Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species-environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold-adapted species was predicted to have high probabilities of extirpation across its current range-with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

Examining the co-occurrences of human threats within terrestrial protected areas

Human threats to biodiversity are prevalent within protected areas (PAs), undermining their effectiveness in halting biodiversity loss. Certain threats tend to co-occur, resulting in amplified cumulative impact through synergistic effects. However, it remains unclear which threats are related the most. We analyzed a dataset of 71 human threats in 18 013 terrestrial PAs of the European Union's Natura 2000 network, using a Joint Species Distribution Modelling approach, to assess the threats' co-occurrence patterns and potential drivers. Overall, threats were more frequently correlated positively than negatively. Threats related to agriculture and urbanization were correlated strongly with most other threats. Approximately 70% of the variance in our model was explained by country-specific factors, indicating the importance of local drivers. Minimizing the negative impact of key threats can likely reduce the impact of related threats. However, more research is needed to understand better the relationships among threats and, importantly, their combined impact on biodiversity.

Whether including exotic species alters conservation prioritization: a case study in the Min River in southeastern China

Conservation practices from the perspective of functional diversity (FD) and conservation prioritization need to account for the impacts of exotic species in freshwater ecosystems. This work first simulated the influence of exotic species on the values of FD in a schemed mechanistic model, and then a practical case study of conservation prioritization was performed in the Min River, the largest river in southeastern China, to discuss whether including exotic species alters prioritization. The mechanistic model revealed that exotic species significantly altered the expected FD if the number of exotic species occupied 2% of the community. Joint species distribution modelling indicated that the highest FD occurred in the west, northwest and north upstreams of the Min River. Values of FD in 64.69% of the basin decreased after the exotic species were removed from calculation. Conservation prioritization with the Zonation software proved that if first the habitats of exotic species were removed during prioritization, 62.75% of the highest prioritized areas were shifted, average species representation of the endemic species was improved and mean conservation efficiency was increased by 7.53%. Existence of exotic species will significantly alter the metrics of biodiversity and the solution for conservation prioritization, and negatively weighting exotic species in the scope of conservation prioritization is suggested to better protect endemic species. This work advocates a thorough estimate of the impacts of exotic species on FD and conservation prioritization, providing complementary evidence for conservation biology and valuable implications for local freshwater fish conservation.

Uncovering Broad Macroecological Patterns by Comparing the Shape of Species' Distributions along Environmental Gradients

AbstractSpecies' distributions can take many different forms. For example, fat-tailed or skewed distributions are very common in nature, as these can naturally emerge as a result of individual variability and asymmetric environmental tolerances, respectively. Studying the basic shape of distributions can teach us a lot about the ways climatic processes and historical contingencies shape ecological communities. Yet we still lack a general understanding of how their shapes and properties compare to each other along gradients. Here, we use Bayesian nonlinear models to quantify range shape properties in empirical plant distributions. With this approach, we are able to distil the shape of plant distributions and compare them along gradients and across species. Studying the relationship between distribution properties, we revealed the existence of broad macroecological patterns along environmental gradients-such as those expected from Rapoport's rule and the abiotic stress limitation hypothesis. We also find that some aspects of the shape of observed ranges-such as kurtosis and skewness of the distributions-could be intrinsic properties of species or the result of their historical contexts. Overall, our modeling approach and results untangle the general shape of plant distributions and provide a mapping of how this changes along environmental gradients.

When Do Traits Tell More Than Species about a Metacommunity? A Synthesis across Ecosystems and Scales

AbstractLinking species traits with the variation in species assemblages across habitats has often proved useful for developing a more mechanistic understanding of species distributions in metacommunities. However, summarizing the rich tapestry of a species in all of its nuance with a few key ecological traits can also lead to an abstraction that provides less predictability than when using taxonomy alone. As a further complication, taxonomic and functional diversities can be inequitably compared, either by integrating taxonomic-level information into the calculation of how functional aspects of communities vary or by detecting spurious trait-environment relationships. To remedy this, we here synthesize analyses of 80 datasets on different taxa, ecosystems, and spatial scales that include information on abundance or presence/absence of species across sites with variable environmental conditions and the species' traits. By developing analyses that treat functional and taxonomic diversity equitably, we ask when functional diversity helps to explain metacommunity structure. We found that patterns of functional diversity explained metacommunity structure and response to environmental variation in only 25% of the datasets using a multitrait approach but up to 59% using a single-trait approach. Nevertheless, an average of only 19% (interquartile range = 0%-29%) of the traits showed a significant signal across environmental gradients. Species-level traits, as typically collected and analyzed through functional diversity patterns, often do not bring predictive advantages over what the taxonomic information already holds. While our assessment of a limited advantage of using traits to explain variation in species assemblages was largely true across ecosystems, traits played a more useful role in explaining variation when many traits were used and when trait constructs were more related to species' status, life history, and mobility. We propose future research directions to make trait-based approaches and data more helpful for inference in metacommunity ecology.

Seasonal variation in dragonfly assemblage colouration suggests a link between thermal melanism and phenology

Phenology, the seasonal timing of life events, is an essential component of diversity patterns. However, the mechanisms involved are complex and understudied. Body colour may be an important factor, because dark-bodied species absorb more solar radiation, which is predicted by the Thermal Melanism Hypothesis to enable them to thermoregulate successfully in cooler temperatures. Here we show that colour lightness of dragonfly assemblages varies in response to seasonal changes in solar radiation, with darker early- and late-season assemblages and lighter mid-season assemblages. This finding suggests a link between colour-based thermoregulation and insect phenology. We also show that the phenological pattern of dragonfly colour lightness advanced over the last decades. We suggest that changing seasonal temperature patterns due to global warming together with the static nature of solar radiation may drive dragonfly flight periods to suboptimal seasonal conditions. Our findings open a research avenue for a more mechanistic understanding of phenology and spatio-phenological impacts of climate warming on insects.

Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model , to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

Diversity and biogeography of plant phyllosphere bacteria are governed by latitude-dependent mechanisms

Predicting and managing the structure and function of plant microbiomes requires quantitative understanding of community assembly and predictive models of spatial distributions at broad geographic scales. Here, we quantified the relative contribution of abiotic and biotic factors to the assembly of phyllosphere bacterial communities, and developed spatial distribution models for keystone bacterial taxa along a latitudinal gradient, by analyzing 16S rRNA gene sequences from 1453 leaf samples taken from 329 plant species in China. We demonstrated a latitudinal gradient in phyllosphere bacterial diversity and community composition, which was mostly explained by climate and host plant factors. We found that host-related factors were increasingly important in explaining bacterial assembly at higher latitudes while nonhost factors including abiotic environments, spatial proximity and plant neighbors were more important at lower latitudes. We further showed that local plant-bacteria associations were interconnected by hub bacteria taxa to form metacommunity-level networks, and the spatial distribution of these hub taxa was controlled by hosts and spatial factors with varying importance across latitudes. For the first time, we documented a latitude-dependent importance in the driving factors of phyllosphere bacteria assembly and distribution, serving as a baseline for predicting future changes in plant phyllosphere microbiomes under global change and human activities.

Migration, breeding location, and seascape shape seabird assemblages in the northern Gulf of Mexico

The Gulf of Mexico supports many seabird species, yet data gaps describing species composition and habitat use are prevalent. We used vessel-based observations from the Gulf of Mexico Marine Assessment Program for Protected Species to identify and characterize distinct seabird assemblages in the northern Gulf of Mexico (within the U.S. Exclusive Economic Zone; nGoM). Using cluster analysis of 17 seabird species, we identified assemblages based on seabird relative density. Vessel-based surveys documented the location, species, and number of seabirds across the nGoM between 2017-2019. For each assemblage, we identified the (co-)dominant species, spatial distribution, and areas of greater relative density. We also assessed the relationship of the total relative density within each assemblage with environmental, spatial, and temporal covariates. Of the species assessed, 76% (n = 13) breed predominantly outside the nGoM basin. We identified four seabird assemblages. Two assemblages, one dominated by black tern and the other co-dominated by northern gannet/laughing gull, occurred on the continental shelf. An assemblage dominated by sooty tern occurred along the continental slope into pelagic waters. The fourth assemblage had no dominant species, was broadly distributed, and was composed of observations with low relative density ('singles' assemblage). Differentiation of assemblages was linked to migratory patterns, residency, and breeding location. The spatial distributions and relationships of the black tern and northern gannet/laughing gull assemblages with environmental covariates indicate associations with river outflows and ports. The sooty tern assemblage overlapped an area prone to mesoscale feature formation. The singles assemblage may reflect commuting and dispersive behaviors. These findings highlight the importance of seasonal migrations and dynamic features across the seascape, shaping seabird assemblages. Considering the potential far-ranging effects of interactions with seabirds in the nGoM, awareness of these unique patterns and potential links with other fauna could inform future monitoring, research, restoration, offshore energy, and aquaculture development in this highly industrialized sea.

Influence of ecological traits on spatio-temporal dynamics of an elasmobranch community in a heavily exploited basin

Elasmobranchs, which include sharks and batoids, play critical roles in maintaining the integrity and stability of marine food webs. However, these cartilaginous fish are among the most threatened vertebrate lineages due to their widespread depletion. Consequently, understanding dynamics and predicting changes of elasmobranch communities are major research topics in conservation ecology. Here, we leverage long-term catch data from a standardized bottom trawl survey conducted from 1996 to 2019, to evaluate the spatio-temporal dynamics of the elasmobranch community in the heavily exploited Adriatic Sea, where these fish have historically been depleted. We use joint species distribution modeling to quantify the responses of the species to environmental variation while also including important traits such as species age at first maturity, reproductive mode, trophic level, and phylogenetic information. We present spatio-temporal changes in the species community and associated modification of the trait composition, highlighting strong spatial and depth-mediated patterning. We observed an overall increase in the abundance of the dominant elasmobranch species, except for spurdog, which has shown a continued decline. However, our results showed that the present community displays lower age at first maturity and a smaller fraction of viviparous species compared to the earlier observed community due to changes in species' relative abundance. The selected traits contributed considerably to explaining community patterns, suggesting that the integration of trait-based approaches in elasmobranch community analyses can aid efforts to conserve this important lineage of fish.

Field experimental evidence of sandy beach community changes in response to artificial light at night (ALAN)

Artificial light at night (ALAN) is a pervasive but still under-recognized driver of global change. In coastal settings, a large majority of the studies assessing ALAN impacts has focused on individual species, even though it is unclear whether results gathered from single species can be used to predict community-wide responses. Similarly, these studies often treat species as single life-stage entities, ignoring the variation associated with distinct life stages. This study addresses both limitations by focusing on the effects of ALAN on a sandy beach community consisting of species with distinct early- and late-life stages. Our hypothesis was that ALAN alters community structure and these changes are mediated by individual species and also by their ontogenetic stages. A field experiment was conducted in a sandy beach of north-central Chile using an artificial LED system. Samples were collected at different night hours (8-levels in total) across the intertidal (9-levels) over several days in November and January (austral spring and summer seasons). The abundance of adults of all species was significantly lower in ALAN treatments. Early stages of isopods showed the same pattern, but the opposite was observed for the early stages of the other two species. Clear differences were detected in the zonation of these species during natural darkness versus those exposed to ALAN, with some adult-juvenile differences in this response. These results support our hypothesis and document a series of changes affecting differentially both early and late life stages of these species, and ultimately, the structure of the entire community. Although the effects described correspond to short-term responses, more persistent effects are likely to occur if ALAN sources become established as permanent features in sandy beaches. The worldwide growth of ALAN suggests that the scope of its effect will continue to grow and represents a concern for sandy beach systems.

Environmental filtering drives the assembly of mammal communities in a heterogeneous Mediterranean region

Agricultural expansion and intensification are major drivers of global change. Quantifying the importance of different processes governing the assembly of local communities in agroecosystems is essential to guide the conservation effort allocated to enhancing habitat connectivity, improving habitat quality or managing species interactions. We used multiple detection methods to record the occurrence of medium-sized and large-sized mammals in three managed landscapes of a heterogeneous Mediterranean region. Then we used a joint species distribution model to evaluate the relative influence of dispersal limitation, environmental filtering, and interspecific interactions on the local assembly of mammal communities in 4-km² plots. The partitioning of the explained variation in species occurrence was attributed on average 99% to environmental filters and 1% to dispersal filters. No role was attributed to biotic filters, in agreement with the scarce support for strong competition or other negative interactions found after a literature review. Four principal environmental factors explained on average 63% of variance in species occurrence and operated mainly at the landscape scale. The amount of shrub cover in the neighboring landscape was the most influential factor favoring mammal occurrence and accounted for nearly one-third of the total variance. The proportion of intensively managed croplands and proxies of human activity within landscape samples limited mammal presence. At the microhabitat scale (~80 m² plots) the mean percentage area deprived of woody vegetation also had a negative effect. Functional traits such as body mass or social behavior accounted for a substantial fraction of the variation attributed to environmental factors. We concluded that multiscale environmental filtering governed local community assembly, whereas the role of dispersal limitation and interspecific interactions was negligible. Our results suggest that further removal of shrubland, the expansion of intensive agriculture, and the increase of human activity are expected to result in species losses. The fact that community integrity responds to a single type of ecological process simplifies practical recommendations. Management strategies should focus on the conservation and restoration of shrubland, adopting alternatives to intensive schemes of agricultural production, and minimizing recreational and other human activities in remnant natural habitats within agroecosystems or mosaic landscapes.

An Agenda for Research of Uncovered Epidemiological Patterns of Tick-Borne Pathogens Affecting Human Health

The panorama of ticks and tick-borne pathogens (TBP) is complex due to the many interactions among vertebrates, vectors, and habitats, occurring at different scales. At a broad spatial range, climate and host availability regulate most tick processes, including questing activity, development, and survival. At a local scale, interactions are obscured by a high indeterminacy, making it arduous to record in field surveys. A solid modelling framework could translate the local/regional empirical findings into larger scales, shedding light on the processes governing the circulation of TBP. In this opinion paper, we advocate for a re-formulation of some paradigms in the research of these outstanding cycles of transmission. We propose revisiting concepts that faced criticisms or lacked solid support, together with the development of a conceptual scheme exploring the circulation of TBP under a range of conditions. We encourage (i) an adequate interpretation of the niche concept of both ticks and vertebrate/reservoir hosts interpreting the (a)biotic components that shape the tick's niche, (ii) an assessment of the role played by the communities of wild vertebrates on the circulation of pathogens, and (iii) the development of new approaches, based on state-of-the-art epidemiological concepts, to integrate findings and modelling efforts on TBP over large regions.

Joint survival modelling for multiple species exposed to toxicants

In environmental risk assessment (ERA), the multitude of compounds and taxa demands cross-species extrapolation to cover the variability in sensitivity to toxicants. However, only the impact of a single compound to a single species is addressed by the general unified threshold model of survival (GUTS). The reduced GUTS is the recommended model to analyse lethal toxic effects in regulatory aquatic ERA. GUTS considers toxicokinetics and toxicodynamics. Two toxicodynamic approaches are considered: Stochastic death (SD) assumes that survival decreases with an increasing internalized amount of the toxicant. Individual tolerance (IT) assumes that individuals vary in their tolerance to toxic exposure. Existing theory suggests that the product of the threshold z_w and killing rate b_w (both SD toxicodynamic parameters) are constant across species or compounds if receptors and target sites are shared. We extend that theory and show that the shape parameter β of the loglogistic threshold distribution in IT is also constant. To verify the predicted relationships, we conducted three tests using toxicity studies for eight arthropods exposed to the insecticide flupyradifurone. We confirmed previous verifications of the relation- between SD parameters, and the newly established relation for the IT parameter β. We enhanced GUTS to jointly model survival for multiple species with shared receptors and pathways by incorporating the relations among toxicodynamic parameters described above. The joint GUTS exploits the shared parameter relations and therefore constrains parameter uncertainty for each of the separate species. Particularly for IT, the joint GUTS more precisely predicted risk to the separate species than the standard single species GUTS under environmentally realistic exposure. We suggest that joint GUTS modelling can improve cross-species extrapolation in regulatory ERA by increasing the reliability of risk estimates and reducing animal testing. Furthermore, the shared toxicodynamic response provides potential to reduce complexity of ecosystem models.

Effects of density, species interactions, and environmental stochasticity on the dynamics of British bird communities

Our knowledge of the factors affecting species abundances is mainly based on time‐series analyses of a few well‐studied species at single or few localities, but we know little about whether results from such analyses can be extrapolated to the community level. We apply a joint species distribution model to long‐term time‐series data on British bird communities to examine the relative contribution of intra‐ and interspecific density dependence at different spatial scales, as well as the influence of environmental stochasticity, to spatiotemporal interspecific variation in abundance. Intraspecific density dependence has the major structuring effect on these bird communities. In addition, environmental fluctuations affect spatiotemporal differences in abundance. In contrast, species interactions had a minor impact on variation in abundance. Thus, important drivers of single‐species dynamics are also strongly affecting dynamics of communities in time and space.

A taxonomic-based joint species distribution model for presence-only data

Species distribution models (SDMs) are an important class of model for mapping taxa spatially and are a key tool for tackling biodiversity loss. However, most common SDMs depend on presence–absence data and, despite the accumulation and exponential growth of biological occurrence data across the globe, the available data are predominantly presence-only (i.e. they lack real absences). Although presence-only SDMs do exist, they inevitably require assumptions about absences of the considered taxa and they are specified mostly for single species and, thus, do not exploit fully the information in related taxa. This greatly limits the utility of global biodiversity databases such as GBIF. Here, we present a Bayesian-based SDM for multiple species that operates directly on presence-only data by exploiting the joint distribution between the multiple ecological processes and, crucially, identifies the sampling effort per taxa which allows inference on absences. The model was applied to two case studies. One, focusing on taxonomically diverse taxa over central Mexico and another focusing on the monophyletic family Cactacea over continental Mexico. In both cases, the model was able to identify the ecological and sampling effort processes for each taxon using only the presence observations, environmental and anthropological data.

The Rapid Rise of Next-Generation Natural History

Many ecologists have lamented the demise of natural history and have attributed this decline to a misguided view that natural history is outdated and unscientific. Although there is a perception that the focus in ecology and conservation have shifted away from descriptive natural history research and training toward hypothetico-deductive research, we argue that natural history has entered a new phase that we call “next-generation natural history.” This renaissance of natural history is characterized by technological and statistical advances that aid in collecting detailed observations systematically over broad spatial and temporal extents. The technological advances that have increased exponentially in the last decade include electronic sensors such as camera-traps and acoustic recorders, aircraft- and satellite-based remote sensing, animal-borne biologgers, genetics and genomics methods, and community science programs. Advances in statistics and computation have aided in analyzing a growing quantity of observations to reveal patterns in nature. These robust next-generation natural history datasets have transformed the anecdotal perception of natural history observations into systematically collected observations that collectively constitute the foundation for hypothetico-deductive research and can be leveraged and applied to conservation and management. These advances are encouraging scientists to conduct and embrace detailed descriptions of nature that remain a critically important component of the scientific endeavor. Finally, these next-generation natural history observations are engaging scientists and non-scientists alike with new documentations of the wonders of nature. Thus, we celebrate next-generation natural history for encouraging people to experience nature directly.

Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit flies

The relative importance of ecological factors and species interactions for shaping species distributions is still debated. The realised niches of eight sympatric tephritid fruit flies were inferred from field abundance data using joint species distribution modelling and network inference, on the whole community and separately on three host plant groups. These estimates were then confronted the fundamental niches of seven fly species estimated through laboratory-measured fitnesses on host plants. Species abundances depended on host plants, followed by climatic factors, with a dose of competition between species sharing host plants. The relative importance of these factors mildly changed among the three host plant groups. Despite overlapping fundamental niches, specialists and generalists had almost distinct realised niches, with possible competitive exclusion of generalists by specialists on Cucurbitaceae. They had different assembly rules: Specialists were mainly influenced by their adaptation to host plants, while generalist abundances varied regardless of their fundamental host use.

Experimental evidence that effects of megaherbivores on mesoherbivore space use are influenced by species' traits

The extinction of 80% of megaherbivore (>1,000 kg) species towards the end of the Pleistocene altered vegetation structure, fire dynamics and nutrient cycling world-wide. Ecologists have proposed (re)introducing megaherbivores or their ecological analogues to restore lost ecosystem functions and reinforce extant but declining megaherbivore populations. However, the effects of megaherbivores on smaller herbivores are poorly understood. We used long-term exclusion experiments and multispecies hierarchical models fitted to dung counts to test (a) the effect of megaherbivores (elephant and giraffe) on the occurrence (dung presence) and use intensity (dung pile density) of mesoherbivores (2-1,000 kg), and (b) the extent to which the responses of each mesoherbivore species was predictable based on their traits (diet and shoulder height) and phylogenetic relatedness. Megaherbivores increased the predicted occurrence and use intensity of zebras but reduced the occurrence and use intensity of several other mesoherbivore species. The negative effect of megaherbivores on mesoherbivore occurrence was stronger for shorter species, regardless of diet or relatedness. Megaherbivores substantially reduced the expected total use intensity (i.e. cumulative dung density of all species) of mesoherbivores, but only minimally reduced the expected species richness (i.e. cumulative predicted occurrence probabilities of all species) of mesoherbivores (by <1 species). Simulated extirpation of megaherbivores altered use intensity by mesoherbivores, which should be considered during (re)introductions of megaherbivores or their ecological proxies. Species' traits (in this case shoulder height) may be more reliable predictors of mesoherbivores' responses to megaherbivores than phylogenetic relatedness, and may be useful for predicting responses of data-limited species.

Assuming independence in spatial latent variable models: Consequences and implications of misspecification

Multivariate spatial data, where multiple responses are simultaneously recorded across spatially indexed observational units, are routinely collected in a wide variety of disciplines. For example, the Southern Ocean Continuous Plankton Recorder survey collects records of zooplankton communities in the Indian sector of the Southern Ocean, with the aim of identifying and quantifying spatial patterns in biodiversity in response to environmental change. One increasingly popular method for modeling such data is spatial generalized linear latent variable models (GLLVMs), where the correlation across sites is captured by a spatial covariance function in the latent variables. However, little is known about the impact of misspecifying the latent variable correlation structure on inference of various parameters in such models. To address this gap in the literature, we investigate how misspecifying and assuming independence for the latent variables' correlation structure impacts estimation and inference in spatial GLLVMs. Through both theory and numerical studies, we show that performance of maximum likelihood estimation and inference on regression coefficients under misspecification depends on a combination of the response type, the magnitude of true regression coefficient, and the corresponding loadings, and, most importantly, whether the corresponding covariate is (also) spatially correlated. On the other hand, estimation and inference of truly nonzero loadings and prediction of latent variables is consistently not robust to misspecification of the latent variable correlation structure.

Integrating statistical and mechanistic approaches with biotic and environmental variables improves model predictions of the impact of climate and land-use changes on future mosquito-vector abundance, diversity and distributions in Australia

Changes to Australia's climate and land-use patterns could result in expanded spatial and temporal distributions of endemic mosquito vectors including Aedes and Culex species that transmit medically important arboviruses. Climate and land-use changes greatly influence the suitability of habitats for mosquitoes and their behaviors such as mating, feeding and oviposition. Changes in these behaviors in turn determine future species-specific mosquito diversity, distribution and abundance. In this review, we discuss climate and land-use change factors that influence shifts in mosquito distribution ranges. We also discuss the predictive and epidemiological merits of incorporating these factors into a novel integrated statistical (SSDM) and mechanistic species distribution modelling (MSDM) framework. One potentially significant merit of integrated modelling is an improvement in the future surveillance and control of medically relevant endemic mosquito vectors such as Aedes vigilax and Culex annulirostris, implicated in the transmission of many arboviruses such as Ross River virus and Barmah Forest virus, and exotic mosquito vectors such as Aedes aegypti and Aedes albopictus. We conducted a focused literature search to explore the merits of integrating SSDMs and MSDMs with biotic and environmental variables to better predict the future range of endemic mosquito vectors. We show that an integrated framework utilising both SSDMs and MSDMs can improve future mosquito-vector species distribution projections in Australia. We recommend consideration of climate and environmental change projections in the process of developing land-use plans as this directly impacts mosquito-vector distribution and larvae abundance. We also urge laboratory, field-based researchers and modellers to combine these modelling approaches. Having many different variations of integrated (SDM) modelling frameworks could help to enhance the management of endemic mosquitoes in Australia. Enhanced mosquito management measures could in turn lead to lower arbovirus spread and disease notification rates.

Improving prediction of rare species' distribution from community data

Species distribution models (SDMs) have been increasingly used to predict the geographic distribution of a wide range of organisms; however, relatively fewer research efforts have concentrated on rare species despite their critical roles in biological conservation. The present study tested whether community data may improve modelling rare species by sharing information among common and rare ones. We chose six SDMs that treat community data in different ways, including two traditional single-species models (random forest and artificial neural network) and four joint species distribution models that incorporate species associations implicitly (multivariate random forest and multi-response artificial neural network) or explicitly (hierarchical modelling of species communities and generalized joint attribute model). In addition, we evaluated two approaches of data arrangement, species filtering and conditional prediction, to enhance the selected models. The model predictions were tested using cross validation based on empirical data collected from marine fisheries surveys, and the effects of community data were evaluated by comparing models for six selected rare species. The results demonstrated that the community data improved the predictions of rare species' distributions to certain extent but might also be unhelpful in some cases. The rare species could be appropriately predicted in terms of occurrence, whereas their abundance tended to be underestimated by most models. Species filtering and conditional predictions substantially benefited the predictive performances of multiple- and single-species models, respectively. We conclude that both the modelling algorithms and community data need to be carefully selected in order to deliver improvement in modelling rare species. The study highlights the opportunity and challenges to improve prediction of rare species' distribution by making the most of community data.

Co-occurrence patterns and the large-scale spatial structure of benthic communities in seagrass meadows and bare sand

Background

Species distribution models are commonly used tools to describe diversity patterns and support conservation measures. There is a wide range of approaches to developing SDMs, each highlighting different characteristics of both the data and the ecology of the species or assemblages represented by the data. Yet, signals of species co-occurrences in community data are usually ignored, due to the assumption that such structuring roles of species co-occurrences are limited to small spatial scales and require experimental studies to be detected. Here, our aim is to explore associations among marine sandy-bottom sediment inhabitants and test for the structuring effect of seagrass on co-occurrences among these species across a New Zealand intertidal sandflat, using a joint species distribution model (JSDM).

Results

We ran a JSDM on a total of 27 macrobenthic species co-occurring in 300,000 m² of sandflat. These species represented all major taxonomic groups, i.e. polychaetes, bivalves and crustaceans, collected in 400 sampling locations. A number of significant co-occurrences due to shared habitat preferences were present in vegetated areas, where negative and positive correlations were approximately equally common. A few species, among them the gastropods Cominella glandiformis and Notoacmea scapha, co-occurred randomly with other seagrass benthic inhabitants. Residual correlations were less apparent and mostly positive. In bare sand flats shared habitat preferences resulted in many significant co-occurrences of benthic species. Moreover, many negative and positive residual patterns between benthic species remained after accounting for habitat preferences. Some species occurring in both habitats showed similarities in their correlations, such as the polychaete Aglaophamus macroura, which shared habitat preferences with many other benthic species in both habitats, yet no residual correlations remained in either habitat.

Conclusions

Firstly, analyses based on a latent variable approach to joint distributions stressed the structuring role of species co-occurrences beyond experimental scales. Secondly, results showed context dependent interactions, highlighted by species having more interconnected networks in New Zealand bare sediment sandflats than in seagrass meadows. These findings stress the critical importance of natural history to modelling, as well as incorporating ecological reality in SDMs.

Refining predictions of metacommunity dynamics by modeling species non-independence

Predicting the dynamics of biotic communities is difficult because species' environmental responses are not independent, but covary due to shared or contrasting ecological strategies and the influence of species interactions. We used latent-variable joint species distribution models to analyze paired historical and contemporary inventories of 585 vascular plant species on 471 islands in the southwest Finnish archipelago. Larger, more heterogeneous islands were characterized by higher colonization rates and lower extinction rates. Ecological and taxonomical species groups explained small but detectable proportions of variance in species' environmental responses. To assess the potential influence of species interactions on community dynamics, we estimated species associations as species-to-species residual correlations for historical occurrences, for colorizations, and for extinctions. Historical species associations could to some extent predict joint colonization patterns, but the overall estimated influence of species associations on community dynamics was weak. These results illustrate the benefits of considering metacommunity dynamics within a joint framework, but also suggest that any influence of species interactions on community dynamics may be hard to detect from observational data.

Joint species distribution modelling with the r-package Hmsc

Joint Species Distribution Modelling (JSDM) is becoming an increasingly popular statistical method for analysing data in community ecology. Hierarchical Modelling of Species Communities (HMSC) is a general and flexible framework for fitting JSDMs. HMSC allows the integration of community ecology data with data on environmental covariates, species traits, phylogenetic relationships and the spatio-temporal context of the study, providing predictive insights into community assembly processes from non-manipulative observational data of species communities.The full range of functionality of HMSC has remained restricted to Matlab users only. To make HMSC accessible to the wider community of ecologists, we introduce Hmsc 3.0, a user-friendly r implementation.We illustrate the use of the package by applying Hmsc 3.0 to a range of case studies on real and simulated data. The real data consist of bird counts in a spatio-temporally structured dataset, environmental covariates, species traits and phylogenetic relationships. Vignettes on simulated data involve single-species models, models of small communities, models of large species communities and models for large spatial data. We demonstrate the estimation of species responses to environmental covariates and how these depend on species traits, as well as the estimation of residual species associations. We demonstrate how to construct and fit models with different types of random effects, how to examine MCMC convergence, how to examine the explanatory and predictive powers of the models, how to assess parameter estimates and how to make predictions. We further demonstrate how Hmsc 3.0 can be applied to normally distributed data, count data and presence-absence data.The package, along with the extended vignettes, makes JSDM fitting and post-processing easily accessible to ecologists familiar with r.

Computationally efficient joint species distribution modeling of big spatial data

The ongoing global change and the increased interest in macroecological processes call for the analysis of spatially extensive data on species communities to understand and forecast distributional changes of biodiversity. Recently developed joint species distribution models can deal with numerous species efficiently, while explicitly accounting for spatial structure in the data. However, their applicability is generally limited to relatively small spatial data sets because of their severe computational scaling as the number of spatial locations increases. In this work, we propose a practical alleviation of this scalability constraint for joint species modeling by exploiting two spatial-statistics techniques that facilitate the analysis of large spatial data sets: Gaussian predictive process and nearest-neighbor Gaussian process. We devised an efficient Gibbs posterior sampling algorithm for Bayesian model fitting that allows us to analyze community data sets consisting of hundreds of species sampled from up to hundreds of thousands of spatial units. The performance of these methods is demonstrated using an extensive plant data set of 30,955 spatial units as a case study. We provide an implementation of the presented methods as an extension to the hierarchical modeling of species communities framework.

Neural hierarchical models of ecological populations

Neural networks are increasingly being used in science to infer hidden dynamics of natural systems from noisy observations, a task typically handled by hierarchical models in ecology. This article describes a class of hierarchical models parameterised by neural networks - neural hierarchical models. The derivation of such models analogises the relationship between regression and neural networks. A case study is developed for a neural dynamic occupancy model of North American bird populations, trained on millions of detection/non-detection time series for hundreds of species, providing insights into colonisation and extinction at a continental scale. Flexible models are increasingly needed that scale to large data and represent ecological processes. Neural hierarchical models satisfy this need, providing a bridge between deep learning and ecological modelling that combines the function representation power of neural networks with the inferential capacity of hierarchical models.

Analyzing community structure subject to incomplete sampling: hierarchical community model vs. canonical ordinations

Recently developing hierarchical community models (HCMs) accounting for incomplete sampling are promising approaches to understand community organization. However, pros and cons of incorporating incomplete sampling in the analysis and related design issues remain unknown. In this study, we compared HCM and canonical redundancy analysis (RDA) carried out with 10 different dissimilarity coefficients to evaluate how each approach restores true community abundance data sampled with imperfect detection. We conducted simulation experiments with varying numbers of sampling sites, visits, mean detectability and mean abundance. Performance of HCM was measured by estimates of "expected" (mean) abundance ( λ ^ ij ) and realized abundance ( N ^ ij : direct estimate of site- and species-specific abundance). We also compared HCM and different types of RDA (normal, partial, and weighted), all performed with the same ten different dissimilarity coefficients, with unequal number of visits to sampling sites. In addition, we applied the models to a virtual survey carried out on the Barro Colorado Island tree plot data for which we know true community abundance. Simulation experiments showed that N ^ ij yielded by HCM best restored the underlying abundance of constituent species among 12 abundance estimates by HCM and RDA regardless if the sampling was equal or unequal. Mean abundance predominantly affected the performance of HCM and RDA while λ ^ ij yielded by HCM had comparable performance to percentage difference and Gower dissimilarity coefficients of RDA. Relative performance of RDA types depended on the combination of dissimilarity coefficients and the distribution of sampling effort. Best performance of N ^ ij followed by λ ^ ij , percentage difference and Gower dissimilarity were also observed for the analysis of tree plot data, and graphical plots (triplots) based on λ ^ ij rather than N ^ ij clearly separated the effects of two environmental covariates on the abundance of constituent species. Under our conditions of model evaluation and the method, we concluded that, in terms of assessing the environmental dependence of abundance, HCMs and RDA can have comparable performance if we can choose appropriate dissimilarity coefficients for RDA. However, since HCMs provide straightforward biological interpretations of parameter estimates and flexibility of the analysis, HCMs would be useful in many situations as well as conventional canonical ordinations.

A pathway for multivariate analysis of ecological communities using copulas

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity-based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over-dispersion, and/or zero-inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation-based filter with exact family-wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity-based settings. Our proposed analysis pathway provides new opportunities to combine model-based approaches with dissimilarity-based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.

Pathogeography: leveraging the biogeography of human infectious diseases for global health management

Biogeography is an implicit and fundamental component of almost every dimension of modern biology, from natural selection and speciation to invasive species and biodiversity management. However, biogeography has rarely been integrated into human or veterinary medicine nor routinely leveraged for global health management. Here we review the theory and application of biogeography to the research and management of human infectious diseases, an integration we refer to as 'pathogeography'. Pathogeography represents a promising framework for understanding and decomposing the spatial distributions, diversity patterns and emergence risks of human infectious diseases into interpretable components of dynamic socio-ecological systems. Analytical tools from biogeography are already helping to improve our understanding of individual infectious disease distributions and the processes that shape them in space and time. At higher levels of organization, biogeographical studies of diseases are rarer but increasing, improving our ability to describe and explain patterns that emerge at the level of disease communities (e.g. co-occurrence, diversity patterns, biogeographic regionalisation). Even in a highly globalized world most human infectious diseases remain constrained in their geographic distributions by ecological barriers to the dispersal or establishment of their causal pathogens, reservoir hosts and/or vectors. These same processes underpin the spatial arrangement of other taxa, such as mammalian biodiversity, providing a strong empirical 'prior' with which to assess the potential distributions of infectious diseases when data on their occurrence is unavailable or limited. In the absence of quality data, generalized biogeographic patterns could provide the earliest (and in some cases the only) insights into the potential distributions of many poorly known or emerging, or as-yet-unknown, infectious disease risks. Encouraging more community ecologists and biogeographers to collaborate with health professionals (and vice versa) has the potential to improve our understanding of infectious disease systems and identify novel management strategies to improve local, global and planetary health.