Can ecological niche models be used to accurately predict the distribution of invasive insects? A case study of Hyphantria cunea in China

In recent decades, ecological niche models (ENMs) have been widely used to predict suitable habitats for species. However, for invasive organisms, the prediction accuracy is unclear. In this study, we employed the most widely used maximum entropy (MaxEnt) model and ensemble model (EM) Biomod2 and verified the practical effectiveness of the ENM in predicting the distribution areas of invasive insects based on the true occurrence of Hyphantria cunea in China. The results showed that when only limited data of invasive areas were used, the two ENMs could not effectively predict the distribution of suitable habitats of H. cunea, although the use of global data can greatly improve the prediction accuracy of ENMs. When analyzing the same data, Biomod2's prediction accuracy was significantly better than that of MaxEnt. For long-term predictions, the area of suitable habitat predicted by the ENMs was much greater than the occurrence area; for short-term predictions, the accuracy of the predicted area was significantly improved. Under the current conditions, the area of suitable habitat for H. cunea in China is 118 × 104 km2, of which 59.32% is moderately or highly suitable habitat. Future climate change could significantly increase the suitable habitat area of H. cunea in China, and the predicted area of suitable habitats in all climate scenarios exceeded 355 × 104 km2, accounting for 36.98% of the total land area in China. This study demonstrates the use of ENMs to study invasive insects and provides a reference for the management of H. cunea in China.

Priority planting area planning for cash crops under heavy metal pollution and climate change: A case study of Ligusticum chuanxiong Hort

INTRODUCTION

Soil pollution by heavy metals and climate change pose substantial threats to the habitat suitability of cash crops. Discussing the suitability of cash crops in this context is necessary for the conservation and management of species. We developed a comprehensive evaluation system that is universally applicable to all plants stressed by heavy metal pollution.

METHODS

The MaxEnt model was used to simulate the spatial distribution of Ligusticum chuanxiong Hort within the study area (Sichuan, Shaanxi, and Chongqing) based on current and future climate conditions (RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios). We established the current Cd pollution status in the study area using kriging interpolation and kernel density. Additionally, the three scenarios were used in prediction models to simulate future Cd pollution conditions based on current Cd pollution data. The current and future priority planting areas for L. chuanxiong were determined by overlay analysis, and two levels of results were obtained.

RESULTS

The results revealed that the current first- and secondary-priority planting areas for L. chuanxiong were 2.06 ×10³ km² and 1.64 ×10⁴ km², respectively. Of these areas, the seven primary and twelve secondary counties for current L. chuanxiong cultivation should be given higher priority; these areas include Meishan, Qionglai, Pujiang, and other regions. Furthermore, all the priority zones based on the current and future scenarios were mainly concentrated on the Chengdu Plain, southeastern Sichuan and northern Chongqing. Future planning results indicated that Renshou, Pingwu, Meishan, Qionglai, Pengshan, and other regions are very important for L. chuanxiong planting, and a pessimistic scenario will negatively impact this potential planting. The spatial dynamics of priority areas in 2050 and 2070 clearly fluctuated under different prediction scenarios and were mainly distributed in northern Sichuan and western Chongqing.

DISCUSSION

Given these results, taking reasonable measures to replan and manage these areas is necessary. This study provides. not only a useful reference for the protection and cultivation of L. chuanxiong, but also a framework for analyzing other cash crops.

Climate Change Impacts on the Potential Distribution of Apocheima cinerarius (Erschoff) (Lepidoptera: Geometridae)

Among the impacts of ongoing and projected climate change are shifts in the distribution and severity of insect pests. Projecting those impacts is necessary to ensure effective pest management in the future. Apocheima cinerarius (Erschoff) (Lepidoptera: Geometridae) is an important polyphagous forest pest in China where causes huge economic and ecological losses in 20 provinces. Under historical climatic conditions, the suitable areas for A. cinerarius in China are mainly in the northern temperate zone (30-50° N) and the southern temperate zone (20-60° S). Using the CLIMEX model, the potential distribution of the pest in China and globally, both historically and under climate change, were estimated. Suitable habitats for A. cinerarius occur in parts of all continents. With climate change, its potential distribution extends northward in China and generally elsewhere in the northern hemisphere, although effects vary depending on latitude. In other areas of the world, some habitats become less suitable for the species. Based on the simulated growth index in CLIMEX, the onset of A. cinerarius would be earlier under climate change in some of its potential range, including Spain and Korea. Measures should anticipate the need for prevention and control of A. cinerarius in its potential extended range in China and globally.

Major population splits coincide with episodes of rapid climate change in a forest-dependent bird

Climate change influences population demography by altering patterns of gene flow and reproductive isolation. Direct mutation rates offer the possibility for accurate dating on the within-species level but are currently only available for a handful of vertebrate species. Here, we use the first directly estimated mutation rate in birds to study the evolutionary history of pied flycatchers (Ficedula hypoleuca). Using a combination of demographic inference and species distribution modelling, we show that all major population splits in this forest-dependent system occurred during periods of increased climate instability and rapid global temperature change. We show that the divergent Spanish subspecies originated during the Eemian-Weichselian transition 115-104 thousand years ago (kya), and not during the last glacial maximum (26.5-19 kya), as previously suggested. The magnitude and rates of climate change during the glacial-interglacial transitions that preceded population splits in pied flycatchers were similar to, or exceeded, those predicted to occur in the course of the current, human-induced climate crisis. As such, our results provide a timely reminder of the strong impact that episodes of climate instability and rapid temperature changes can have on species' evolutionary trajectories, with important implications for the natural world in the Anthropocene.

Factors Influencing the Distribution of Endemic Damselflies in Vanuatu

Simple Summary

Predicting the distribution of endemic insects is vital to continual study and conservation efforts. Here we used ecological niche models and pH data to determine which environmental factors may be influencing the distribution of a group of damselflies in Vanuatu. We tested the utility of niche models in this context and found pH to be a strong predictor for this genus.

Abstract

Vanuatubasis Ober and Staniczek is a genus of damselfly endemic to Vanuatu. Little is known about the distribution and general natural history of the genus. We present the results of 14 weeks of fieldwork in Vanuatu to provide a better understanding of the biology of this genus. Specifically, we tested ecological niche models to predict the presence of Vanuatubasis throughout the region and explored how water pH may play a role in their distribution and ecology. The results of this fieldwork refined our model and further predicted the presence of this genus on additional islands. We also found stream pH as a strong predictor for the presence of Vanuatubasis, with their presence in alkaline streams significantly higher (p < 0.001). The mean pH for those streams where the genus was collected was 8.44 (n = 53).

The potential global distribution of the papaya mealybug, Paracoccus marginatus, a polyphagous pest

BACKGROUND

The papaya mealybug, Paracoccus marginatus, is a highly polyphagous invasive pest that affects more than 200 plants, many of which are of economic importance. We modelled the potential distribution of P. marginatus using CLIMEX, a process-oriented, climate-based niche model. We combined this model with spatial data on irrigation and cropping patterns to increase the real-world applicability of the model.

RESULTS

The resulting model agreed with known distribution points for this pest and with broad areas where P. marginatus has been reported, but for which no GPS data were available. Our model highlights the potential expansion of P. marginatus into novel areas in Central and East Africa, as well as further expansion in Central America and Asia, as these areas are highly climatically suitable, and have large expanses of suitable crop hosts. It also highlights areas, such as the central and eastern states of the USA as well as the western provinces of China, that are suitable for seasonal invasions of P. marginatus.

CONCLUSION

Our results offer refined resolution on areas with high potential for invasion by P. marginatus. © 2020 Society of Chemical Industry.

Greater topoclimatic control of above- versus below-ground communities

Assessing the degree to which climate explains the spatial distributions of different taxonomic and functional groups is essential for anticipating the effects of climate change on ecosystems. Most effort so far has focused on above-ground organisms, which offer only a partial view on the response of biodiversity to environmental gradients. Here including both above- and below-ground organisms, we quantified the degree of topoclimatic control on the occurrence patterns of >1,500 taxa and phylotypes along a c. 3,000 m elevation gradient, by fitting species distribution models. Higher model performances for animals and plants than for soil microbes (fungi, bacteria and protists) suggest that the direct influence of topoclimate is stronger on above-ground species than on below-ground microorganisms. Accordingly, direct climate change effects are predicted to be stronger for above-ground than for below-ground taxa, whereas factors expressing local soil microclimate and geochemistry are likely more important to explain and forecast the occurrence patterns of soil microbiota. Detailed mapping and future scenarios of soil microclimate and microhabitats, together with comparative studies of interacting and ecologically dependent above- and below-ground biota, are thus needed to understand and realistically forecast the future distribution of ecosystems.

Coexistence barriers confine the poleward range of a globally distributed plant

In the study of factors shaping species' poleward range boundaries, climatic constraints are often assigned greater importance than biotic interactions such as competition. However, theory suggests competition can truncate a species' fundamental niche in harsh environments. We test this by challenging a mechanistic niche model - containing explicit competition terms - to predict the poleward range boundaries of two globally distributed, ecologically similar aquatic plant species. Mechanistic competition models accurately predicted the northern range limits of our study species, outperforming competition-free mechanistic models and matching the predictive ability of statistical niche models fit to occurrence records. Using the framework of modern coexistence theory, we found that relative nonlinearity in competitors' responses to temperature fluctuations maintains their coexistence boundary, highlighting the importance of this fluctuation-dependent mechanism. Our results support a more nuanced, interactive role of climate and competition in determining range boundaries, and illustrate a practical, process-based approach to understanding the determinants of range limits.

Multiple axes of ecological vulnerability to climate change

Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms-niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)-each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies.

Detecting parasite associations within multi-species host and parasite communities

Understanding the role of biotic interactions in shaping natural communities is a long-standing challenge in ecology. It is particularly pertinent to parasite communities sharing the same host communities and individuals, as the interactions among parasites-both competition and facilitation-may have far-reaching implications for parasite transmission and evolution. Aggregated parasite burdens may suggest that infected host individuals are either more prone to infection, or that infection by a parasite species facilitates another, leading to a positive parasite-parasite interaction. However, parasite species may also compete for host resources, leading to the prediction that parasite-parasite associations would be generally negative, especially when parasite species infect the same host tissue, competing for both resources and space. We examine the presence and strength of parasite associations using hierarchical joint species distribution models fitted to data on resident parasite communities sampled on over 1300 small mammal individuals across 22 species and their resident parasite communities. On average, we detected more positive associations between infecting parasite species than negative, with the most negative associations occurring when two parasite species infected the same host tissue, suggesting that parasite species associations may be quantifiable from observational data. Overall, our findings suggest that parasite community prediction at the level of the individual host is possible, and that parasite species associations may be detectable in complex multi-species communities, generating many hypotheses concerning the effect of host community changes on parasite community composition, parasite competition within infected hosts, and the drivers of parasite community assembly and structure.

Expansion of Coccidioidomycosis Endemic Regions in the United States in Response to Climate Change

Coccidioidomycosis (Valley fever) is a fungal disease endemic to the southwestern United States. Across this region, temperature and precipitation influence the extent of the endemic region and number of Valley fever cases. Climate projections for the western United States indicate that temperatures will increase and precipitation patterns will shift, which may alter disease dynamics. We estimated the area potentially endemic to Valley fever using a climate niche model derived from contemporary climate and disease incidence data. We then used our model with projections of climate from Earth system models to assess how endemic areas will change during the 21st century. By 2100 in a high warming scenario, our model predicts that the area of climate-limited endemicity will more than double, the number of affected states will increase from 12 to 17, and the number of Valley fever cases will increase by 50%. The Valley fever endemic region will expand north into dry western states, including Idaho, Wyoming, Montana, Nebraska, South Dakota, and North Dakota. Precipitation will limit the disease from spreading into states farther east and along the central and northern Pacific coast. This is the first quantitative estimate of how climate change may influence Valley fever in the United States. Our predictive model of Valley fever endemicity may provide guidance to public health officials to establish disease surveillance programs and design mitigation efforts to limit the impacts of this disease.

Responses of species abundance distribution patterns to spatial scaling in subtropical secondary forests

To quantify and assess the processes underlying community assembly and driving tree species abundance distributions(SADs) with spatial scale variation in two typical subtropical secondary forests in Dashanchong state-owned forest farm, two 1-ha permanent study plots (100-m × 100-m) were established. We selected four diversity indices including species richness, Shannon-Wiener, Simpson and Pielou, and relative importance values to quantify community assembly and biodiversity. Empirical cumulative distribution and species accumulation curves were utilized to describe the SADs of two forests communities trees. Three types of models, including statistic model (lognormal and logseries model), niche model (broken-stick, niche preemption, and Zipf-Mandelbrodt model), and neutral theory model, were estimated by the fitted SADs. Simulation effects were tested by Akaike's information criterion (AIC) and Kolmogorov-Smirnov test. Results found that the Fagaceae and Anacardiaceae families were their respective dominance family in the evergreen broad-leaved and deciduous mixed communities. According to original data and random sampling predictions, the SADs were hump-shaped for intermediate abundance classes, peaking between 8 and 32 in the evergreen broad-leaved community, but this maximum increased with size of total sampled area size in the deciduous mixed community. All niche models could only explain SADs patterns at smaller spatial scales. However, both the neutral theory and purely statistical models were suitable for explaining the SADs for secondary forest communities when the sampling plot exceeded 40 m. The results showed the SADs indicated a clear directional trend toward convergence and similar predominating ecological processes in two typical subtropical secondary forests. The neutral process gradually replaced the niche process in importance and become the main mechanism for determining SADs of forest trees as the sampling scale expanded. Thus, we can preliminarily conclude that neutral processes had a major effect on biodiversity patterns in these two subtropical secondary forests but exclude possible contributions of other processes.

The role of fish life histories in allometrically scaled food-web dynamics

Body size determines key ecological and evolutionary processes of organisms. Therefore, organisms undergo extensive shifts in resources, competitors, and predators as they grow in body size. While empirical and theoretical evidence show that these size-dependent ontogenetic shifts vastly influence the structure and dynamics of populations, theory on how those ontogenetic shifts affect the structure and dynamics of ecological networks is still virtually absent.Here, we expand the Allometric Trophic Network (ATN) theory in the context of aquatic food webs to incorporate size-structure in the population dynamics of fish species. We do this by modifying a food web generating algorithm, the niche model, to produce food webs where different fish life-history stages are described as separate nodes which are connected through growth and reproduction. Then, we apply a bioenergetic model that uses the food webs and the body sizes generated by our niche model to evaluate the effect of incorporating life-history structure into food web dynamics.We show that the larger the body size of a fish species respective to the body size of its preys, the higher the biomass attained by the fish species and the greater the ecosystem stability. We also find that the larger the asymptotic body size attained by fish species the larger the total ecosystem biomass, a result that holds true for both the largest fish in the ecosystem and each fish species in the ecosystem.This work provides an expanded ATN theory that generates food webs with life-history structure for chosen species. Our work offers a systematic approach for disentangling the effects of increasing life-history complexity in food-web models.

Defaunation effects on plant recruitment depend on size matching and size trade-offs in seed-dispersal networks

Defaunation by humans causes a loss of large animals in many ecosystems globally. Recent work has emphasized the consequences of downsizing in animal communities for ecosystem functioning. However, no study so far has integrated network theory and life-history trade-offs to mechanistically evaluate the functional consequences of defaunation in plant-animal networks. Here, we simulated an avian seed-dispersal network and its derived ecosystem function seedling recruitment to assess the relative importance of different size-related mechanisms. Specifically, we considered size matching (between bird size and seed size) and size trade-offs, which are driven by differences in plant or animal species abundance (negative size-quantity relationship) as well as in recruitment probability and disperser quality (positive size-quality relationship). Defaunation led to impoverished seedling communities in terms of diversity and seed size, but only if models accounted for size matching. In addition, size trade-off in plants, in concert with size matching, provoked rapid decays in seedling abundance in response to defaunation. These results underscore a disproportional importance of large animals for ecosystem functions. Downsizing in ecological networks will have severe consequences for ecosystem functioning, especially in interaction networks that are structured by size matching between plants and animals.

Integrating mechanistic and empirical model projections to assess climate impacts on tree species distributions in northwestern North America

Empirical and mechanistic models have both been used to assess the potential impacts of climate change on species distributions, and each modeling approach has its strengths and weaknesses. Here, we demonstrate an approach to projecting climate-driven changes in species distributions that draws on both empirical and mechanistic models. We combined projections from a dynamic global vegetation model (DGVM) that simulates the distributions of biomes based on basic plant functional types with projections from empirical climatic niche models for six tree species in northwestern North America. These integrated model outputs incorporate important biological processes, such as competition, physiological responses of plants to changes in atmospheric CO₂ concentrations, and fire, as well as what are likely to be species-specific climatic constraints. We compared the integrated projections to projections from the empirical climatic niche models alone. Overall, our integrated model outputs projected a greater climate-driven loss of potentially suitable environmental space than did the empirical climatic niche model outputs alone for the majority of modeled species. Our results also show that refining species distributions with DGVM outputs had large effects on the geographic locations of suitable habitat. We demonstrate one approach to integrating the outputs of mechanistic and empirical niche models to produce bioclimatic projections. But perhaps more importantly, our study reveals the potential for empirical climatic niche models to over-predict suitable environmental space under future climatic conditions.

Contrasting the effects of environment, dispersal and biotic interactions to explain the distribution of invasive plants in alpine communities

Despite considerable efforts devoted to investigate the community assembly processes driving plant invasions, few general conclusions have been drawn so far. Three main processes, generally acting as successive filters, are thought to be of prime importance. The invader has to disperse (1st filter) into a suitable environment (2nd filter) and succeed in establishing in recipient communities through competitive interactions (3rd filter) using two strategies: competition avoidance by the use of different resources (resource opportunity), or competitive exclusion of native species. Surprisingly, despite the general consensus on the importance of investigating these three processes and their interplay, they are usually studied independently. Here we aim to analyse these three filters together, by including them all: abiotic environment, dispersal and biotic interactions, into models of invasive species distributions. We first propose a suite of indices (based on species functional dissimilarities) supposed to reflect the two competitive strategies (resource opportunity and competition exclusion). Then, we use a set of generalised linear models to explain the distribution of seven herbaceous invaders in natural communities (using a large vegetation database for the French Alps containing 5,000 community-plots). Finally, we measure the relative importance of competitive interaction indices, identify the type of coexistence mechanism involved and how this varies along environmental gradients. Adding competition indices significantly improved model's performance, but neither resource opportunity nor competitive exclusion were common strategies among the seven species. Overall, we show that combining environmental, dispersal and biotic information to model invasions has excellent potential for improving our understanding of invader success.

Recurrent evolution of life history ecotypes in sockeye salmon: implications for conservation and future evolution

We examine the evolutionary history and speculate about the evolutionary future of three basic life history ecotypes that contribute to the biocomplexity of sockeye salmon (Oncorhynchus nerka). The ‘recurrent evolution’ (RE) hypothesis claims that the sea/river ecotype is ancestral, a ‘straying’ form with poorly differentiated (meta)population structure, and that highly structured populations of lake-type sockeye and kokanee have evolved repeatedly in parallel adaptive radiations between recurrent glaciations of the Pleistocene Epoch. Basic premises of this hypothesis are consistent with new, independent evidence from recent surveys of genetic variation in mitochondrial and microsatellite DNA: (1) sockeye salmon are most closely related to pink (O. gorbuscha) and chum (O. keta) salmon with sea-type life histories; (2) the sockeye life history ecotypes exist as polyphyletic lineages within large drainages and geographic regions; (3) the sea/river ecotype exhibits less genetic differentiation among populations than the lake or kokanee ecotypes both within and among drainages; and (4) genetic diversity is typically higher in the sea/river ecotype than in the lake and kokanee ecotypes. Anthropogenic modification of estuarine habitat and intensive coastal fisheries have likely reduced and fragmented historic metapopulations of the sea/river ecotype, particularly in southern areas. In contrast, the kokanee ecotype appears to be favoured by marine fisheries and predicted changes in climate.

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad-scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment-only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment-only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.

Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models

Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence of H. verticillata in North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well-documented species like H. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under-reported and unexplored native ranges.

Highly resolved early Eocene food webs show development of modern trophic structure after the end-Cretaceous extinction

Generalities of food web structure have been identified for extant ecosystems. However, the trophic organization of ancient ecosystems is unresolved, as prior studies of fossil webs have been limited by low-resolution, high-uncertainty data. We compiled highly resolved, well-documented feeding interaction data for 700 taxa from the 48 million-year-old latest early Eocene Messel Shale, which contains a species assemblage that developed after an interval of protracted environmental and biotal change during and following the end-Cretaceous extinction. We compared the network structure of Messel lake and forest food webs to extant webs using analyses that account for scale dependence of structure with diversity and complexity. The Messel lake web, with 94 taxa, displays unambiguous similarities in structure to extant webs. While the Messel forest web, with 630 taxa, displays differences compared to extant webs, they appear to result from high diversity and resolution of insect–plant interactions, rather than substantive differences in structure. The evidence presented here suggests that modern trophic organization developed along with the modern Messel biota during an 18 Myr interval of dramatic post-extinction change. Our study also has methodological implications, as the Messel forest web analysis highlights limitations of current food web data and models.

From projected species distribution to food-web structure under climate change

Climate change is inducing deep modifications in species geographic ranges worldwide. However, the consequences of such changes on community structure are still poorly understood, particularly the impacts on food-web properties. Here, we propose a new framework, coupling species distribution and trophic models, to predict climate change impacts on food-web structure across the Mediterranean Sea. Sea surface temperature was used to determine the fish climate niches and their future distributions. Body size was used to infer trophic interactions between fish species. Our projections reveal that 54 fish species of 256 endemic and native species included in our analysis would disappear by 2080-2099 from the Mediterranean continental shelf. The number of feeding links between fish species would decrease on 73.4% of the continental shelf. However, the connectance of the overall fish web would increase on average, from 0.26 to 0.29, mainly due to a differential loss rate of feeding links and species richness. This result masks a systematic decrease in predator generality, estimated here as the number of prey species, from 30.0 to 25.4. Therefore, our study highlights large-scale impacts of climate change on marine food-web structure with potential deep consequences on ecosystem functioning. However, these impacts will likely be highly heterogeneous in space, challenging our current understanding of climate change impact on local marine ecosystems.

Global marine bacterial diversity peaks at high latitudes in winter

Genomic approaches to characterizing bacterial communities are revealing significant differences in diversity and composition between environments. But bacterial distributions have not been mapped at a global scale. Although current community surveys are way too sparse to map global diversity patterns directly, there is now sufficient data to fit accurate models of how bacterial distributions vary across different environments and to make global scale maps from these models. We apply this approach to map the global distributions of bacteria in marine surface waters. Our spatially and temporally explicit predictions suggest that bacterial diversity peaks in temperate latitudes across the world's oceans. These global peaks are seasonal, occurring 6 months apart in the two hemispheres, in the boreal and austral winters. This pattern is quite different from the tropical, seasonally consistent diversity patterns observed for most macroorganisms. However, like other marine organisms, surface water bacteria are particularly diverse in regions of high human environmental impacts on the oceans. Our maps provide the first picture of bacterial distributions at a global scale and suggest important differences between the diversity patterns of bacteria compared with other organisms.

Incorporating uncertainty in predictive species distribution modelling

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Flow regime, temperature, and biotic interactions drive differential declines of trout species under climate change

Broad-scale studies of climate change effects on freshwater species have focused mainly on temperature, ignoring critical drivers such as flow regime and biotic interactions. We use downscaled outputs from general circulation models coupled with a hydrologic model to forecast the effects of altered flows and increased temperatures on four interacting species of trout across the interior western United States (1.01 million km(2)), based on empirical statistical models built from fish surveys at 9,890 sites. Projections under the 2080s A1B emissions scenario forecast a mean 47% decline in total suitable habitat for all trout, a group of fishes of major socioeconomic and ecological significance. We project that native cutthroat trout Oncorhynchus clarkii, already excluded from much of its potential range by nonnative species, will lose a further 58% of habitat due to an increase in temperatures beyond the species' physiological optima and continued negative biotic interactions. Habitat for nonnative brook trout Salvelinus fontinalis and brown trout Salmo trutta is predicted to decline by 77% and 48%, respectively, driven by increases in temperature and winter flood frequency caused by warmer, rainier winters. Habitat for rainbow trout, Oncorhynchus mykiss, is projected to decline the least (35%) because negative temperature effects are partly offset by flow regime shifts that benefit the species. These results illustrate how drivers other than temperature influence species response to climate change. Despite some uncertainty, large declines in trout habitat are likely, but our findings point to opportunities for strategic targeting of mitigation efforts to appropriate stressors and locations.

Evolution of climate niches in European mammals?

Our ability to predict consequences of climate change is severely impaired by the lack of knowledge on the ability of species to adapt to changing environmental conditions. We used distribution data for 140 mammal species in Europe, together with data on climate, land cover and topography, to derive a statistical description of their realized climate niche. We then compared climate niche overlap of pairs of species, selected on the basis of phylogenetic information. In contrast to expectations, related species were not similar in their climate niche. Rather, even species pairs that had a common ancestor less than 1 Ma already display very high climate niche distances. We interpret our finding as a strong interspecific competitive constraint on the realized niche, rather than a rapid evolution of the fundamental niche. If correct, our results imply a very limited usefulness of climate niche models for the prediction of future mammal distributions.