NTNU Norwegian University of Science and Technology Faculty of Natural Sciences Department of Biology
Emma Pettersen
Spatial patterns and drivers of the functional diversity of cannrivore communities in the Arctic
Master’s thesis in Natural Science with Teacher Education Supervisor: James Speed
Co-supervisor: Damaris Matten June 2021
Photo by Jonatan Pie
Master ’s thesis
Emma Pettersen
Spatial patterns and drivers of the functional diversity of cannrivore communities in the Arctic
Master’s thesis in Natural Science with Teacher Education Supervisor: James Speed
Co-supervisor: Damaris Matten June 2021
Norwegian University of Science and Technology Faculty of Natural Sciences
Department of Biology
ABSTRACT
Understanding the spatial patterns of diversity and the drivers shaping these patterns, is crucial in order to better predict the response of the ecosystem to biodiversity loss and climate change. Species richness has traditionally been used when investigating spatial patterns of diversity. However, using a functional approach to biodiversity can give useful insight in species functional contributions to ecosystems and predict ecological consequences associated with environmental change. In this study, I explore whether abiotic, biotic or geographic factors predict the functional diversity of terrestrial vertebrate carnivores across the Arctic tundra biome.
To investigate this, 11 traits across 85 carnivorous birds and mammals from multiple
databases and literature were assembled. The spatial pattern of functional space (richness) and packing (dispersion) for all carnivorous species were estimated using distribution maps and the trait data. Functional diversity (functional richness and functional dispersion) was calculated on to a 100 km x 100 km grid. Seven variables were used to represent six hypothesis predicting functional diversity patterns. To evaluate the relative effect of these drivers, generalized least squares (GLS) were built and I further used model averaging based on the Akaike information Criterion to assess the relative importance of each variable.
I found that functional richness and functional dispersion largely showed high values across the Arctic tundra biome, where it peaked in South Arctic Siberian and in the Canadian Arcitc Archipelago, respectively. Functional diversity was best predicted by trophic interactions, namely functional diversity of herbivores, as well as habitat heterogeneity, climatic severity and zoogeographic regions. Given the importance for functional diversity for ecosystem functioning, ongoing climate warming and northward expansion of boreal species could increase the functional diversity across the arctic. The consequence of the loss of functional unique species remains open.
ABSTRACT IN NORWEGIAN
Å kunne forstår diversitetsmønsteret og hvilke faktorer som driver disse er avgjørende for å bedre kunne forutsi økosystemets respons til tap av biodiversitet og klimaendringer.
Artsrikhet har tradisjonelt blitt brukt i slike studier. Derimot ved å bruke en funksjonell tilnærming til biodiversitet kan dette gi nyttig innsikt i artenes funksjonelle bidrag i økosystemene, og videre forutsi økologiske konsekvenser assosiert med miljøendringer. I denne studien ser jeg nærmere på om abiotisk, biotiske og geografiske faktorer kan forutsi diversiteten av terrestriske kjøttetende virveldyr på tvers av den Arktiske tundraen.
For å undersøke dette ble 11 trekk på tvers av 85 kjøttetende fugler og pattedyr samlet fra flere databaser og litteratur. Ved bruk av trekkdata og distribusjonskart ble funksjonell rikhet og funksjonell spredning kalkulert på et 100 km x 100 km rutenett. Seks hypoteser
representert av syv ulike variabler ble utviklet basert på hva som kunne forventes å forme funksjonell diversitet. For å evaluere effekten av variablene ble generalisert minste kvadraters-metode brukt, videre brukte jeg tilnærmingen model gjennomsnitt basert på Akaike informasjonskriterium for å vurdere den relative betydningen av hver variabel.
Jeg fant at funksjonell rikhet og funksjonell spredning generelt viste høye verdier på tvers av den Arktiske tundraen, hvor den var høyest i Sør-arktisk Sibir og på det kanadiske
polararkipelet, henholdsvis. Funksjonell diversitet ble best forutsett av trofiskeinteraksjoner, nærmere bestemt funksjonell diversitet av planteetere, samt habitat heterogenitet, klimatisk alvorlighetsgrad og zoogeografiske regioner. Gitt betydningen den funksjonell diversitet har for økosystemfunksjon, pågående klimaendringer og immigrerende arter fra sørlige
breddegrader kan øke den funksjonelle diversiteten i Arktisk. Konsekvensene for tap av funksjonelle unike arter er fremdeles usikker.
ACKNOWLEDGMENTS
First of all, I want to thank my supervisor James David M. Speed for introducing me to this project and this field of study, and my second supervisor Damaris Matten. Thank you both for always being available for answering all my questions, help in R, discussion, and providing useful feedback along the way. I have learned so much and are so grateful for your support.
And I want to give a thanks to my friends, my roommates, and family for the support and motivations while working on this thesis. Specially, I want to thank my sister Line for helping with proofreading and for all other advice and tips in this final semester. What would I do without you?
TABLE OF CONTENTS
LIST OF FIGURES... V LIST OF TABLES... V LIST OF ABBREVIATIONS ... V
1 INTRODUCTION ... 1
2 MATERIALS AND METHODS ... 5
2.1 SELECTING ARCTIC SPECIES ... 5
2.2 FUNCTIONAL TRAIT DATA... 6
2.3 SPECIES DIVERSITY AND FUNCTIONAL DIVERSITY CALCULATIONS ... 6
2.4 DRIVERS OF FUNCTIONAL CARNIVORE DIVERSITY ... 7
2.5 MODEL APPROACH AND STATISTICAL ANALYSIS ... 8
3 RESULTS ... 10
3.1 SPATIAL PATTERNS OF DIVERSITY ... 10
3.2 DRIVERS OF DIVERSITY ... 11
4 DISCUSSION... 14
4.1 SPATIAL PATTERNS OF DIVERSITY ... 14
4.2 DRIVERS OF DIVERSITY ... 15
5 CONCLUSION ... 18
6 REFERENCES ... 20
APPENDIX A ... 26
APPENDIX B ... 30
List of figures
Figure 1. Pairs-plot depicting pairwise dependence between the diversity metric ... 10 Figure 2. Spatial patterns of species richness and functional diversity ... 11 Figure 3. Relative variable importance and model average coefficients ... 13
List of tables
Table 1.Hypothesis for drivers of functional diveristy... 4
List of Abbreviations
FD Functional diversity FRic Functional richness FDis Functional dispersion GLS Generalized least squares
NDVI Normalized difference vegetation index RVI Relative important variable
1 Introduction
Biodiversity is a concept that embraces the spectra of biological variation, ranging from genetic level to the ecosystem level. It plays a key role in maintaining ecosystem functions and stability, and these are well studied (Hooper et al., 2005; Post, 2013). Biodiversity can also influence the resilience and the resistant of the ecosystem to environmental change (Chapin Iii et al., 2000; Duffy et al., 2016). In a time where we are experiencing high extinction rates across many taxa (Pimm et al., 2014), understanding the drivers of spatial patterns are crucial to better predict the ecosystem-level response to biodiversity loss (Barrio et al., 2016).
While the term biodiversity provides an extensive framework for studying spatial patterns of variation, the broad definition also makes it a complicated concept to measure (Purvis &
Hector, 2000). Species richness, the number of species in a defined area, has often been used as a surrogate for ecosystem functioning and the success of conservation initiatives. However, this measurement has been debated for being too narrow and not addressing the full
complexity of diversity (Cadotte et al., 2011; Gagic et al., 2015; Safi et al., 2011). For
instance, species richness does not recognize the evolutionary differences between species or how species interact with their ecosystem. How diversity influences ecosystem functioning depends on the traits and niches filled by the species (Cadotte et al., 2011). Beyond the loss of species, the loss of traits could be more severe (Flynn et al., 2009). Further, Flynn et al.
(2009) showed that just using abundance of species can result in overlooking important ecological information. One approach to solve this limitation in representing community function, evolutionary history, and the interacting within communities has led to the development of functional diversity. Safi et al. (2011) found by doing a global analysis of patterns in diversity of terrestrial mammals, that functional diversity and species richness are strongly correlated. However, the correlation is not perfect, making species richness a less ideal predictor for biodiversity.
Functional diversity (FD) concerns about what the organisms are doing in the ecosystem rather on their evolutionary history (phylogenetic diversity; (Petchey & Gaston, 2006). FD is used to describe the variations and distribution of traits in a community (Petchey & Gaston, 2002). Which trait is of relevance depends on the aim of the study. Essentially meaning those traits that reflect the species interaction with its ecosystem are relevant to include. (Tilman,
2001). A number of authors have showed that FD is a good measurement for predicting ecological functional consequences associated with human disturbance (Flynn et al., 2009;
Pimiento et al., 2020; Vollstädt et al., 2017), when investigating trophic interactions (Gravel et al., 2016), and ecosystem function (Hooper et al., 2005). As a consequence of such research, we can make better conservation and restoration decisions (Cadotte et al., 2011).
Community structure can strongly affect the functioning of ecosystem. The loss of top
predators can cascade down the food chain and alter basic ecosystem processes via cascading effects (Estes et al., 2011). This is alarming when population size of predators is declining worldwide (Ceballos et al., 2017; Ripple et al., 2014). In the Arctic, top predators such as the Artic fox (Vulpes lagopus) are essential for maintaining the tundra ecosystem from becoming grasslands (Croll et al., 2005). Terrestrial carnivore species also play an important role for ecosystem functioning even in high Arctic food-webs (Gilg et al., 2009). Even though species richness in the Arctic is increasing by immigration from the boreal biome, many arctic cold adapted species are expected to become increasingly threatened (Gilg et al., 2012). The Arctic tundra is undergoing rapid climatic changes. Understanding the spatial distribution of
terrestrial carnivores is crucial in order to better predict the response of the ecosystem under environmental change.
The drivers and mechanisms behind large scale patterns of biodiversity is a central theme for ecologists and biogeographers (Gaston, 2000). Environmental factors, evolutionary history and colonization dynamics have shown to explain large scale species richness patterns.
Historical and geographic drivers such as climatic oscillations in the Quaternary have been important for driving present-day biodiversity patterns at high latitudes (Davies et al., 2011).
Further, the extent of ice cover since the Last Glacial Maximum have influenced colonization and species richness in the Arctic tundra (CAFF, 2013). In addition to glaciation, a
combination of historic events such as dispersal, continental drift and extinction have led to different patterns of species distributions. Barriers between different zoogeographic regions have hindered species and clades to disperse (Eiserhardt et al., 2013). Consequently, making zoogeographic regions consist of different species, and possibly making them differ in species richness.
Landscape heterogeneity has shown to influence spatial patterns of diversity since more
2013; Questad & Foster, 2008) with providing a greater niche space for functional different species to coexist (Sandom et al., 2013). This has been shown in both birds (Davies et al., 2007), and mammals (Kerr & Packer, 1997).
Other potential drivers are linked to productive energy, where areas with high productivity often support higher diversity of species. Plant productivity associated with resource availability determines the structure and abundance of herbivores and predators in the ecosystem (Oksanen et al., 1981). The species diversity of Arctic herbivores is decreasing with latitude, and has been related to the decrease in primary production (Barrio et al., 2016), associated with low temperatures at high latitudes. Low productivity at the base of the trophic chains restricts the complexity of the food-webs and the presence of terrestrial carnivores.
Further, the influence of trophic interactions at large spatial scales has been acknowledged (Barrio et al., 2016; Sandom et al., 2013) and should be included when investigating biodiversity patterns (Speed et al., 2019). Species richness of prey is found to be important when explaining the richness of predators (Sandom et al., 2013). Moreover, diversification in prey can lead to diversification in predators (Brodersen et al., 2018).
In this study, I investigate how geographic, abiotic and biotic factors influence the functional diversity of terrestrial carnivores across the Arctic biome (Table 1.) Species richness has been documented across the Arctic (CAFF, 2013) and new studies have analyzed the drivers for different diversity patterns for terrestrial vertebrate herbivores in the Arctic (Barrio et al.
2016; Speed at al. 2019). However, the patterns driving the diversity of Arctic terrestrial carnivores are still unknown. My aim is to evaluate the different drivers shaping the biodiversity patterns and identify which variable have the largest impact on functional diversity.
When looking into geographic factors, I expect regions with shorter post-glacial history, being regions that were ice-covered during the Last Glacial Maximum, to show lower levels of functional diversity due to limiting time to recolonize and diverse (H1), and different zoogeographic regions showing different levels of functional diversity (H2). For the abiotic factors, I expect climatic severity to constraint trait expression leading to low levels of functional diversity in such areas (H3). Further, I expect functional diversity to increase with habitat and topographic heterogeneity (H4). For assessing the biotic influence, I expect more productive areas to host higher levels of functional diversity of carnivores than less
productive areas (H5). Finally, I expect the functional diversity of carnivores to be influenced by the functional diversity of herbivores, seeing high functional diversity in carnivores in areas with high functional diversity in herbivores (H6).
Table 1.Hypothesis for drivers of terrestrial vertebrate carnivore functional diversity across the Arctic biome.
Explanatory variables Hypothesis and justifications Geographic factors.
(Variables: Last Glacial Maximum, zoogeographic regions)
H1: There is lower diversity of predators in areas with shorter post- glacial history. Shorter time to recolonize and diverse (Davies et al.
2011)
H2: Levels of diversity differ between the different zoogeographic regions. Organisms evolving in relative isolation for long periods of time (Eiserhardt et al., 2013)
Abiotic drivers (Variables: bio6, topography, habitat)
H3: Functional diversity decreases with climatic severity; harsh weather constraints traits expressions (Reymond et al., 2013).
Regions with more climatic stability have lower extinction rates
H4: Increase in functional diversity in areas with higher habitat and topographic heterogeneity, provides greater niche space (Kerr &
Parker, 1997; Davies et al., 2007) Biotic drivers
(Variables: NDVI herbivore functional diversity data)
H5: Productive ecosystems can contain a higher number of trophic levels. Making productive ecosystems to hold higher functional diversity in carnivores (Oksanen et al., 1981)
H6: Where there is high functional diversity of herbivores there is high functional diversity of carnivores or functional diversity in carnivores increase with herbivore functional diversity (Brodersen et al. 2018)
2 Materials and Methods
2.1 Selecting Arctic species
Species distribution maps were obtained from The International Union for Conservation of Nature (IUCN, 2021). Spatial polygons for all terrestrial vertebrates (mammals, birds, amphibians and reptiles) were downloaded. The Arctic region was defined by the
Conservation of Flora and Fauna boundaries for the Arctic (Conservation of Arctic Flora and Fauna Working Group, 2010). This included the high Arctic and low Arctic as well as the Subarctic. Only terrestrial vertebrate species occurring in the Arctic and Subarctic were included, consisting of both resident and migratory species. Pelagic birds were defined by having ‘PelagicSpesialist’ attribute equal to 1, as found in the EltonTrait table (Wilman et al., 2014), and removed from the dataset. This resulted in 541 species present in the Arctic region.
The carnivores were selected based on the consumption of vertebrates in their diet, with a value equal, or over 50 % of vertebrate matter (sum of the percentage of higher vertebrates, fish, amphibians and scavenging in their diet), as found in the EltonTrait species-level foraging attributes table (Wilman et al., 2014). The scavenging foraging attribute was included in the overall carnivorous diet, being important for species such as the wolverine.
Diets for mammals and birds were found in the EltonTrait database. For the amphibian species the database AmphiBIO was used (Oliveira et al., 2017). Species having attribute
“Vert” being equal to 1 was identified as having vertebrates in their diet. This resulted in two amphibian species, further research showed that they did not have over 50 % vertebrate matter in their diet (Avery, 1971; Covaciu-marcov et al., 2010; Hedeen, 1972). Two species of terrestrial reptiles were identified, the European viper (Vipera berus) and the common lizard (Zootoca vivipara). Only the European viper were shown to be carnivorous (Avery, 1971; Vacheva & Naumov, 2020). Due to missing distribution maps for the European viper on the IUCN webpage, it was excluded from this study.
The final list resulted in 85 Arctic carnivorous species, including 22 mammals and 63 birds.
The polygon layers of the species ranges were rasterized into a 100 km x 100 km equal grid.
Grid cells that had more than 50% ice-covered land or water were excluded. The final grid layer resulted in 1291 cells.
2.2 Functional trait data
For each species, 11 traits were quantified (Figure A1, Table A1). These traits were selected to capture the ecological roles in the Arctic tundra ecosystem (described in Table A1). Traits included: diet, foraging type, body mass, sexual dimorphism, habitat, litter/clutch size, breeding per season, population dynamics, wintering strategy, activity pattern, average prey size, maximum prey size, group size, and hunting group size. The functional data were mainly based on the species information provided by the trait database EltonTrait (Wilman et al., 2014), Birds of the World (S. M. Billerman et al., 2020) and the Handbook of the Mammals of the World (Wilson & Meittermeier, 2009). Additional sources for some of the species were used when the information was lacking in the above sources (Table B1). The trait values were averages across the study region. When an interval for a given trait such as body mass or litter/clutch size were presented, the midpoint was taken. If species had missing trait data, values from closely related species were used instead (see Table B1). Body size, typical prey size, and maximum prey size was log10 transformed to achieve a normal distribution, as recommended by Villéger et al. (2008).
2.3 Species diversity and functional diversity calculations
Different methods and approaches exist to capture functional diversity. They reflect different aspects of the distributions of species in niche space (Mason et al 2005). Two complementary functional diversity metrics were used to describe how species are distributed in the
community, or in the functional space: functional richness (FRic) and functional dispersion (FDis).
To assess how the functional trait space is filled across the study region I used the metric functional richness. FRic is calculated in a multidimensional Euclidian space where the traits are represented by different axis. Species are placed along the axis according to their trait values. The volume of the trait space occupied is calculated as the convex null volume and scaled between 0 and 1 (Villéger et al., 2008). This metric has some limitations considering its sensitivity to outliers (extreme trait values in the dataset). When assessing functional diversity, other metrics need to be included to not lose information. Low functional richness means that a low portion of the available niche space in the community are unused (Mason et al., 2005)
While FRic describes how the functional range is covered, FDis describes the variability of functional space occupied by the species in the community. FDis is calculated as ‘the mean distance of individuals species to the centroid of all species to the community’ (Laliberté et al., 2014). Because FDis takes into account the relative distance from the functional space center, it tells us how the species are functional packed and segregated. For instance, FDis is high when most of the species have functional traits that are far away from the center of the functional trait range (Masons et al. 2005). High levels of functional dispersion indicate high niche differentiation and low competition.
The functional indices were calculated using the dbFD function from the “FD” package (Laliberté et al., 2014) in RStudio 3.6 (RStudioTeam, 2020). Given that the dbFD-function only considers grid cells with 3 species or more, grid cells containing 3 or less species were excluded. All functional diversity measurements were calculated in each of the 1291
100 km x 100 km grid cells. Species richness was calculated as the sum of all species in each 1291 grid cell.
2.4 Drivers of functional carnivore diversity
The explanatory variables used represent the main hypothesis for predicting functional diversity at large spatial scale (Table 1). The variables included time since Last Glacial Maximum, zoographic regions, climatic severity, topographic and habitat heterogeneity, productivity, and herbivore functional diversity data.
The geographic variables included glacial history and zoographic regions. The extent of ice coverage since the Last Glacial Maximum (LGM) were represented on 1000-years intervals (Peltier, 1993). The zoogeographic regions of Holt et al. (2013) was used. These included the North American, Eurasian and Arctico-Siberian region (Figure A2).
Abiotic variables included climatic severity and landscape heterogeneity. Data for the climatic severity was gathered from WorldClim climate database (Hijmans et al., 2005). The variable bio6 was used, indicating the coldest temperature of the coldest month. This variable is assumed to be the most limiting to terrestrial vertebrate survival in the Arctic. Landscape heterogeneity is reflected by two variables, included topographic heterogeneity and habitat heterogeneity. Topographic heterogeneity was calculated based on the GLOBE digital
elevation model (Bicheron et al., 2008). The standard deviation of elevation within the 100km
grid cells was calculated reflecting topographic dispersal barriers. For the habitat
heterogeneity the GlobCover dataset was used to calculate the number of land types present in each 100 km x 100 km grid cell.
Biotic interaction was also considered. For the bottom-up relationship the vegetation productivity was collected using the normalized difference vegetation index (NDVI) representing the maximum greenness in an aera. Further, the functional diversity of herbivores was obtained from Speed at el. (2019). Here, functional diversity is calculated using the dendrogram approach and reflects the functional richness of terrestrial vertebrate herbivores across the Arctic.
All explanatory variables were rasterized and were stacked onto the same grid as the
functional diversity measurements. When the explanatory variables had lacking data the grid cells were the removed for further analysis.
2.5 Model approach and statistical analysis
To evaluate the relative effect of each driver of carnivore functional diversity in the Arctic generalized least squares (GLS) were built including species richness, FRic and FDis as response variables. The full models included seven explanatory variables (Table 1): glaciation history (H1), zoographic regions (H2), climatic severity (H3), habitat and topographic
heterogeneity (H4), vegetation productivity (H5) and Arctic herbivore functional diversity (H6). The global model included all variables with zoographic region included as a factorial variable. The other variables were centered and scaled in order to make the estimated coefficients directly comparable. Relationships between the explanatory variable were
checked for linearity (Figure A4). They were tested for multicollinearity using pairwise linear correlations. NDVI were moderate correlated with the functional diversity of Arctic
herbivores with a correlation of R2=0.41. All other variables had R2<0.33. Since all explanatory variables had R2<0.41 they were included for further analysis.
Spatial distributed data can generate spatial autocorrelation and cause bias in the investigated variables. GLS are therefore a suited approach since it incorporates spatial covariance
structures in the models to control for spatial autocorrelation. After checking for several variance-covariance structures, the exponential correlation structure was used, where x- and
structure was the best-fit choice based on Akaike information Criterion (AIC) compared to the other variance-covariance structures.
GLS models were built for each of the seven explanatory variables (128 models for each of the 3 response variables). In order to evaluate the relative importance of all the explanatory variables predicting diversity of Arctic vertebrate carnivores, model averaging approach based on the AIC was used. Since model averaging have shown to generate consistent result across different methods in ecology (Dormann et al., 2018). Estimated coefficients for each variable were averaged across all models ranging from the top ranked model to the null model in which they were present and weighted according to the probability associated with each model. Models were built in R 3.6 (RStudioTeam, 2020) using the packages nlme (Pinheiro et al., 2021) and MuMIn (Bartoń, 2016)
3 Results
3.1 Spatial patterns of diversity
The relationship between species richness and functional richness (Figure 1) was logarithmic (R2=0.74). Lower proportion of species richness shows greater variability in functional richness, while more species increase the functional space occupied. Functional dispersion was independent of species richness (Figure 1). Further, the measurement for FRic and FDis were largely independent of each other having R2 = 0.34 (Figure 1). This confirms they reflect different aspects of functional diversity.
Figure 1. Pairs-plot depicting pairwise dependence between the diversity metric. From the top, species richness, functional richness and functional dispersion. Lower panels show scatterplots, the diagonal shows histogram distribution, and the upper panel shows the correlation between the diversity metrics.
Arctic carnivore species richness and functional richness was highest in east Arctico-Siberian (Figure 2). Species richness ranged from 2 to 44 (median 26, interquartile range 20-32) species per grid cell. The High Arctic had general the lowest amount of species coverage across the subzones (5.6 % on average) and increased to the Subarctic having 74.5 %, on average, of the total species pool. Functional diversity measurements were more evenly
spread than species richness showing generally high proportions (Figure 2). Functional richness showed largely high values (median 0.70, interquartile range 0.69-0.76) across the study regions. Functional richness peaked in southern Arctico-Siberian and had the lowest values on the Arctic islands and archipelagos, including Northeast Greenland, Svalbard, Severnaya Zemlya and New Siberian Islands. On the other hand, some Arctic islands, West Greenland, Iceland, Novaya Zemlya and Wrangel Island, showed high amount of functional richness. Functional dispersion was higher on the Arctic islands than functional richness.
High FDis were primarily concentrated in the Canadian Arctic Archipelago and the other Arctic Island excluding Northeast Greenland, (median 47.2, interquartile range 43.76-49.72).
This was not matching the pattern of species richness, where FDis showed high values in many species-poor areas.
Figure 2. Spatial patterns of diversity represented by species richness, functional richness, and functional dispersion of terrestrial Arctic vertebrate carnivores. Arctic zones are
highlighted by black dotted lines, from south to north, Subarctic, low Arctic and high Arctic.
All maps are on the same color scale where darker red color indicates higher values. Maps are projected using Lambert Azimuthal Equal-Area Projection. In total, 1291 grid cell with the dimension 100 km x 100 km is displayed.
3.2 Drivers of diversity
Seven possible explanatory variables describing the diversity patterns in Figure 1 were analyzed with model averaging approach. No single model had a strong support, based on the AIC weights form the model selection. All models from the null to the full model was chosen.
Relative variable importance (RVI) scores showed herbivore functional diversity and habitat heterogeneity to be the most important variable for driving species richness and functional
richness, with a RVI =1 for herbivore functional diversity and RVI > 0.99 for habitat heterogeneity (Figure 3a). Further, zoogeographic region was important for functional richness (RVI=0.69), but not for species richness (RVI=0.35). Winter minimum temperature was moderate important for both species and functional richness with RVI score being 0.37 and 0.44, respectively. Vegetation productivity (NDVI) was also important for driving species richness RVI=1 but only moderate important for driving functional richness RVI=0.45.
Topographic heterogeneity was moderate important predicting species richness (RVI=0.52) and the least important for driving functional richness (R=0.27). Glacial history was not relative important driving species or functional richness (R<0.43).
For functional dispersion winter minimum temperature was the most important variable with a score of RVI>99 (Figure 3a). Plant productivity was the second most important variable relative to the other variable tested (RVI=0.54), followed by ice-free history (RVI=0.41). The least important variable where herbivore functional diversity, habitat heterogeneity,
topographic heterogeneity and zoogeographic region, all having RVI score < 39.99.
Estimated coefficients of each variable driving the pattern of species and functional diversity are shown in Figure 3b. All the predictors were centered and scaled to make the coefficients directly comparable. The estimated coefficients showed how the predictors influence the different diversity metrics, in either revealing a positive or negative relationship.
The estimated variable for herbivore functional diversity showed a positive effect on both species richness (0.1± 0.03, Figure 3b) and FRic (0.15 ± 0.04). Species richness and
functional richness both decreased with habitat heterogeneity (-0.08 ± 0.02 and -0.06 ± 0.03, respectively). FRic tended to increase with milder winter temperature (0.04 ± 0.13) and decrease with vegetation productivity (-0.01 ± 0.02). FDis showed similar patterns showing a positive relationship herbivore functional diversity (0.003 ± 0.03) and a negative relationship with habitat heterogeneity (-0.003 ± 0.02). FDis and FRic were higher in North America compared to Arctico-Siberian (0.004 ± 0.33 and 0.23 ± 0.48) and lower in Eurasia compared to Arctico-Siberian (-0.05 ± 0.15 and -0.06 ± 0.13).
Figure 3. (a) Relative variable importance for environmental drivers as predictors of different diversity measurement for Arctic vertebrate carnivore diversity. The AIC weight is summed for each variable in the model which it appears, representing the relative importance of the variable. Variable importance is interpreted as the probability of the variable being a component in the best fitted model. Consequently, we can use this to rank the predictors in their order of importance. Zoographic region is used as a factorial variable. (b) Model average coefficients for divers of species richness, functional richness and functional dispersion. All predictors were standardized making the coefficients directly comparable. Coefficients were averaged across all models, means and 95% confidence intervals are shown. Coefficients not overlapping the zero line indicate a significantly effect driving the diversity, either in positive or negative direction. Coefficients with 95 % confidence intervals not overlapping zero are indicated with solid symbols while coefficient with 95 % confidence intervals overlapping zero are indicated with open symbols.
4 Discussion
In this study several hypotheses were tested to evaluate different drivers of spatial patterns in functional diversity of vertebrate carnivores across the Arctic tundra. I found that the
functional diversity of carnivores is driven by several factors. Functional diversity was generally large across the study region even in regions with low species richness. I found evidence strongly supporting the hypothesis that functional diversity of the Arctic herbivore community increases the functional diversity of carnivores (H7). Winter minimum
temperature was most important for explaining functional dispersion, this suggests that only a few traits are suited at colder temperatures (H3). Further, there was some evidence for
functional diversity to be different depending on the zoogeographic region (H1). Habitat heterogeneity was an important driver for functional richness, but it did not increase with more diverse habitats (H4).
4.1 Spatial patterns of diversity
The relationship between functional richness and species richness was weaker than expected (with a correlation of R2=0.74). Functional richness tended to flatten out with increasing species richness (Figure 1). This indicates that with increasing species richness the chance of the addition of new species which are functional dissimilar decreases (Safi et al. 2011).
Probably, because many niches in the community have been filled (Mason et al. 2005). A stronger correlation between functional richness and species richness has been found in mammals at a global scale (Safi et al. 2011) and for Arctic vertebrate herbivores (Speed at al.
2019). This over-dispersion of functional richness found in terrestrial carnivores in some regions of the Arctic suggests recent recolonization to new areas, adaptions to new environments, or low competition (Safi et al. 2011).
Species richness tended to decrease with higher latitude and be lower on the Arctic
Archipelago, which was expected (CAFF, 2013). Two different measurements of functional diversity have been included to get a full picture of the functional diversity of terrestrial vertebrate carnivores across the Arctic tundra. Functional richness was higher than species richness in many areas and was more evenly spread. Indicating that the carnivore species exhibit more traits than expected from just species richness. Functional dispersion was generally large across the Arctic tundra suggesting high niche differentiation and low
competition. Some areas in the Russian Subarctic showed low levels of functional dispersion,
possibly caused by having some of the coldest winter temperature (Figure 3). This suggests a narrower extent of functional traits occupied by species. The low functional richness on the Arctic islands (Northeast Greenland, Svalbard, Severnaya Zemlya and New Siberian Islands) are likely driven by the low species richness, as well as island size, there is just enough
species to occupy the functional trait space. The high dispersion on these islands indicates that the species are less functional similar and occupying different niches.
4.2 Drivers of diversity
By focusing on the guild of vertebrate carnivores in the Arctic this study indicates that trophic interactions, abiotic factors and geographic history are important for driving the functional diversity of carnivorous species. The variables herbivore functional diversity, habitat heterogeneity, zoogeographic regions and winter minimum temperature were the most relative important variables driving functional diversity of vertebrate carnivores across the Arctic.
I found evidence strongly supporting the hypothesis that functional diversity of herbivores increases the functional diversity of carnivores (H6). Prey richness has been strongly linked to predator richness even when the effect of other environmental drivers has been considered (Sandom et al. 2013) and is likely occurring because of a variety of functional different prey types, which provides greater niche availability and release from competition (Brodersen et al.
2018). The strong impact herbivores have on carnivores could indicate that hunting and foraging traits are under great selection. The trophic interaction between herbivores (prey) and carnivores (predators) could impact the evolutionary history of carnivores. Following the cascading diversification between herbivores and carnivores (Brodersen et al., 2018; Pontarp, 2020).
Although I hypothesized that functional diversity would be driven by habitat and topographic heterogeneity (H4), I found strong support for the opposite. Functional dispersion and
richness, as well as species richness, increase with more homogenous habitats. Only habitat heterogeneity was relatively important compared to the other drivers to drive functional richness and species richness, while topographic heterogeneity was only important for driving species richness. This result was surprising and not consistent with other studies (CAFF, 2013; Davies et al., 2007). Reasons for this could be that highly mobile species tend to be less
restricted to local habitat heterogeneity than to other factors such has climate (Arita &
Rodríguez, 2004). Unsurprisingly, carnivorous species in the Arctic are characterized by being highly mobile, and/or migratory. Another explanation could be that the habitat
classification was to coarse and consequently did not capture the habitat heterogeneity at scale relevant for Arctic carnivores. This has also been discussed by Gorcynski et al. (2021) when looking at tropical mammals, they also found no support for the habitat heterogeneity hypothesis driving functional richness. Topographic heterogeneity, another variable for heterogeneity, was least important driving functional diversity. It is likely that it is more important at local scale rather than et large spatial scales where it was calculated for (CAFF, 2013). Still, it was moderate important for predicting species richness. Functional dispersion tends to increase with habitat heterogeneity indicating even though these areas have low range of the total functional traits, the species tend to be functional different from each other. But keep in mind that other dynamics could be revealed when investigated at a finer scale.
Functional diversity was shown to differ between the zoographic regions (H2). Even though the zoogeographic region was relatively important in explaining functional richness it also revealed the most uncertainty in the data. The community of carnivores in both North
America and Eurasia were on average more functionally rich and functionally dispersed than in the Arctico-Siberian region (Figure 3b). One reason could be because many areas in the Arctico-Siberian occur in the High Arctic where a large part is located on islands, and these areas are generally less species rich (Figure 1). The North American and the Eurasian region on the other hand, mostly occur in the Subarctic which contains more species and
consequently have the potential to host higher levels of functional richness (Figure 1).
When looking at glacial history which has strongly influenced the geography of the Arctic (CAFF, 2013). There was some evidence for functional diversity to be lower in recently reglaciated areas (H1). Other historical and geographic factors such as historic temperature could be more important.
Some evidence was found to support the hypothesis of decreasing functional diversity with climatic severity (H3). It was the most important variable for explaining functional dispersion and slightly important for driving functional richness. Milder winter minimum temperature increased functional richness, on average, indicating that climatic severity can shape the potential size of functional space carnivores can occupy. The significant increase in functional
dispersion with milder temperature indicates that just a few traits allow for persisting in the colder regions and filters out species with unsuited traits (Reymond et al., 2013).
Plant productivity was an important driver for species richness and functional dispersion and to a lesser degree functional richness. A more productive system is expected to hold higher number of trophic levels (Oksanen et al. 1981). However, this study showed a decrease in functional dispersion (and species richness) with increasing vegetation productivity. The lack of support for increasing diveristy with productivity (H5) could be explained by the many populations of terrestraial carnivores which are maintained by subsidies from the marine ecosystem (Eide et al., 2012). For example, Arctic foxes rely on marine food items when their primary prey is scarce (Roth, 2003). In addition, several birds and mammals in the data set rely on marine species for food (Table 1B). They were included for being partly terrestrial and are important components of many Arctic terrestrial ecosystems.
Given the current environmental change in the Arctic, this study could give some insight in how the community of terrestrial vertebrate carnivores will respond. Many changes in the Arctic tundra have been observed. For instance, an increase in vegetation productivity (Elmendorf et al., 2012), warmer summer and winter temperature, and an increase of species richness from the boreal system (Gilg et al. 2012). For the carnivore community a greener and more productive Arctic will lead to the loss of species (Figure 3b), but the functional richness will stay the same. The cascading effect of the loss of top-predator species could then be prevented when other carnivores in the community have or take on the same functional role. This implies that the ecosystem will hold the same functions but with less species. In other words, the functional richness can act as a buffer for ecosystem instability.
Further, warmer winter minimum temperature will decrease the number of species in the carnivore community, but they will show a higher niche differentiation due to an increase in functional dispersion. When the climate gets warmer the constraint on trait expression might make it possible for more warm adapted boreal species to expand their range further into the Arctic, likely on the cost of cold adapted species (Barrio et al. 2016). Identifying which species in the Arctic are going to be threatened by climate change and how their loss is affecting the broader ecological community is still uncertain.
Chances are that the functional diversity of Arctic vertebrate herbivores is increasing, as an effect of a greener Arctic and influx of predator species (Speed et al. 2019). Further, the strong influence of the Arctic herbivore community has on carnivores shown here (Figure 3), predicts an increase in functional diversity of vertebrate carnivores across the Arctic.
Nevertheless, it may not be that simple. Depression of small mammal population cycles will have widespread cascading effects on the ecosystem including the breeding success of carnivores (i.e. Snowy owl (Bubo scandiacus), short eared owls (Asio flammeus), jaegers (skuas – Stercorarius spp.) (ACIA, 2005).
Although the species in the Arctic have experienced changing climate in the transition to the early Holocene (ACIA, 2005), other disturbances such as habitat fragmentation combined with climate change could make adaption and dispersals to new habitats more difficult (Renton et al., 2014). How individual species in the carnivore community will track these changes remains open and needs further studies.
This analysis is done on macro-scale with grid cells of 100 km x 100 km, findings might be different when applied on a finer scale. Additionally, the estimates of functional diversity in my study are reflected by my choice of traits. These were chosen because I believe they reflect important dimension of carnivores’ niche, including foraging traits, body mass, and reproductions traits to mention some. The selection of ecological important factors will be to a certain degree subjective and depend on the available literature and data for different species and taxa. In my study, information was lacking for some species and needed to be substituted for closely related species. This can also have affected the result of this study.
5 Conclusion
In this study, I have shown that trophic interactions are as important as abiotic factors in predicting functional diversity patterns across the Arctic tundra biome. The relative
importance of herbivore functional diversity on carnivore functional diversity could impact the evolutionary history of carnivores through cascading diversification. For predicting functional diversity, zoogeographic region, habitat heterogeneity and winter minimum temperature were important, as well.
The Arctic is identified as being particularly vulnerable for climate change, therefore understanding the drivers of functional diversity could better predict the response of the ecosystem. Here an increase in functional diversity may occur in response to warming and increasing immigration from the south. The results of this study can be used to motivate further research. Looking closer at the functional unique species occurring in the Arctic and the functional consequences associated with range expanding species from southern latitudes.
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Appendix A
Figure A1. Distribution of traits between the species. Dominant diet is the highest contribution a species has of the four groups investigated (percentage of fish, higher vertebrates, scavenging and amphibians). No species had a diet consisting as mostly of amphibians. See Table A1 for the description of the different traits, and table B1 for the individual trait values.
Table A1. Traits assigned to terrestrial vertebrates in the Arctic and their relations to individual and ecosystem level functions.
Trait Function Nature Modality/unit
Body mass Metabolic demand; capacity to alter habitat;
capacity to transfer energy among habitats;
Key variable in trophic ecology, reflecting both feeding and
predation ecology.
Cont. Log(n)
Diet Potential impacts on prey and competitors including the capacity to alter ecosystem structure.
Cont. Higher vertebrates; Fish;
Amphibians; Scavenging Feeding mechanism Trophic impact, including capacity to exert
cascading effects within food web; Capacity to directly or indirectly alter physical habitat via feeding.
Nominal Pursuit; Pursuit diving, Diving; Scavenging;
Stalk and ambush, mouse leap; Perch; Hovering;
Ambush; Bill dip; Plunge diver; Walking; Wading;
Kleptoparasitism Sexual dimorphism See body mass, accounts for the difference
between the sexes.
Cont. Numeric Habitat Habitat use; ecosystem contributions. The
breadth of habitats use could reflect their capacity to adapt to environmental change.
Binary Terrestrial; Marine;
Freshwater; Ice Litter/clutch size Larger litter/clutch sizes underlie
population dynamics, notably growth rates, with impacts on trophic dynamics.
Cont. Numeric
Breeding per season Underlie population dynamics. Cont. Log(n) Prey size, average
and maximum
Impact on prey and competitors. Cont. Log(n) Winter strategy Large-scale nutrient and energy transfer.
Reproduction. Several species migrate out of the Arctic during winter.
Nominal Active; Migratory; Under snow
Group size Ability to from group foraging; Modify physical habitat.
Nominal Solitary; Pair; Small;
Large Hunting group size Large groups will have a more intense effect
on prey.
Nominal Solitary; Pair; Small;
Large Population
dynamics
The effects of carnivores vary over time, from variable (cyclic) when carnivores are in peak abundance, to uniform (noncyclic).
These affect trophic dynamics in relation to herbivores.
Nominal Noncyclic; cyclic
Activity patterns A species temporal distribution of their resource use.
Nominal Nocturnal; crepuscular;
diurnal
Figure A2: Zoogeographic region from Holt et al. (2013). The different colours show the three zoogeographic regions occurring in the Arctic, North American, Eurasian and Arctico- Siberian.
Figure A3: Variograms showing spatial autocorrelating at increasing distance for the global GLS models. GLS models were fitted with exponential-covariance structures.
Figure A4: Pairwise plot between all explanatory variables. Lower panel show data and the upper panel show Pearson correlation coefficients.
