look at range --> 0.3-1 plot(specialists_M_alpha$H_50, specialists_M_alpha$shannon) plot(specialists_M_alpha$P_50, specialists_M_alpha$shannon)

# Predictions for P newdf_predicted_shan_P50_M <- data.frame(shannon = NA, H_50 = NA, P_50 = rep(seq(0, 0. 9, 0.001), 3)) length_shan_P50_M <- nrow(newdf_predicted_shan_P50_M) newdf_predicted_shan_P50_M$H_50[1:length_shan_P50_M/3] <- 0.25 newdf_predicted_shan_P50_M$H_50[(length_shan_P50_M/3+1):(2*length_shan_P50_M/3)]<-0.5 newdf_predicted_shan_P50_M$H_50[(2*length_shan_P50_M/3+1):length_shan_P50_M] <- 0.75 newdf_predicted_shan_P50_M$shannon <- predict(model_shannon_50_M_best, newdata = newdf _predicted_shan_P50_M, re.form = NA) newdf_predicted_shan_P50_M$H_50 <- as.factor(as.character(newdf_predicted_shan_P50_M$H _50)) plot_predicted_shan_P50_M <- ggplot(data = specialists_M_alpha, aes(x = P_50, y = shan non)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_shan_P50_M, aes(x = P_50, y = shannon, linetype = H_50), colour = "green") plot_predicted_shan_P50_M + labs(title = "Fitted values of Shannon's index for marram- specific species\nin function of marram proportion (P) at 50m radius scale", x = "Prop ortion of marram (P)", y = "Shannon's index", linetype = "Aggregation\nof marram (H)") + theme(plot.title = element_text(face = "bold")) + xlim(0, 1)

# Predictions for H newdf_predicted_shan_H50_M <- data.frame(shannon = NA, P_50 = NA, H_50 = rep(seq(0.1, 1, 0.001), 3)) length_shan_H50_M <- nrow(newdf_predicted_shan_H50_M) newdf_predicted_shan_H50_M$P_50[1:length_shan_H50_M/3] <- 0.25 newdf_predicted_shan_H50_M$P_50[(length_shan_H50_M/3+1):(2*length_shan_H50_M/3)]<-0.5 newdf_predicted_shan_H50_M$P_50[(2*length_shan_H50_M/3+1):length_shan_H50_M] <- 0.75 newdf_predicted_shan_H50_M$shannon <- predict(model_shannon_50_M_best, newdata = newdf _predicted_shan_H50_M, re.form = NA) newdf_predicted_shan_H50_M$P_50 <- as.factor(as.character(newdf_predicted_shan_H50_M$P _50)) plot_predicted_shan_H50_M <- ggplot(data = specialists_M_alpha, aes(x = H_50, y = shan non)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_shan_H50_M, aes(x = H_50, y = shannon, linetype = P_50), colour = "green") plot_predicted_shan_H50_M + labs(title = "Fitted values of Shannon's index for marram- specific species\nin function of marram aggregation (H) at 50m radius scale", x = "Agg regation of marram (H)", y = "Shannon's index", linetype = "Proportion\nof marram (P)" ) + theme(plot.title = element_text(face = "bold"))

################################### #### MODELS FOR SPECIFIC GROUPS ### ###################################

# Make new datasets without NA's so that the same for each model coleoptera_G_alpha_no_NA <- coleoptera_G_alpha coleoptera_G_alpha_no_NA <- coleoptera_G_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_2 0, P_5, H_5, P_50, H_50) coleoptera_D_alpha_no_NA <- coleoptera_D_alpha coleoptera_D_alpha_no_NA <- coleoptera_D_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_2 0, P_5, H_5, P_50, H_50) coleoptera_M_alpha_no_NA <- coleoptera_M_alpha

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coleoptera_M_alpha_no_NA <- coleoptera_M_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_2 0, P_5, H_5, P_50, H_50) arachnida_G_alpha_no_NA <- arachnida_G_alpha arachnida_G_alpha_no_NA <- arachnida_G_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_20, P_5, H_5, P_50, H_50) arachnida_D_alpha_no_NA <- arachnida_D_alpha arachnida_D_alpha_no_NA <- arachnida_D_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_20, P_5, H_5, P_50, H_50) arachnida_M_alpha_no_NA <- arachnida_M_alpha arachnida_M_alpha_no_NA <- arachnida_M_alpha_no_NA %>% drop_na(P_10, H_10, P_20, H_20, P_5, H_5, P_50, H_50)

#### Coleoptera (Insecta) ####

# General species (G)

# Check for overdispersion (count data, so glmer is the better choice) mean(na.omit(coleoptera_G_alpha$abundance)) ## [1] 3.603509 var(na.omit(coleoptera_G_alpha$abundance)) ## [1] 22.7683 model_coleoptera_G_5 <- glmer.nb(abundance ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = coleoptera_G_alpha_no_NA) model_coleoptera_G_10 <- glmer.nb(abundance ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I( H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = coleoptera_G_ alpha_no_NA) model_coleoptera_G_20 <- glmer.nb(abundance ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I( H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = coleoptera_G_ alpha_no_NA) model_coleoptera_G_50 <- glmer.nb(abundance ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I( H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = coleoptera_G_ alpha_no_NA)

# Choose the best model for Coleoptera abundance AIC(model_coleoptera_G_5, model_coleoptera_G_10, model_coleoptera_G_20, model_coleopte ra_G_50)

# We redo the model on 10m with all data available for those fixed variables. model_coleoptera_G_10_best <- glmer.nb(abundance ~ P_10 + H_10 + I(H_10^2) + (1|Countr y:Location), data = coleoptera_G_alpha) # + P_10*H_10 + I(P_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) summary(model_coleoptera_G_10_best)

# Predictions for P newdf_predicted_coleoptera_G_P10 <- data.frame(abundance = NA, H_10 = 0.5, P_10 = seq( 0, 1, 0.001)) newdf_predicted_coleoptera_G_P10$abundance <- predict(model_coleoptera_G_10_best, newd ata = newdf_predicted_coleoptera_G_P10, re.form = NA, type = "response") coleoptera_G_alpha <- coleoptera_G_alpha[-c(153, 204),] plot_predicted_coleoptera_G_P10 <- ggplot(data = coleoptera_G_alpha, aes(x = P_10, y = abundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_coleoptera_G_P10, aes(x = P_10, y = abundance), c olour = "black") plot_predicted_coleoptera_G_P10 + labs(title = "Fitted values of general Coleoptora ab undance\nin function of marram proportion (P) at 10m radius scale", x = "Proportion of

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marram (P)", y = "General Coleoptera abundance") + theme(plot.title = element_text(face = "bold"))

# Decide what is the best x-range for H lines --> 0.25-1 plot(coleoptera_G_alpha$H_10, coleoptera_G_alpha$abundance) # Predictions for H newdf_predicted_coleoptera_G_H10 <- data.frame(abundance = NA, P_10 = 0.5, H_10 = seq( 0.1, 1, 0.001)) newdf_predicted_coleoptera_G_H10$abundance <- predict(model_coleoptera_G_10_best, newd ata = newdf_predicted_coleoptera_G_H10, re.form = NA, type = "response") coleoptera_G_alpha <- coleoptera_G_alpha[-c(153, 204),] plot_predicted_coleoptera_G_H10 <- ggplot(data = coleoptera_G_alpha, aes(x = H_10, y = abundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_coleoptera_G_H10, aes(x = H_10, y = abundance), c olour = "black") plot_predicted_coleoptera_G_H10 + labs(title = "Fitted values of general Coleoptora ab undance\nin function of marram aggregation (H) at 10m radius scale", x = "Aggregation of marram (H)", y = "General Coleoptera abundance") + theme(plot.title = element_text(face = "bold"))

# Dune species (D) skewness(coleoptera_D_alpha$abundance) # Too large skewness ## [1] 5.755167 # Check for overdispersion mean(na.omit(coleoptera_D_alpha$abundance)) ## [1] 1.568421 var(na.omit(coleoptera_D_alpha$abundance)) ## [1] 2.503247 model_coleoptera_D_5 <- glmer.nb(abundance ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = coleoptera_D_alpha_no_NA) model_coleoptera_D_10 <- glmer.nb(abundance ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I( H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = coleoptera_D_ alpha_no_NA) model_coleoptera_D_20 <- glmer.nb(abundance ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I( H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = coleoptera_D_ alpha_no_NA) model_coleoptera_D_50 <- glmer.nb(abundance ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I( H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = coleoptera_D_ alpha_no_NA)

# Choose the best model for Coleoptera abundance AIC(model_coleoptera_D_5, model_coleoptera_D_10, model_coleoptera_D_20, model_coleopte ra_D_50)

# We redo the model on 10m with all data available for those fixed variables. model_coleoptera_D_10_best <- glmer.nb(abundance ~ H_10 + (1|Country:Location), data = coleoptera_D_alpha) # + I(H_10^2) + P_10 + + P_10*H_10 + I(P_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) summary(model_coleoptera_D_10_best)

# Decide what is the best x-range for H lines --> 0.5-1 plot(coleoptera_D_alpha$H_10, coleoptera_D_alpha$abundance) # Predictions for H newdf_predicted_coleoptera_D_H10<-data.frame(abundance= NA, H_10 = seq(0.5, 1, 0.001))

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newdf_predicted_coleoptera_D_H10$abundance <- predict(model_coleoptera_D_10_best, newd ata = newdf_predicted_coleoptera_D_H10, re.form = NA, type = "response") plot_predicted_coleoptera_D_H10 <- ggplot(data = coleoptera_D_alpha, aes(x = H_10, y = abundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_coleoptera_D_H10, aes(x = H_10, y = abundance), c olour = "orange") plot_predicted_coleoptera_D_H10 + labs(title = "Fitted values of dune-specific Coleopt ora abundance\nin function of marram aggregation (H) at 10m radius scale", x = "Aggreg ation of marram (H)", y = "Dune-specific Coleoptera abundance") + theme(plot.title = e lement_text(face = "bold"))

# Marram species (M)

# Check for overdispersion mean(na.omit(coleoptera_M_alpha$abundance)) ## [1] 1.619048 var(na.omit(coleoptera_M_alpha$abundance)) ## [1] 0.7043057 # We will do a lmer() as the skewness of the data is not too large skewness(coleoptera_M_alpha_no_NA$abundance) ## [1] 1.189888 coleoptera_M_alpha_no_NA$abundance_tr <- log(coleoptera_M_alpha_no_NA$abundance) skewness(coleoptera_M_alpha_no_NA$abundance_tr) ## [1] 0.6745912

# Models model_coleoptera_M_5 <- lmer(abundance_tr ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = coleoptera_M_alpha_no_NA, REML = FALSE) # Assumptions OK model_coleoptera_M_10 <- lmer(abundance_tr ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H _10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = coleoptera_M_a lpha_no_NA, REML = FALSE) # Assumptions OK model_coleoptera_M_20 <- lmer(abundance_tr ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H _20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = coleoptera_M_a lpha_no_NA, REML = FALSE) # Assumptions OK model_coleoptera_M_50 <- lmer(abundance_tr ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H _50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = coleoptera_M_a lpha_no_NA, REML = FALSE) # Assumptions OK

# Choose the best model for Coleoptera abundance AIC(model_coleoptera_M_5, model_coleoptera_M_10, model_coleoptera_M_20, model_coleopte ra_M_50) skewness(coleoptera_M_alpha$abundance) ## [1] 1.297664 coleoptera_M_alpha$abundance_tr <- log(coleoptera_M_alpha$abundance) skewness(coleoptera_M_alpha$abundance_tr) ## [1] 0.6922407

# We redo the model on 10m with all data available for those fixed variables. model_coleoptera_M_10_best <- lmer(abundance_tr ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P_10^2)*H_10 + (1|Country:Location), data = coleoptera_M_alpha, REML =

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FALSE) # + P_10*I(H_10^2) summary(model_coleoptera_M_10_best)

# Predictions for P newdf_predicted_coleoptera_M_P10 <- data.frame(abundance = NA, H_10 = NA, P_10 = rep(s eq(0, 1, 0.001), 3)) length_coleoptera_M_P10 <- nrow(newdf_predicted_coleoptera_M_P10) newdf_predicted_coleoptera_M_P10$H_10[1:length_coleoptera_M_P10/3] <- 0.25 newdf_predicted_coleoptera_M_P10$H_10[(length_coleoptera_M_P10/3+1):(2*length_coleopte ra_M_P10/3)] <- 0.5 newdf_predicted_coleoptera_M_P10$H_10[(2*length_coleoptera_M_P10/3+1):length_coleopter a_M_P10] <- 0.75 newdf_predicted_coleoptera_M_P10$abundance <- predict(model_coleoptera_M_10_best, newd ata = newdf_predicted_coleoptera_M_P10, re.form = NA) newdf_predicted_coleoptera_M_P10$H_10 <- as.factor(as.character(newdf_predicted_coleop tera_M_P10$H_10)) newdf_predicted_coleoptera_M_P10$abundance <- exp(newdf_predicted_coleoptera_M_P10$abu ndance) plot_predicted_coleoptera_M_P10 <- ggplot(data = coleoptera_M_alpha, aes(x = P_10, y = abundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_coleoptera_M_P10, aes(x = P_10, y = abundance, li netype = H_10), colour = "green") plot_predicted_coleoptera_M_P10 + labs(title = "Fitted values of marram-specific Coleo ptera abundance in function of\nmarram proportion (P) at 10m radius scale", x = "Propo rtion of marram (P)", y = "Marram-specific Coleoptera abundance", linetype = "Aggregat ion\nof marram (H)") + theme(plot.title = element_text(face = "bold")) + xlim(0, 1)

#### Arachnida ####

# General species (G)

# Check for overdispersion mean(na.omit(arachnida_G_alpha$abundance)) ## [1] 2.482972 var(na.omit(arachnida_G_alpha$abundance)) ## [1] 3.48651

# Models model_arachnida_G_5 <- glmer.nb(abundance ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I(P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = arachnida_G_alpha_no_NA) model_arachnida_G_10 <- glmer.nb(abundance ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = arachnida_G_alpha_no_NA) model_arachnida_G_20 <- glmer.nb(abundance ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = arachnida_G_alpha_no_NA) model_arachnida_G_50 <- glmer.nb(abundance ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = arachnida_G_alpha_no_NA)

# Choose the best model for Coleoptera abundance AIC(model_arachnida_G_5, model_arachnida_G_10, model_arachnida_G_20, model_arachnida_G_50)

# We redo the model on 50m with all data available for those fixed variables. model_arachnida_G_50_best <- glmer.nb(abundance ~ P_50 + H_50 + I(P_50^2) + I(H_50^2)

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+ (1|Country:Location), data = arachnida_G_alpha) # + P_50*H_50 + P_50*I(H_50^2) + I (P_50^2)*H_50 summary(model_arachnida_G_50_best)

# Predictions for P newdf_predicted_arachnida_G_P50 <- data.frame(abundance = NA, H_50 = 0.5, P_50 = seq(0 , 1, 0.001)) newdf_predicted_arachnida_G_P50$abundance <- predict(model_arachnida_G_50_best, newdat a = newdf_predicted_arachnida_G_P50, re.form = NA, type = "response") plot_predicted_arachnida_G_P50 <- ggplot(data = arachnida_G_alpha, aes(x = P_50, y = a bundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_arachnida_G_P50, aes(x = P_50, y = abundance), co lour = "black") plot_predicted_arachnida_G_P50 + labs(title = "Fitted values of general Arachnida abun dance\nin function of marram proportion (P) at 50m radius scale", x = "Proportion of m arram (P)", y = "General Arachnida abundance") + theme(plot.title = element_text(face = "bold"))

# Decide what is the best x-range for H lines --> 0.25-1 plot(arachnida_G_alpha$H_50, arachnida_G_alpha$abundance) # Predictions for H newdf_predicted_arachnida_G_H50 <- data.frame(abundance = NA, P_50 = 0.5, H_50 = seq(0 .2, 1, 0.001)) newdf_predicted_arachnida_G_H50$abundance <- predict(model_arachnida_G_50_best, newdat a = newdf_predicted_arachnida_G_H50, re.form = NA, type = "response") plot_predicted_arachnida_G_H50 <- ggplot(data = arachnida_G_alpha, aes(x = H_50, y = a bundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_arachnida_G_H50, aes(x = H_50, y = abundance), co lour = "black") plot_predicted_arachnida_G_H50 + labs(title = "Fitted values of general Arachnida abun dance\nin function of marram aggregation (H) at 50m radius scale", x = "Aggregation of marram (H)", y = "General Arachnida abundance") + theme(plot.title = element_text(face = "bold"))

# Dune species (D) skewness(arachnida_D_alpha$abundance) ## [1] 2.581719 # Check for overdispersion mean(na.omit(arachnida_D_alpha$abundance)) ## [1] 1.291667 var(na.omit(arachnida_D_alpha$abundance)) ## [1] 0.4348592

# Models model_arachnida_D_5 <- glmer(abundance ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I (P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = arachnida_D_alpha_no_NA, fam ily = "poisson") model_arachnida_D_10 <- glmer(abundance ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10 ^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = arachnida_D_alpha _no_NA, family = "poisson") model_arachnida_D_20 <- glmer(abundance ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20 ^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = arachnida_D_alpha _no_NA, family = "poisson") model_arachnida_D_50 <- glmer(abundance ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50

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^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = arachnida_D_alpha _no_NA, family = "poisson")

# Choose the best model for Coleoptera abundance AIC(model_arachnida_D_5, model_arachnida_D_10, model_arachnida_D_20, model_arachnida_D _50)

# We redo the model on 10m with all data available for those fixed variables. model_arachnida_D_10_best <- glmer(abundance ~ P_10 + (1|Country:Location), data = ara chnida_D_alpha, family = "poisson") # + H_10 + P_10*H_10 + I(H_10^2) + I(P_10^2) + I( P_10^2)*H_10 + P_10*I(H_10^2) summary(model_arachnida_D_10_best) # no significance

# Marram species (M)

# Check for overdispersion mean(na.omit(arachnida_M_alpha$abundance)) ## [1] 1.816456 var(na.omit(arachnida_M_alpha$abundance)) ## [1] 1.271829 plot(arachnida_M_alpha$abundance)

# Models model_arachnida_M_5 <- glmer(abundance ~ P_5 + H_5 + P_5*H_5 + I(P_5^2) + I(H_5^2) + I (P_5^2)*H_5 + P_5*I(H_5^2) + (1|Country:Location), data = arachnida_M_alpha_no_NA, fam ily = "poisson") model_arachnida_M_10 <- glmer(abundance ~ P_10 + H_10 + P_10*H_10 + I(P_10^2) + I(H_10 ^2) + I(P_10^2)*H_10 + P_10*I(H_10^2) + (1|Country:Location), data = arachnida_M_alpha _no_NA, family = "poisson") model_arachnida_M_20 <- glmer(abundance ~ P_20 + H_20 + P_20*H_20 + I(P_20^2) + I(H_20 ^2) + I(P_20^2)*H_20 + P_20*I(H_20^2) + (1|Country:Location), data = arachnida_M_alpha _no_NA, family = "poisson") model_arachnida_M_50 <- glmer(abundance ~ P_50 + H_50 + P_50*H_50 + I(P_50^2) + I(H_50 ^2) + I(P_50^2)*H_50 + P_50*I(H_50^2) + (1|Country:Location), data = arachnida_M_alpha _no_NA, family = "poisson")

# Choose the best model for Coleoptera abundance AIC(model_arachnida_M_5, model_arachnida_M_10, model_arachnida_M_20, model_arachnida_M _50)

# We redo the model on 50m with all data available for those fixed variables. model_arachnida_M_50_best <- glmer(abundance ~ P_50 + H_50 + P_50*H_50 + (1|Country:Lo cation), data = arachnida_M_alpha, family = "poisson") # + I(P_50^2) + I(H_50^2) + I( P_50^2)*H_50 + P_50*I(H_50^2) summary(model_arachnida_M_50_best)

# Predictions for P newdf_predicted_arachnida_M_P50 <- data.frame(abundance = NA, H_50 = NA, P_50 = rep(se q(0, 0.8, 0.001), 3)) length_arachnida_M_P50 <- nrow(newdf_predicted_arachnida_M_P50) newdf_predicted_arachnida_M_P50$H_50[1:length_arachnida_M_P50/3] <- 0.25 newdf_predicted_arachnida_M_P50$H_50[(length_arachnida_M_P50/3+1):(2*length_arachnida_ M_P50/3)] <- 0.5 newdf_predicted_arachnida_M_P50$H_50[(2*length_arachnida_M_P50/3+1):length_arachnida_M _P50] <- 0.75

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newdf_predicted_arachnida_M_P50$abundance <- predict(model_arachnida_M_50_best, newdat a = newdf_predicted_arachnida_M_P50, re.form = NA, type= "response") newdf_predicted_arachnida_M_P50$H_50 <- as.factor(as.character(newdf_predicted_arachni da_M_P50$H_50)) plot_predicted_arachnida_M_P50 <- ggplot(data = arachnida_M_alpha, aes(x = P_50, y = a bundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_arachnida_M_P50, aes(x = P_50, y = abundance, lin etype = H_50), colour = "green") plot_predicted_arachnida_M_P50 + labs(title = "Fitted values of marram-specific Arachn ida abundance\nin function of marram proportion (P) at 50m radius scale", x = "Proport ion of marram (P)", y = "Marram-specific Arachnida abundance", linetype = "Aggregation \nof marram (H)") + theme(plot.title = element_text(face = "bold"))

# Decide what is the best x-range for H lines --> 0.25-1 plot(arachnida_M_alpha$H_50, arachnida_M_alpha$abundance) # Predictions for H newdf_predicted_arachnida_M_H50 <- data.frame(abundance = NA, P_50 = NA, H_50 = rep(se q(0.2, 1, 0.001), 3)) length_arachnida_M_H50 <- nrow(newdf_predicted_arachnida_M_H50) newdf_predicted_arachnida_M_H50$P_50[1:length_arachnida_M_H50/3] <- 0.25 newdf_predicted_arachnida_M_H50$P_50[(length_arachnida_M_H50/3+1):(2*length_arachnida_ M_H50/3)] <- 0.5 newdf_predicted_arachnida_M_H50$P_50[(2*length_arachnida_M_H50/3+1):length_arachnida_M _H50] <- 0.75 newdf_predicted_arachnida_M_H50$abundance <- predict(model_arachnida_M_50_best, newdat a = newdf_predicted_arachnida_M_H50, re.form = NA, type= "response") newdf_predicted_arachnida_M_H50$P_50 <- as.factor(as.character(newdf_predicted_arachni da_M_H50$P_50)) plot_predicted_arachnida_M_H50 <- ggplot(data = arachnida_M_alpha, aes(x = H_50, y = a bundance)) + geom_point(colour = "darkgrey") + geom_smooth(data = newdf_predicted_arachnida_M_H50, aes(x = H_50, y = abundance, lin etype = P_50), colour = "green") plot_predicted_arachnida_M_H50 + labs(title = "Fitted values of marram-specific Arachn ida abundance\nin function of marram aggregation (H) at 50m radius scale", x = "Aggreg ation of marram (H)", y = "Marram-specific Arachnida abundance", linetype = "Proportio n\nof marram (P)") + theme(plot.title = element_text(face = "bold"))

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