Macroecological Models Research Articles

Using macroecological species distribution models to estimate changes in the suitability of sites for threatened species reintroduction

Abstract1. Species reintroductions often aim to establish populations of threatened taxa over the long‐term. However, climate change may jeopardize reintroduction efforts by altering the conditions of a recipient site beyond the tolerances of the focal species. To aid the selection of recipient sites that will retain their suitability under climate change, species distribution models (SDMs) have been proposed as a method of locating areas with a greater likelihood of facilitating species persistence.2. We applied SDMs to predict macroclimatic suitability changes for 13 threatened plant and invertebrate species considered for reintroduction at four lowland raised bog sites undergoing restoration. We estimated suitability based on current and projected future conditions under two greenhouse gas concentration scenarios – one low (RCP2.6) and one high (RCP8.5) – using three general circulation models, for the period 2041–2060.3. When considering current predicted suitability, our models indicated that nine species were viable candidates for reintroduction to at least one of the restoration sites. But when accounting for potential future changes in suitability, the number of candidates was reduced to seven species, based on the RCP8.5 climate change scenario. While three of the sites received consistently similar predictions of suitability across species and scenarios, the most northerly site, Red Moss, received divergent suitability predictions for some species. This site is predicted to remain suitable forMetrioptera brachypteraandGenista anglicaunder at least one scenario despite substantial losses forecast across the rest of their U.K. ranges, suggesting that it could act as a macroclimatic refuge as climate change advances.4. The findings presented here made a valuable contribution to the reintroduction planning process, by facilitating the prioritization of reintroduction efforts towards species with a greater likelihood of establishing long‐term populations at the prospective recipient sites.

BILBI: Supporting global biodiversity assessment through high-resolution macroecological modelling

Global biodiversity indicators are often derived by intersecting observed or projected changes in anthropogenic pressures with underlying patterns in the distribution of biodiversity. However these patterns are typically delineated at a coarser resolution than the key ecological processes shaping both land-use and biological distributions. The ‘Biogeographic modelling Infrastructure for Large-scaled Biodiversity Indicators’ (BILBI) integrates advances in macroecological modelling, informatics, remote sensing and high-performance computing to assess spatio-temporal change in collective properties of biodiversity, particularly beta diversity, at ~1 km grid resolution across the entire terrestrial surface of the planet. BILBI greatly refines the mapping of biodiversity patterns relative to more traditional surrogates such as ecoregions and species range maps. This capability is already proving of considerable value in informing global assessments through: 1) generation of indicators of past-to-present change in biodiversity resulting from habitat transformation or protection; and 2) projection of potential future change in biodiversity resulting from alternative global-change scenarios.

Atmospheric indices allow anticipating the incidence of jellyfish coastal swarms

Jellyfish swarms affect littoral ecosystems, are unpleasant for bathers and jeopardize coastal socio-economic sectors. Anticipating the incidence of jellyfish swarms could be useful for implementing preventive management measures. Macroclimatic indices are good candidates for this type of anticipation since they are macro-ecologically related to oceanographic characteristics that affect marine species after a certain time lag. An increase of jellyfish swarms has been recently reported in the Mediterranean Sea. From 2005 to 2018, jellyfish swarms in the littoral of the province of Malaga (Spain, northwest coast of the Alboran Sea), mainly formed by the mauve stinger (Pelagia noctiluca), were frequent during summer. We recorded data on jellyfish swarm incidence in the province of Malaga from 2005 to 2018 using the reports in local newspapers, searches in Google Search Engine, and a citizen science application for mobile phones as information sources. With this information, we classified the period in years of low, medium and high incidence of jellyfish swarms. Then, we tested if the known effects of the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) in winter on the sea surface temperature (SST) during the year, which in turn affects the proliferation of jellyfish, could explain the inter-annual variation in this incidence. Our hypothesis significantly explained the variation in the medium versus low incidence of jellyfish swarms, with medium incidence in the summers of higher SST. This suggests that medium incidence of jellyfish swarms was caused by the proliferation of jellyfish. This also suggests that years of medium incidence of jellyfish in the beach during summer could be anticipated by computing the average AO and NAO values of the previous winter. Years of high incidence of swarms could not be explained by this process. We speculate that they may be caused by a change in the distribution of the swarms rather than by proliferation. Jellyfish may be pushed from the pelagic western anticyclonic gyre of the Alboran Sea to the northern coast by eddies that are formed when, as recent literature has shown, this gyre is weakened by westerly winds and the Atlantic jet. Citizen science has contributed useful data to build macroecological models that may result in better management plans based on scientific data.

Scaling of movements with body mass in a small opossum: evidence for an optimal body size in mammals

Abstract Movement by mammals generally increases with body size, described by a positive exponent scaling with either home range area or day range distances. Below ca. 100 g, however, interspecific comparisons suggest a negative scaling, increasing movement with decreasing body size. Such a pattern is expected from the rising costs of thermoregulation below ca. 100 g, implying that it should also be observed in intraspecific comparisons. We tested this hypothesis by investigating the scaling exponent of daily home range with body mass for a small (< 100 g) marsupial, the gray slender mouse opossum, Marmosops incanus. We tracked 85 opossums (56 M, 29 F) with a spool-and-line device between August 1998 and October 2005 in the Serra dos Órgãos National Park, a region of Atlantic Forest in the State of Rio de Janeiro, Brazil. Individual paths were mapped and daily home ranges quantified by the minimum convex polygon encompassing each path. We formulated linear models and compared them using Akaike information criteria. The best-supported model for females had only climatic season as a main determinant of daily home range, whereas the best model for males had body mass and reproductive season as the main effects. As predicted, the scaling exponent of daily home range with body mass of males was negative, in contrast with positive intraspecific exponents for opossums > 100 g estimated in a previous study. The inversion in scaling relationships around 100 g in opossums supports the rising costs of thermoregulation as the main cause of this general pattern in mammals. Effects of body mass are generally weak in intraspecific comparisons, but might still be detected after standardizing other effects, opening new possibilities for testing macroecological models at smaller scales. Espera-se que a quantidade de movimento de mamíferos aumente com o tamanho corporal, descrita por um expoente de escala positivo, tanto para área de vida quanto para distâncias diárias de deslocamento. Abaixo de ca. 100 g, comparações interespecíficas sugerem um expoente negativo, áreas de movimento aumentando com menores tamanhos de corpo. Este padrão é apoiado pelo custo crescente de termorregulação abaixo de ca. 100 g, que implica que também ocorreria em comparações intraespecíficas. Testamos esta hipótese investigando o expoente de escala da área de vida diária com a massa corporal em um pequeno (< 100 g) marsupial, Marmosops incanus. Indivíduos foram rastreados com carretel-de-rastreamento entre agosto de 1998 e outubro de 2005, no Parque Nacional da Serra dos Órgãos, uma região de Mata Atlântica no Estado do Rio de Janeiro, Brasil. Trajetórias individuais foram mapeadas e sua área de vida diária medida pelo polígono convexo mínimo envolvendo cada trajetória. Oitenta e cinco indivíduos foram rastreados, sendo 29 fêmeas e 56 machos. Modelos lineares foram formulados e comparados com o Critério de Informação de Akaike. O modelo com maior suporte para fêmeas teve apenas estação climática como determinante principal da área de vida diária, enquanto o melhor modelo para machos teve massa corporal e estação reprodutiva como efeitos principais. O expoente de escala de área de vida diária com massa corporal de machos foi negativo, contrastando como os expoentes positivos para marsupiais > 100 g estimados em um estudo anterior. A inversão de relações de escala em torno de 100 g nestes marsupiais apoia que custos crescentes de termorregulação sejam a causa principal deste padrão geral em mamíferos. Os efeitos da massa corporal são geralmente fracos em comparações intraespecíficas, mas podem ser detectados após a exclusão de outros efeitos, abrindo novas possibilidades para testar modelos macroecológicos em escalas menores.

A comparison of macroecological and stacked species distribution models to predict future global terrestrial vertebrate richness

AbstractAimPredicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S‐SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S‐SDMs. Here, we aim to compare the results of two model types (S‐SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used.LocationGlobal land, excluding Antarctica.TaxonAmphibians, birds and mammals.MethodsWe fitted S‐SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S‐SDMs and MEMs and for current conditions also to extent‐of‐occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning.ResultsUnder current conditions, species richness predictions from MEMs and S‐SDMs were strongly correlated with EOO‐based species richness. However, both model types over‐predicted areas with low and under‐predicted areas with high species richness. Outputs from MEMs and S‐SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type.Main conclusionsBoth model types were able to reproduce EOO‐based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S‐SDMs are useful for looking at potential species‐specific responses.

How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems

AbstractAimBiodiversity prediction becomes increasingly important in the face of global diversity loss, whereas substantial challenges still exist in both conceptual and technical aspects. There exist many predictive models, and an integrative evaluation can help understand their performance in handling the multifacets of biodiversity. This study aims to evaluate the performance of these modelling approaches to predict both α‐ and β‐diversity in diverse ecological contexts.LocationNorth Yellow Sea, China.MethodsThe biodiversity models follow three strategies, “assemble first, predict later”, “predict first, assemble later” and “assemble and predict together”. Hill diversity profile, Fisher's log‐series parameter and the distance decay of similarity are used to measure α‐ and β‐diversity. The evaluation study is conducted based on seasonal bottom trawl surveys from October 2016 to August 2017 in North Yellow Sea, China, allocated to coastal and offshore areas. We evaluate the predictive power of the models using cross‐validation.ResultsFollowing the “assemble first, predict later” approach, macroecological model (MEM) provided the most accurate prediction overall, whereas stacked species distribution model (SSDM) and joint species distribution model (JSDM), following the second and third modelling approaches, tended to overestimate α‐diversity and underestimate β‐diversity. The performances of SSDM and JSDM could be improved by moderately down‐weighting rare species. The relative performances of the three modelling approaches were consistent among seasons and spatial regions.Main conclusionsThe superior performances of MEM in a range of temporal and spatial contexts favour the “assemble first, predict later” approach and imply a tight community assembly in the studied area. The overall predictive powers of varying models suggest that the spatial pattern of marine biodiversity could be fairly well predicted with commonly accessible hydrologic data in a mesoscales. The approach of multi‐model evaluations is applicable to a variety of ecosystems for biodiversity prediction.

Evaluation of species boundaries in sympatric and parapatric populations of Mesoamerican toads

AbstractApproaches that integrate multiple independent, yet complimentary, lines of evidence have been effectively utilized to identify and evaluate species diversity. Integrative approaches are especially useful in taxa that exhibit cryptic diversity and are highly morphologically conserved, as well as organisms whose distributions may be sympatric or parapatric. The Incilius coccifer complex in Honduras is comprised of three putative taxa: I. coccifer, I. ibarrai and I. porteri. The taxonomy of the I. coccifer complex has been a source of debate among specialists, with some recognizing three species, while others choose to recognize one widespread taxon. To assess species boundaries and evaluate the taxonomic structure for the I. coccifer complex, we utilized a combination of comprehensive field sampling, molecular phylogenetics and macroecological modelling. Using 58 samples representing all three putative taxa, we generated sequence data from the mitochondrial loci 16S and COI in order to assess genetic diversity and phylogenetic relationships, and tested putative species boundaries using General Mixed Yule‐Coalescent models. To evaluate macroecological differences in the distribution of putative taxa, we utilized maximum entropy modelling and identified areas of suitable and non‐suitable habitat, as well as identifying potential areas of overlap between species habitats. We recovered three clades that broadly correspond to the three named taxa that, while being monophyletic, are separated by relatively small genetic distances. Species distribution models revealed that I. coccifer is macroecologically different than the other two taxa, but that I. ibarrai and I. porteri are highly similar. We uncovered cases of sympatry between pairs of species in at least three localities in Honduras, suggesting the potential for hybridization in these closely related lineages.

Metabolic impacts of climate change on marine ecosystems: Implications for fish communities and fisheries

AbstractAimClimate change will reshape marine ecosystems over the 21st century through diverse and complex mechanisms that are difficult to assess quantitatively. Here, we characterize expectations for how marine community biomass will respond to the energetic consequences of changes in primary production and temperature‐dependent metabolic rates, under a range of fishing/conservation scenarios.LocationGlobal ocean.Time period1950–2100.Major taxa studiedCommercially harvested marine ectotherms (‘fish’).MethodsWe use a size‐structured macroecological model of the marine ecosystem, coupled with a catch model that allows for calibration with global historical data and simulation of fishing. We examine the four energetic mechanisms that, within the model framework, determine the community response to climate change: net primary production, phytoplankton cell size, and the temperature dependencies of growth and natural mortality.ResultsClimate change decreases the modelled global fish community biomass by as much as 30% by 2100. This results from a diminished energy supply to upper trophic levels as photosynthesis becomes more nutrient limited and phytoplankton cells shrink, and from a temperature‐driven increase of natural mortality that, together, overwhelm the effect of accelerated somatic growth rates. Ocean circulation changes drive regional variations of primary production, producing patterns of winners and losers that largely compensate each other when averaged globally, whereas decreasing phytoplankton size drives weaker but more uniformly negative changes. The climate impacts are similar across the range of conservation scenarios but are slightly amplified in the strong conservation scenarios owing to the greater role of natural mortality.Main conclusionsThe spatial pattern of climate impacts is mostly determined by changes in primary production. The overall decline of community biomass is attributed to a temperature‐driven increase of natural mortality, alongside an overall decrease in phytoplankton size, despite faster somatic growth. Our results highlight the importance of the competition between accelerated growth and mortality in a warming ocean.

Strengthening the contribution of macroecological models to conservation practice

AbstractIssueOwing to their large‐scale scope and emphasis on prediction, macroecological models have the potential to provide key contributions to evidence‐based conservation practice. However, examples of macroecological modelling outputs directly influencing conservation practice and decision‐making remain rare. The general barriers to implementation of ecological research into conservation practice have been discussed at length, whereas much less attention has been given to the specific barriers faced by macroecological modelling research.EvidenceHere, I start to address this gap by discussing how two important barriers could be overcome in a practical manner, because their burden falls primarily on the researcher. The first barrier is the potential perception of macroecological models as “black boxes” by potential end‐users, who may have little time and/or quantitative training to inspect the underlying methods fully. The second barrier is the difficulty in effective translation of the uncertainty inherent to most macroecological models, given the high threshold for weight of evidence required to support most decisions. To overcome these barriers, I put forward a number of solutions, most of which rely on researchers agreeing to and adopting model documentation and communication standards already in existence.ConclusionI conclude by introducing the bigger challenges ahead for the macroecology–conservation practice interface: transformation of publication incentives and establishment of a two‐way flow of knowledge between macroecologists and conservation practitioners. Macroecologists can contribute much‐needed evidence to support conservation decisions. However, fundamental changes to their research communication standards, their publication incentives and their understanding of regulatory settings will be needed to ensure that macroecological contributions are considered in practice.

Using macroecological constraints on spatial biodiversity predictions under climate change: the modelling method matters

The prevailing method for estimating the potential impact of future climate change on biological communities is to stack binary predictions from species distribution models (binary stacked species distribution models, bS-SDM). However, it has been argued that bS-SDM may overestimate species richness and, hence, community composition. Alternative approaches, such as SESAM (‘Spatially Explicit Species Assemblage Modelling’), explicitly incorporate limits to species richness, preventing overestimation. We compared richness and taxonomic composition estimates as predicted by SESAM and bS-SDM for Mediterranean bird communities both in the present day and as projected in the future under simulated climate change scenarios. We trained single-species distribution models (S-SDM) and direct macroecological richness models (MEM) for 81 bird species, using climate, topographic, land-use and human-pressure indicators as predictors. Then, we compared and evaluated the models’ predictions. Species richness as predicted by bS-SDM was more accurate than under SESAM for present-day communities. Taxonomic composition was well predicted under both methods. However, we detected significant differences in future projections. Under bS-SDM, increased suitable area for a number of species leads to important changes in community composition and predicts higher levels of diversity in the future. In stark contrast, SESAM predicts lower species richness in the future and strong homogenization of bird communities across space. This study shows how the choice of the modelling approach drives substantially different expectations about future community composition under climate change. We therefore recommend contrasting predictions generated under different modelling approaches to gain better understanding of possible future scenario of biodiversity change. In addition, trasferability tests (i.e. hindcasting to past or predicting from the past to the present) should be used to effectively compare S-SDM and SESAM predictive abilities.

Are stacked species distribution models accurate at predicting multiple levels of diversity along a rainfall gradient?

AbstractWe use observed patterns of species richness and composition of ant communities along a 1000 mm rainfall gradient in northern Australian savanna to assess the accuracy of species richness and turnover predictions derived from stacked species distribution models (S‐SDMs) and constrained by macroecological models (MEMs). We systematically sampled ants at 15 sites at 50 km intervals along the rainfall gradient in 2012 and 2013. Using the observed data, we created MEMs of species richness, composition and turnover. We built distribution models for 135 of the observed species using data from museum collections and online databases. We compared two approaches of stacking SDMs and three modelling algorithms to identify the most accurate way of predicting richness and composition. We then applied the same beta diversity metrics to compare the observed versus predicted patterns. Stacked SDMs consistently over‐predicted local species richness, and there was a mismatch between the observed pattern of richness estimated from the MEM, and the pattern predicted by S‐SDMs. The most accurate richness and turnover predictions occurred when the stacked models were rank‐ordered by their habitat suitability and constrained by the observed MEM richness predictions. In contrast with species richness, the predictions obtained by the MEM of community similarity, composition and turnover matched those predicted by the S‐SDMs. S‐SDMs regulated by MEMs may therefore be a useful tool in predicting compositional patterns despite being unreliable estimators of species richness. Our results highlight that the choice of species distribution model, the stacking method used, and underlying macroecological patterns all influence the accuracy of community assembly predictions derived from S‐SDMS.

Learning from model errors: Can land use, edaphic and very high‐resolution topo‐climatic factors improve macroecological models of mountain grasslands?

AbstractAimAssess the potential of new predictors (land use, edaphic factors and high‐resolution (HR) topographic and climatic variables, i.e. topo‐climatic) to improve the prediction of plant community functional traits (specific leaf area, vegetative height and seed mass) and species richness in models of mountain grasslands.LocationThe western Swiss Alps.MethodsUsing 912 grassland plots, we constructed predictive models for community‐weighted means of plant traits and species richness using HR (25 m) topo‐climatic predictors traditionally used in previous modelling studies in this area. In addition, 78 new plots were sampled for evaluation and error assessment in four narrower sets of homogenous conditions based on predictions by the topo‐climatic models within two elevational belts (montane and alpine). New, finer‐scale predictors were generated from direct field measurements or very high‐resolution (VHR; 5 m) numerical data. We then used multimodel inference to test the capacity of these finer predictors to explain part of the residual variance in the initial topo‐climatic models.ResultsWe showed that the finer‐scale predictors explained up to 44% of the residual variance in the classical topo‐climatic models. The VHR topographic position, soil C/N ratio and pH performed notably well in our analysis. Land use (farming intensity) was highlighted as potentially important in montane grasslands, but improvements were only significant for species richness predictions.Main conclusionsCompared with previously used topo‐climatic models, the new, finer‐scale predictors significantly improved the prediction of all traits and species richness in alpine plant communities and that of specific leaf area and richness in montane grasslands. The differences in the importance of the predictors, dependent on both trait and position along the elevational gradient, highlight the different factors that shape the distribution of species and communities along elevational gradients.

Stacked species distribution and macroecological models provide incongruent predictions of species richness for Drosophilidae in the Brazilian savanna

Abstract We tested the adequacy of two richness‐modelling approaches within the ‘spatially explicit species assemblage modelling’ (SESAM) framework for drosophilid flies in a tropical biome. The pattern of drosophilid species richness throughout the Brazilian savanna was investigated by comparing richness estimates from macroecological models (MEM) and stacked species distribution models (S‐SDM). We used occurrence records for macroecological modelling and to generate geographic ranges by modelling species’ niches, which were stacked to generate SDM richness. Richness predictions were compared between models and with empirical data from well‐sampled areas. The spatial variation in drosophilid richness for both estimates revealed more species in the central and south‐eastern regions of the biome. Nonetheless, MEM generated a more fragmented pattern than S‐SDM, with scattered patches of high richness. S‐SDM produced richness estimates nearer to the empirical values than MEM, which in turn strongly underestimated richness. The correlation between S‐SDM and observed richness suggests that climate is the major (indirect) driver of drosophilid richness in the Brazilian savanna. Richness estimates based on macroecological modelling are, however, almost certainly affected by inventory incompleteness and sampling bias. We emphasise that S‐SDM can be a valuable approach to explore species richness patterns in poorly sampled regions.

Improving spatial predictions of taxonomic, functional and phylogenetic diversity

Abstract In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling (SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling (MEM—SSDM‐MEM) approaches to impose a limit on the number of species co‐occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices. We found that coupling the SSDM with the MEM improves the overall predictions for the three diversity facets compared with those of the SSDM alone. The accuracy of the SSDM predictions for the diversity indices varied greatly along the elevation gradient, and when considering broad to more exact structure in the functional and phylogenetic trees, the SSDM‐MEM predictions were more stable. SSDM‐MEM moderately but significantly improved the prediction of taxonomic diversity, which was mainly driven by the corrected number of predicted species. The performance of both modelling frameworks increased when predicting the functional and phylogenetic diversity indices. In particular, fair predictions of the taxonomic composition by SSDM‐MEM led to increasingly accurate predictions of the functional and phylogenetic indices, suggesting that the compositional errors were associated with species that were functionally or phylogenetically close to the correct ones; however, this did not always hold for the SSDM predictions. Synthesis. In this study, we tested the use of a recently published approach that couples species distribution and macroecological models to provide the first predictions of the distribution of multiple facets of plant diversity: taxonomic, functional and phylogenetic. Moderate but significant improvements were obtained; thus, our results open promising avenues for improving our ability to predict the different facets of biodiversity in space and time across broad environmental gradients when functional and phylogenetic information is available.

Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub‐disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species’ presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change.

Aquatic biodiversity in Europe: a unique dataset on the distribution of Trichoptera species with important implications for conservation

Freshwater ecosystems are known to harbour a rich diversity of habitats and species, but knowledge on the actual distribution of many species still remains scattered or unknown. Supported through the BioFresh project, we collected occurrence records of the insect order Trichoptera throughout Europe. By addressing 82 caddisfly experts, we compiled over 600,000 georeferenced occurrence records, 441,000 of which represent adult specimens. We evaluated the dataset regarding the caddisfly distribution based on freshwater ecoregions. This analysis reveals areas with high Trichoptera biodiversity and centres of endemism in southern Europe (e.g. Spain, Italy and the Balkans) as well as in mountainous regions (e.g. Alps). Also, data-deficient regions become obvious. This is either caused by missing experts providing occurrence records or by the inability to mobilise experts and their data of a certain region. Still, the database ranks among the most comprehensive actual distribution data collections of freshwater invertebrates. The database represents a highly valuable information source for a variety of macro-ecological analyses and modelling scenarios, and it could be the base for a European-wide IUCN Red List of threatened caddisfly species that supports conservation policy decisions.

Effects of functional traits on the prediction accuracy of species richness models

AbstractAimWe assess main ecological determinants affecting the comparative performance of macroecological models (MEMs) that model species richness directly, and stacked species distribution models based on stacking probabilities (pS‐SDMs) and binary predictions (bS‐SDMs). Specifically, we aimed to understand how statistical effects such as prevalence and environmental heterogeneity are entangled with species' ecology in Swiss avian assemblages.LocationSwitzerland.MethodsWe tested for statistical and ecological effects on overprediction and underprediction by regressing species richness residuals against community‐averaged values of prevalence, functional traits and functional dissimilarity. Further, we defined bird functional groups through hierarchical clustering and compared accuracy of species richness predictions between groups to understand the differences between model types and ecological determinants thereof. Last, we tested how accuracy of species assemblages constructed from bS‐SDMs relates to species' functional characteristics.ResultsUnderprediction of high diversity sites by pS‐SDMs and MEMs was mainly explained by prevalence, whereas overprediction of low diversity sites was strongly affected by diet and habitat traits, and increased with functional dissimilarity. Model performances varied strongly between functional groups with more accurate and less biased predictions for generalist species groups. Critically, overprediction bias in richness predictions by bS‐SDMs was uncorrelated with assemblage prediction success.Main conclusionsThe reliability of all community models tested here strongly depended on functional species' characteristics related mainly to diet, foraging and breeding habitat. This underlines the need to incorporate all relevant and species‐specific or functional group‐specific ecological filters in the models. Improved prediction accuracy of species richness will require finer‐resolved environmental predictors that better describe available niche space and account for specific spatial and resource requirements of different species. More research is needed to understand the relationship between accuracy of species richness and species assemblage predictions in bS‐SDMs as well as the role of biotic interactions.

Disproportionate Climate-Induced Range Loss Forecast for the Most Threatened African Antelopes

Despite increasing evidence that climatic changes are having a widespread effect on the global distribution and abundance of wildlife [1, 2], the key question of whether the ranges of species that are already threat-listed are likely to be disproportionately affected lacks quantitative assessment. According to the "small-range climate-hypersensitivity hypothesis," we predict small range size to be directly linked to large climate-induced range reduction. Antelopes, an exemplary macroecological model due to their striking ecological diversity and species richness, present an ideal opportunity to test this. Here we provide the first empirical evidence that climate change will cause a disproportionate decline in African antelopes with small geographic ranges, which places the most threatened taxa in double jeopardy. This substantiates our theoretical expectation that the link between small range size and large climate-induced range reduction is a general phenomenon. Our empirically based models also allow specific recommendations for mitigating climate-induced species declines. Gap analysis shows high priorities for antelope conservation to include creation of new protected areas in the horn of Africa and Liberia, aswell as improved connectivity between existing protected areas. Predicted extinction of four species unable to reach areas with suitable climatic conditions by 2080 moreover highlights a potentially important role for ex situ conservation. The study emphasizes the urgent need to incorporate climate change into the IUCN threat assessment by extending the timeframe over which population trends are assessed [3].

Predicting Consumer Biomass, Size-Structure, Production, Catch Potential, Responses to Fishing and Associated Uncertainties in the World's Marine Ecosystems.

Existing estimates of fish and consumer biomass in the world’s oceans are disparate. This creates uncertainty about the roles of fish and other consumers in biogeochemical cycles and ecosystem processes, the extent of human and environmental impacts and fishery potential. We develop and use a size-based macroecological model to assess the effects of parameter uncertainty on predicted consumer biomass, production and distribution. Resulting uncertainty is large (e.g. median global biomass 4.9 billion tonnes for consumers weighing 1 g to 1000 kg; 50% uncertainty intervals of 2 to 10.4 billion tonnes; 90% uncertainty intervals of 0.3 to 26.1 billion tonnes) and driven primarily by uncertainty in trophic transfer efficiency and its relationship with predator-prey body mass ratios. Even the upper uncertainty intervals for global predictions of consumer biomass demonstrate the remarkable scarcity of marine consumers, with less than one part in 30 million by volume of the global oceans comprising tissue of macroscopic animals. Thus the apparently high densities of marine life seen in surface and coastal waters and frequently visited abundance hotspots will likely give many in society a false impression of the abundance of marine animals. Unexploited baseline biomass predictions from the simple macroecological model were used to calibrate a more complex size- and trait-based model to estimate fisheries yield and impacts. Yields are highly dependent on baseline biomass and fisheries selectivity. Predicted global sustainable fisheries yield increases ≈4 fold when smaller individuals (< 20 cm from species of maximum mass < 1kg) are targeted in all oceans, but the predicted yields would rarely be accessible in practice and this fishing strategy leads to the collapse of larger species if fishing mortality rates on different size classes cannot be decoupled. Our analyses show that models with minimal parameter demands that are based on a few established ecological principles can support equitable analysis and comparison of diverse ecosystems. The analyses provide insights into the effects of parameter uncertainty on global biomass and production estimates, which have yet to be achieved with complex models, and will therefore help to highlight priorities for future research and data collection. However, the focus on simple model structures and global processes means that non-phytoplankton primary production and several groups, structures and processes of ecological and conservation interest are not represented. Consequently, our simple models become increasingly less useful than more complex alternatives when addressing questions about food web structure and function, biodiversity, resilience and human impacts at smaller scales and for areas closer to coasts.

Differential effects of environmental heterogeneity on global mammal species richness

AbstractAimSpatial environmental heterogeneity (EH) is an important driver of species richness, affecting species coexistence, persistence and diversification. EH has been widely studied in ecology and evolution and quantified in many different ways, with a strong bias towards a few common measures of EH like elevation range. Here, we calculate 51 measures of EH within grid cells world‐wide across three spatial grains to investigate similarities and differences among these measures. Moreover, we compare the association between species richness of terrestrial mammals and each EH measure to assess the impact of methodological choices on EH–richness relationships found by standard macroecological modelling approaches.LocationGlobal.MethodsWe derive 51 measures of EH from nine variables related to the five subject areas land cover, vegetation, climate, soil and topography, using nine calculation methods. We first explore differences among these EH measures with correlation and principal components analyses. We then analyse the relationship between mammal species richness and each EH measure alone and while accounting for effects of current climate, regional biogeographic history and human influence. We assess the impact of subject area and method of calculation of EH measures on model support using conditional inference trees.ResultsDespite some redundancy, correlations (rs = −0.45 to 1.00, median 0.35) and spatial patterns indicate clear differences between the EH measures. We find clear effects of subject area and calculation method on the importance of EH measures for mammal species richness. Measures of climatic and topographic EH and measures calculated as counts and ranges (as against, for example, coefficient of variation) receive particularly high model support across all spatial grains.Main conclusionsThe outcome of broad‐scale EH–richness studies is greatly determined by methodological decisions on calculation of measures and statistical analysis. These decisions should therefore be made carefully with regard to the hypothesis and mechanism of interest.