Using joint species distribution models for evaluating how species‐to‐species associations depend on the environmental context

Environmental response strategies for the spatial distribution of seed plants in Gansu

The interplay between plant diversity and environmental response strategies is crucial for ecosystem adaptability and stability. A central focus in modern ecology is elucidating how environmental factors shape plant diversity patterns and regulate species distributions across heterogeneous landscapes. This study employed Joint Species Distribution Model (JSDM) to quantitatively analyze the influence of environmental variables on plant spatial distributions in Gansu Province, China, while examining interspecies interactions under varying conditions. Results demonstrated that environmental factors explained 95.4% of the variance, highlighting their predominant role in determining plant distributions. Habitat type accounted for the largest share of variance (33.5%), followed by elevation (22.1%), mean annual temperature (20.3%), mean annual precipitation (15.1%), and solar radiation (4.4%). Species' responses to environmental covariates were predominantly independent, with weak phylogenetic correlation (posterior mean: 0.17), reflecting limited ecological niche conservatism at the family level. Geographically, regions such as the northern Qilian foothills, Lanzhou-Baiyin wilderness, Loess Plateau, and Gannan Plateau exhibited negative correlations with most plant families, functioning as critical limiting or driving factors in spatial variability. Additionally, 33.7% of seed plant families showed negative correlations with light intensity, underscoring its role as a major limiting factor. Provincially, competition does not primarily constrain seed plant coexistence in Gansu. Regionally, however, pronounced differences in environmental responses were observed. In the northwest, solar radiation (37%) and precipitation (25%) were dominant drivers of plant distribution, while in the southeast, solar radiation (36.3%) and elevation (34.7%) were predominant. These findings underscore that species co-occurrence patterns are scale-dependent and influenced by regional resource availability. In resource-abundant southeastern areas, plant families displayed positive co-occurrence patterns indicative of mutualistic or symbiotic interactions, whereas resource-scarce northwestern areas experienced intensified negative co-occurrences due to heightened interspecific competition. This study highlights the critical role of environmental gradients in structuring seed plant distributions in Gansu, providing insights into the interaction of ecological adaptation and evolutionary history in shaping plant diversity. By identifying the drivers of plant distribution across heterogeneous environments, this research offers significant implications for biodiversity conservation and plant resource management strategies in Gansu Province, while contributing to a broader understanding of plant-environment dynamics in complex ecosystems.

Impacts of Climate Change Conditions on the Potential Distribution of Anoplophora glabripennis and Its Host Plants, Salix babylonica and Salix matsudana, in China

The Anoplophora glabripennis (Motschulsky) is a phytophagous pest that is seriously endangering Salix babylonica Linn. and S. matsudana Koidz. Poor control can damage local ecosystems, resulting in economic losses and management risks. In the context of climate change, the climatic ecological niche of organisms is no longer compatible with the surrounding environment. To mitigate the effects of climate change, some organisms respond adaptively to climate change through different mechanisms and in different ways. In this study, an optimized MaxEnt model was used to explore the potential distribution areas of A. glabripennis and its host plants, S. babylonica and S. matsudana, in response to current and future climate and to determine their movement routes and relative dynamics. The results show that the optimized model exhibits the lowest complexity and excellent prediction accuracy. It is important to note that both temperature and precipitation are the main environmental factors affecting the distribution of suitable habitats for A. glabripennis and its host plants. This is evidenced by the mean temperature of the warmest quarter and precipitation of the wettest month being the main environmental factors affecting the distribution of suitable habitats for the host plants. Similarly, the minimum temperature of the coldest month and precipitation seasonality are the primary bioclimatic variables constraining the dispersal of A. glabripennis. Under climate change, the suitable areas of both S. babylonica and S. matsudana are declining, while the suitable areas of A. glabripennis are expanding in future climates. Furthermore, three species exhibited a proclivity for migration to higher latitudes in response to climate change. In conclusion, this study contributes to our understanding of the biogeographic characteristics of these A. glabripennis, S. babylonica, and S. matsudana and provides a basis for the formulation of timely conservation strategies to reduce the potential impacts of climate change. This is of great significance for the rational management, utilization, and protection of forest ecosystems in China.

Human disturbance increases spatiotemporal associations among mountain forest terrestrial mammal species

Spatial and temporal associations between sympatric species underpin biotic interactions, structure ecological assemblages, and sustain ecosystem functioning and stability. However, the resilience of interspecific spatiotemporal associations to human activity remains poorly understood, particularly in mountain forests where anthropogenic impacts are often pervasive. Here, we applied context-dependent Joint Species Distribution Models to a systematic camera-trap survey dataset from a global biodiversity hotspot in eastern Himalayas to understand how prominent human activities in mountain forests influence species associations within terrestrial mammal communities. We obtained 10,388 independent detections of 17 focal species (12 carnivores and five ungulates) from 322 stations over 43,163 camera days of effort. We identified a higher incidence of positive associations in habitats with higher levels of human modification (87%) and human presence (83%) compared to those located in habitats with lower human modification (64%) and human presence (65%) levels. We also detected a significant reduction of pairwise encounter time at increasing levels of human disturbance, corresponding to more frequent encounters between pairs of species. Our findings indicate that human activities can push mammals together into more frequent encounters and associations, which likely influences the coexistence and persistence of wildlife, with potential far-ranging ecological consequences.

Implications of the scale of detection for inferring co-occurrence patterns from paired camera traps and acoustic recorders

Multifunctional landscapes that support economic activities and conservation of biological diversity (e.g., cattle ranches with native forest) are becoming increasingly important because small remnants of native forest may comprise the only habitat left for some wildlife species. Understanding the co-occurrence between wildlife and disturbance factors, such as poaching activity and domesticated ungulates, is key to successful management of multifunctional landscapes. Tools to measure co-occurrence between wildlife and disturbance factors include camera traps and autonomous acoustic recording units. We paired 52 camera-trap stations with acoustic recorders to investigate the association between 2 measures of disturbance (poaching and cattle) and wild ungulates present in multifunctional landscapes of the Colombian Orinoquía. We used joint species distribution models to investigate species-habitat associations and species-disturbance correlations. One model was fitted using camera-trap data to detect wild ungulates and disturbance factors, and a second model was fitted after replacing camera-trap detections of disturbance factors with their corresponding acoustic detections. The direction, significance, and precision of the effect of covariates depended on the sampling method used for disturbance factors. Acoustic monitoring typically resulted in more precise estimates of the effects of covariates and of species-disturbance correlations. Association patterns between wildlife and disturbance factors were found only when disturbance was detected by acoustic recorders. Camera traps allowed us to detect nonvocalizing species, whereas audio recording devices increased detection of disturbance factors leading to more precise estimates of co-occurrence patterns. The collared peccary (Pecari tajacu), lowland tapir (Tapirus terrestris), and white-tailed deer (Odocoileus virginianus) co-occurred with disturbance factors and are conservation priorities due to the greater risk of poaching or disease transmission from cattle.

Predicting the responses of European grassland communities to climate and land cover change

European grasslands are among the most species-rich ecosystems on small spatial scales. However, human-induced activities like land use and climate change pose significant threats to this diversity. To explore how climate and land cover change will affect biodiversity and community composition in grassland ecosystems, we conducted joint species distribution models (SDMs) on the extensive vegetation-plot database sPlotOpen to project distributions of 1178 grassland species across Europe under current conditions and three future scenarios. We further compared model accuracy and computational efficiency between joint SDMs (JSDMs) and stacked SDMs, especially for rare species. Our results show that: (i) grassland communities in the mountain ranges are expected to suffer high rates of species loss, while those in western, northern and eastern Europe will experience substantial turnover; (ii) scaling anomalies were observed in the predicted species richness, reflecting regional differences in the dominant drivers of assembly processes; (iii) JSDMs did not outperform stacked SDMs in predictive power but demonstrated superior efficiency in model fitting and predicting; and (iv) incorporating co-occurrence datasets improved the model performance in predicting the distribution of rare species. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

Double jeopardy: global change and interspecies competition threaten Siberian cranes

Anthropogenic global change is precipitating a worldwide biodiversity crisis, with myriad species teetering on the brink of extinction. The Arctic, a fragile ecosystem already on the frontline of global change, bears witness to rapid ecological transformations catalyzed by escalating temperatures. In this context, we explore the ramifications of global change and interspecies competition on two arctic crane species: the critically endangered Siberian crane (Leucogeranus leucogeranus) and the non-threatened sandhill crane (Grus canadensis). How might global climate and landcover changes affect the range dynamics of Siberian cranes and sandhill cranes in the Arctic, potentially leading to increased competition and posing a greater threat to the critically endangered Siberian cranes? To answer these questions, we integrated ensemble species distribution models (SDMs) to predict breeding distributions, considering both abiotic and biotic factors. Our results reveal a profound divergence in how global change impacts these crane species. Siberian cranes are poised to lose a significant portion of their habitats, while sandhill cranes are projected to experience substantial range expansion. Furthermore, we identify a growing overlap in breeding areas, intensifying interspecies competition, which may imperil the Siberian crane. Notably, we found the Anzhu Islands may become a Siberian crane refuge under global change, but competition with Sandhill Cranes underscores the need for enhanced conservation management. Our study underscores the urgency of considering species responses to global changes and interspecies dynamics in risk assessments and conservation management. As anthropogenic pressures continue to mount, such considerations are crucial for the preservation of endangered species in the face of impending global challenges.

The importance of oxygen for explaining rapid shifts in a marine fish

Large-scale shifts in marine species biogeography have been a notable impact of climate change. An effective explanation of what drives these species shifts, as well as accurate predictions of where they might move, is crucial to effectively managing these natural resources and conserving biodiversity. While temperature has been implicated as a major driver of these shifts, physiological processes suggest that oxygen, prey, and other factors should also play important roles. We expanded upon previous temperature-based distribution models by testing whether oxygen, food web productivity, salinity, and scope for metabolic activity (the Metabolic Index) better explained the changing biogeography of Black Sea Bass (Centropristis striata) in the Northeast US. This species has been expanding further north over the past 15 years. We found that oxygen improved model performance beyond a simple consideration of temperature (ΔAIC = 799, ΔTSS = 0.015), with additional contributions from prey and salinity. However, the Metabolic Index did not substantially increase model performance relative to temperature and oxygen (ΔAIC = 0.63, ΔTSS = 0.0002). Marine species are sensitive to oxygen, and we encourage researchers to use ocean biogeochemical hindcast and forecast products to better understand marine biogeographic changes.

Interspecific behavioural interference and range dynamics: current insights and future directions

Novel biotic interactions in shifting communities play a key role in determining the ability of species' ranges to track suitable habitat. To date, the impact of biotic interactions on range dynamics have predominantly been studied in the context of interactions between different trophic levels or, to a lesser extent, exploitative competition between species of the same trophic level. Yet, both theory and a growing number of empirical studies show that interspecific behavioural interference, such as interspecific territorial and mating interactions, can slow down range expansions, preclude coexistence, or drive local extinction, even in the absence of resource competition. We conducted a systematic review of the current empirical research into the consequences of interspecific behavioural interference on range dynamics. Our findings demonstrate there is abundant evidence that behavioural interference by one species can impact the spatial distribution of another. Furthermore, we identify several gaps where more empirical work is needed to test predictions from theory robustly. Finally, we outline several avenues for future research, providing suggestions for how interspecific behavioural interference could be incorporated into existing scientific frameworks for understanding how biotic interactions influence range expansions, such as species distribution models, to build a stronger understanding of the potential consequences of behavioural interference on the outcome of future range dynamics.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

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.

An environmental habitat gradient and within-habitat segregation enable co-existence of ecologically similar bird species

Niche theory predicts that ecologically similar species can coexist through multidimensional niche partitioning. However, owing to the challenges of accounting for both abiotic and biotic processes in ecological niche modelling, the underlying mechanisms that facilitate coexistence of competing species are poorly understood. In this study, we evaluated potential mechanisms underlying the coexistence of ecologically similar bird species in a biodiversity-rich transboundary montane forest in east-central Africa by computing niche overlap indices along an environmental elevation gradient, diet, forest strata, activity patterns and within-habitat segregation across horizontal space. We found strong support for abiotic environmental habitat niche partitioning, with 55% of species pairs having separate elevation niches. For the remaining species pairs that exhibited similar elevation niches, we found that within-habitat segregation across horizontal space and to a lesser extent vertical forest strata provided the most likely mechanisms of species coexistence. Coexistence of ecologically similar species within a highly diverse montane forest was determined primarily by abiotic factors (e.g. environmental elevation gradient) that characterize the Grinnellian niche and secondarily by biotic factors (e.g. vertical and horizontal segregation within habitats) that describe the Eltonian niche. Thus, partitioning across multiple levels of spatial organization is a key mechanism of coexistence in diverse communities.

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.

Convergence in floodplain pond communities indicates different pathways to community assembly

Disturbance can strongly influence ecosystems, yet much remains unknown about the relative importance of key processes (selection, drift, and dispersal) in the recovery of ecological communities following disturbance. We combined field surveys with a field experiment to elucidate mechanisms governing the recovery of aquatic macroinvertebrates in habitats of an alluvial floodplain following flood disturbance. We monitored macroinvertebrates in 24 natural parafluvial habitats over 60 days after a major flood, as well as the colonization of 24 newly-built ponds by macroinvertebrates over 45 days in the same floodplain. We examined the sources of environmental variation and their relative effects on aquatic assemblages using a combination of null models and Mantel tests. We also used a joint species distribution model to investigate the importance of primary metacommunity structuring processes during recovery: selection, dispersal, and drift. Contrary to expectations, we found that beta diversity actually decreased among natural habitats over time after the flood or the creation of the ponds, instead of increasing. This result was despite environmental predictors showing contrasting patterns for explaining community variation over time in the natural habitats compared with the experimental ponds. Flood heterogeneity across the floodplain and spatial scale differences between the experimental ponds and the natural habitats seemingly constrained the balance between deterministic and stochastic processes driving the ecological convergence of assemblages over time. While environmental selection was the dominant structuring process in both groups, biotic interactions also had a prominent influence on community assembly. These findings have profound implications towards understanding metacommunity structuring in riverscapes that includes common linkages between disturbance heterogeneity, spatial scale properties, and community composition.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00027-023-00957-9.

Intertidal macroinvertebrate community structure in a subtropical channel is driven by sediment properties across different land-use types

Macroinvertebrate community in the intertidal setup plays an important role in coastal ecosystem functions and biogeochemical cycle. However, different land use pattern may influence on their community structure, diversity, and composition in the coastal ecosystems. Using Van-Veen grab sampler, 60 sediment samples were seasonally collected from mangroves-dominated, aquaculture-dominated, and anthropogenically affected area in the lower intertidal zone of the Kohelia channel of Bangladesh, the Northern Bay of Bengal. We have tasted the variation in sediment properties across three land-use types in this intertidal habitat. To understand the patterns of benthic macroinvertebrate distribution, a neutral community model was applied. Our results showed that community composition and biodiversity of the benthic macroinvertebrate communities varied significantly between mangrove-dominated area with anthropogenically affected areas among the four seasons. The neutral community model revealed that community assembly of benthic macroinvertebrates in the lower intertidal habitats is structured by stochastic processes while sediment properties have significant influence on species distribution and interactions. Results suggested that land-use changes altered sediment properties and could change the diversity and distribution of the macroinvertebrate communities in the lower intertidal habitats.

Community confounding in joint species distribution models

Joint species distribution models have become ubiquitous for studying species-environment relationships and dependence among species. Accounting for community structure often improves predictive power, but can also affect inference on species-environment relationships. Specifically, some parameterizations of joint species distribution models allow interspecies dependence and environmental effects to explain the same sources of variability in species distributions, a phenomenon we call community confounding. We present a method for measuring community confounding and show how to orthogonalize the environmental and random species effects in suite of joint species distribution models. In a simulation study, we show that community confounding can lead to computational difficulties and that orthogonalizing the environmental and random species effects can alleviate these difficulties. We also discuss the inferential implications of community confounding and orthogonalizing the environmental and random species effects in a case study of mammalian responses to the Colorado bark beetle epidemic in the subalpine forest by comparing the outputs from occupancy models that treat species independently or account for interspecies dependence. We illustrate how joint species distribution models that restrict the random species effects to be orthogonal to the fixed effects can have computational benefits and still recover the inference provided by an unrestricted joint species distribution model.

Community assembly of microbial habitat generalists and specialists in urban aquatic ecosystems explained more by habitat type than pollution gradient

Urban freshwater ecosystems have important ecosystem functions, provide habitats for diverse microbial communities and are susceptible to multiple interconnected factors such as environmental pollution. Despite the ecological significance of bacteria and microeukaryotes, little is known about how their community assembly responds to various environmental factors across water and sediment habitats and ecological processes shaping them. Here, environmental DNA-based approaches were used to investigate the community assembly processes of bacteria and microeukaryotes (including habitat generalists and specialists) in urban water and sediment across an urban-pollution gradient in Wuhan, central China. The diversity, community composition and potential function of bacteria and microeukaryotes showed significantly stronger variation between water and sediment than across an urban pollution gradient. Although, bacterial and microeukaryotic community assemblies were dominated by strong selection processes in both water and sediment habitats, but a contrasting community assembly mechanism was identified between habitat generalists and specialists. Bacterial and microeukaryotic communities showed a greater response to physicochemical variability in water, while a strong distance-decay relationship was found in sediment. Further, cross-kingdom microbial network analysis revealed strong modular associations of bacteria and microeukaryotes, meanwhile, microeukaryotic habitat specialists might be keystone, but generalists have higher proportion of connections in the networks. This study provides significant insights into the response of bacteria and microeukaryotes to different urban pollutions between water and sediment, and the ecological processes structuring microbial community dynamics across habitat types under anthropogenic disturbances.

Anthropogenic Induced Beta Diversity in Plant–Pollinator Networks: Dissimilarity, Turnover, and Predictive Power

Biogeography has traditionally focused on the distribution of species, while community ecology has sought to explain the patterns of community composition. Species interactions networks have rarely been subjected to such analyses, as modeling tools have only recently been developed for interaction networks. Here, we examine beta diversity of ecological networks using pollination networks sampled along an urbanization and agricultural intensification gradient in east Leinster, Ireland. We show, for the first time, that anthropogenic gradients structure interaction networks, and exert greater structuring force than geographical proximity. We further showed that species turnover, especially of plants, is the major driver of interaction turnover, and that this contribution increased with anthropogenic induced environmental dissimilarity, but not spatial distance. Finally, to explore the extent to which it is possible to predict each of the components of interaction turnover, we compared the predictive performance of models that included site characteristics and interaction properties to models that contained species level effects. We show that if we are to accurately predict interaction turnover, data are required on the species-specific responses to environmental gradients. This study highlights the importance of anthropogenic disturbances when considering the biogeography of interaction networks, especially in human dominated landscapes where geographical effects can be secondary sources of variation. Yet, to build a predictive science of the biogeography of interaction networks, further species-specific responses need to be incorporated into interaction distribution modeling approaches.

Modelling temperature-driven changes in species associations across freshwater communities

Due to global climate change-induced shifts in species distributions, estimating changes in community composition through the use of Species Distribution Models has become a key management tool. Being able to determine how species associations change along environmental gradients is likely to be pivotal in exploring the magnitude of future changes in species' distributions. This is particularly important in connectivity-limited ecosystems, such as freshwater ecosystems, where increased human translocation is creating species associations over previously unseen environmental gradients. Here, we use a large-scale presence-absence dataset of freshwater fish from lakes across the Fennoscandian region in a Joint Species Distribution Model, to measure the effect of temperature on species associations. We identified a trend of negative associations between species tolerant of cold waters and those tolerant of warmer waters, as well as positive associations between several more warm-tolerant species, with these associations often shifting depending on local temperatures. Our results confirm that freshwater ecosystems can expect to see a large-scale shift towards communities dominated by more warm-tolerant species. While there remains much work to be done to predict exactly where and when local extinctions may take place, the model implemented provides a starting-point for the exploration of climate-driven community trends. This approach is especially informative in regards to determining which species associations are most central in shaping future community composition, and which areas are most vulnerable to local extinctions.

Generalized infinite factorization models

Factorization models express a statistical object of interest in terms of a collection of simpler objects. For example, a matrix or tensor can be expressed as a sum of rank-one components. However, in practice, it can be challenging to infer the relative impact of the different components as well as the number of components. A popular idea is to include infinitely many components having impact decreasing with the component index. This article is motivated by two limitations of existing methods: (i) the lack of careful consideration of the within component sparsity structure; and (ii) no accommodation for grouped variables and other non-exchangeable structures. We propose a general class of infinite factorization models that address these limitations. Theoretical support is provided, practical gains are shown in simulation studies, and an ecology application focusing on modelling bird species occurrence is discussed.

DNA metabarcoding reveals the significant influence of anthropogenic effects on microeukaryotic communities in urban waterbodies

Biological monitoring and assessment are the first and most fundamental steps towards diagnosing ecological or environmental quality. Increasing anthropogenic impact on urban ecosystems has prompted the development of less expensive and more efficient bioassessment approaches. Generally, a morphospecies based approach is effective for plants and large organisms but challenging for the microbial biosphere. To overcome this challenge, we used high-throughput DNA sequencing for predicting anthropogenic effects on microeukaryotic communities in urban waterbodies along a pollution gradient in Wuhan City, central China in summer 2019. Our results indicated that microeukaryotic community structure was distinct between non-urban polluted reservoir and urban polluted waterbodies. The heterogeneity of environmental condition significantly affected the microeukaryotic diversity, community structure, and species interactions. Integrated co-occurring network analysis revealed that the pollution gradient has a significant adverse impact on network complexity and network dissimilarity. These results revealed that the significant variation in anthropogenically-driven environmental condition shaped microeukaryotic communities in urban freshwater ecosystems. Furthermore, we observed that the relative abundance of indicative OTUs were significantly and negatively correlated with pollution level and these indicative OTUs could be used to predict the water quality status with up to 77% success. Thus, our multiple approaches combining 18S rDNA amplicon sequencing, co-occurring network and indicator species analyses suggest that this study gives a novel approach based on microeukaryotic communities to assess and predict the water quality status of urban aquatic environments.

Conditional love? Co-occurrence patterns of drought-sensitive species in European grasslands are consistent with the stress-gradient hypothesis

AIM

The stress-gradient hypothesis (SGH) postulates that species interactions shift from negative to positive with increasing abiotic stress. Interactions between species are increasingly being recognized as important drivers of species distributions, but it is still unclear whether stress-induced changes in interactions affect continental-to-global scale species distributions. Here, we tested whether associations of vascular plant species in dry grasslands in Europe follow the SGH along a climatic water deficit (CWD) gradient across the continent.

LOCATION

Dry grasslands in Europe.

TIME PERIOD

Present.

MAJOR TAXA STUDIED

Vascular plants.

METHODS

We built a context-dependent joint species distribution model (JSDM) to estimate the residual associations (i.e., associations that are not explained by the abiotic environment) of 161 plant species as a function of the CWD based on community data from 8,660 vegetation plots. We evaluated changes in residual associations between species for pairs and on the community level, and we compared responses for groups of species with different drought tolerances.

RESULTS

We found contrasting shifts in associations for drought-sensitive and drought-tolerant species. For drought-sensitive species, 21% of the pairwise associations became more positive with increasing CWD, whereas 17% became more negative. In contrast, only 17% of the pairwise associations involving drought-tolerant species became more positive, whereas 27% became more negative in areas with a high CWD. Additionally, the incidence of positive associations increased with drought for drought-sensitive species and decreased for drought-tolerant species.

MAIN CONCLUSIONS

We found that associations of drought-sensitive plant species became more positive with drought, in line with the SGH. In contrast, associations of drought-tolerant species became more negative. Additionally, changes in associations of single species pairs were highly variable. Our results indicate that stress-modulated species associations might influence the distribution of species over large geographical extents, thus leading to unexpected responses under climate change through shifts in species associations.

Null-model-based network comparison reveals core associations

Microbial network construction and analysis is an important tool in microbial ecology. Such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks are error prone and do not necessarily reflect true community structure. We have developed anuran, a toolbox for investigation of noisy networks with null models. Such models allow researchers to generate data under the null hypothesis that all associations are random, supporting identification of nonrandom patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. We apply anuran to a time series of fecal samples from 20 women to demonstrate the existence of CANs in a subset of the sampled individuals. Moreover, we use data from the Global Sponge Project to demonstrate that orders of sponges have a larger CAN than expected at random. In conclusion, this toolbox is a resource for investigators wanting to compare microbial networks across conditions, time series, gradients, or hosts.

Identifying changing interspecific associations along gradients at multiple scales using wavelet correlation networks

Identifying interspecific associations is very important for understanding the community assembly process. However, most methods provide only an average association and assume that the association strength does not vary along the environmental gradient or with time. The scale effects are generally ignored. We integrated the idea of wavelet and network topological analysis to provide a novel way to detect nonrandom species associations across scales and along gradients using continuous or presence-absence ecological data. We first used a simulated species distribution data set to illustrate how the wavelet correlation analysis builds an association matrix and demonstrates its statistical robustness. Then, we applied the wavelet correlation network to a presence-absence data set of soil invertebrates. We found that the associations of invertebrates varied along an altitudinal gradient. We conclude by discussing several possible extensions of this method, such as predicting community assembly, utility in the temporal dimension, and the shifting effects of highly connected species within a community. The combination of the multiscale decomposition of wavelet and network topology analysis has great potential for fostering an understanding of the assembly and succession of communities, as well as predicting their responses to future climate change across spatial or temporal scales.

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.

Uncovering patterns of freshwater positive interactions using meta-analysis: Identifying the roles of common participants, invasive species and environmental context

Positive interactions are sensitive to human activities, necessitating synthetic approaches to elucidate broad patterns and predict future changes if these interactions are altered or lost. General understanding of freshwater positive interactions has been far outpaced by knowledge of these important relationships in terrestrial and marine ecosystems. We conducted a global meta-analysis to evaluate the magnitude of positive interactions across freshwater habitats. In 340 studies, we found substantial positive effects, with facilitators increasing beneficiaries by, on average, 81% across all taxa and response variables. Mollusks in particular were commonly studied as both facilitators and beneficiaries. Amphibians were one group benefiting the most from positive interactions, yet few studies investigated amphibians. Invasive facilitators had stronger positive effects on beneficiaries than non-invasive facilitators. We compared positive effects between high- and low-stress conditions and found no difference in the magnitude of benefit in the subset of studies that manipulated stressors. Future areas of research include understudied facilitators and beneficiaries, the stress gradient hypothesis, patterns across space or time and the influence of declining taxa whose elimination would jeopardise fragile positive interaction networks. Freshwater positive interactions occur among a wide range of taxa, influence populations, communities and ecosystem processes and deserve further exploration.

Elevation shapes biodiversity patterns through metacommunity-structuring processes

Stochastic (e.g., via species dispersal and ecological drift) and deterministic (e.g., via environmental and biotic filtering) processes can produce diversity patterns related to changes in elevation. However, existing studies have not generally examined these processes within a compressive framework. Stream macroinvertebrates are an important and diverse component of freshwater environments in high-mountain systems. By considering metacommunity-structuring processes using Hierarchical Modelling of Species Communities (HMSC), we investigated changes in taxon richness of stream macroinvertebrates along elevational gradients in streams of the Cangshan mountain range in Southwest China. We found that increasing taxon richness along the elevation gradient until the optimum was reached could be modeled using the integrated actions of full structuring processes within the metacommunity modeling. Consistent increases in taxon-richness along the elevation gradient were able to be modeled considering environmental filtering alone. In addition, the importance of structuring processes on shaping communities decreased along spatial hierarchical-scales (from local habitat to mountain-aspect levels). These results suggest that stochastic and biotic-filtering processes can confound environmental filtering in shaping macroinvertebrate communities in high-mountain streams. A comprehensive understanding of the mechanisms underlying elevational biodiversity patterns of riverine communities can be improved through quantitative frameworks (e.g., HMSC) linking metacommunity theory to the real-world systems.

Intraspecific host variation plays a key role in virus community assembly

Infection by multiple pathogens of the same host is ubiquitous in both natural and managed habitats. While intraspecific variation in disease resistance is known to affect pathogen occurrence, how differences among host genotypes affect the assembly of pathogen communities remains untested. In our experiment using cloned replicates of naive Plantago lanceolata plants as sentinels during a seasonal virus epidemic, we find non-random co-occurrence patterns of five focal viruses. Using joint species distribution modelling, we attribute the non-random virus occurrence patterns primarily to differences among host genotypes and local population context. Our results show that intraspecific variation among host genotypes may play a large, previously unquantified role in pathogen community structure.

What do tree-related microhabitats tell us about the abundance of forest-dwelling bats, birds, and insects?

Retaining trees during harvesting to conserve biodiversity is becoming increasingly common in forestry. To assess, select and monitor these habitat trees, ecologists and practitioners often use Tree-related Microhabitats (TreMs), which are assumed to represent the abundance and diversity of environmental resources for a wide range of forest-dwelling taxa. However, the relationship between TreMs and forest organisms is not fully understood. In this context, we attempted to identify and quantify the links between TreMs and three groups of forest organisms: insects, bats, and birds. Specifically, we tested whether species abundance is influenced by TreM abundance, either as direct predictor or as mediator of environmental predictors. We collected data in 86 temperate, 1-ha mixed forest plots and employed a hierarchical generalized mixed model to assess the influence of seven environmental predictors (aspect, number and height of standing dead trees, cover of herb and shrub layer, volume of lying deadwood, and terrain ruggedness index (TRI)) on the abundance of TreMs (15 groups) on potential habitat trees, insects (10 orders), bats (5 acoustic groups) and birds (29 species) as a function of seven environmental predictors: aspect, number and height of standing dead trees, cover of herb and shrub layer, volume of lying deadwood, and terrain ruggedness index (TRI). This allowed us to generate a correlation matrix with potential links between abundances of TreMs and co-occurring forest organisms. These correlations and the environmental predictors were tested in a structural equation model (SEM) to disentangle and quantify the effects of the environment from direct effects of TreMs on forest organisms. Four TreM groups showed correlations > |0.30| with forest organisms, in particular with insects and bats. Rot holes and concavities were directly linked with three insect groups and two bat groups. Their effect was smaller than effects of environmental predictors, except for the pairs "rot holes - Sternorrhyncha" and "rot holes - bats" of the Pipistrellus group. In addition, TreMs had indirect effects on forest organisms through mediating the effects of environmental predictors. We found significant associations between two out of fifteen TreM groups and five out of 44 forest organism groups. These results indicate that TreM abundance on potential habitat trees is not suited as a general indicator of the species abundance across broad taxonomic groups but possibly for specific target groups with proven links.

A framework to bridge scales in distribution modeling of soil microbiota

Creating accurate habitat suitability and distribution models (HSDMs) for soil microbiota is far more challenging than for aboveground organism groups. In this perspective paper, we propose a conceptual framework that addresses several of the critical issues holding back further applications. Most importantly, we tackle the mismatch between the broadscale, long-term averages of environmental variables traditionally used, and the environment as experienced by soil microbiota themselves. We suggest using nested sampling designs across environmental gradients and objectively integrating spatially hierarchic heterogeneity as covariates in HSDMs. Second, to incorporate the crucial role of taxa co-occurrence as driver of soil microbial distributions, we promote the use of joint species distribution models, a class of models that jointly analyze multiple species’ distributions, quantifying both species-specific environmental responses (i.e. the environmental niche) and covariance among species (i.e. biotic interactions). Our approach allows incorporating the environmental niche and its associated distribution across multiple spatial scales. The proposed framework facilitates the inclusion of the true relationships between soil organisms and their abiotic and biotic environments in distribution models, which is crucial to improve predictions of soil microbial redistributions as a result of global change.

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.

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.

Bacterial microbiota composition of Ixodes ricinus ticks: the role of environmental variation, tick characteristics and microbial interactions

Ecological factors, host characteristics and/or interactions among microbes may all shape the occurrence of microbes and the structure of microbial communities within organisms. In the past, disentangling these factors and determining their relative importance in shaping within-host microbiota communities has been hampered by analytical limitations to account for (dis)similar environmental preferences ('environmental filtering'). Here we used a joint species distribution modelling (JSDM) approach to characterize the bacterial microbiota of one of the most important disease vectors in Europe, the sheep tick Ixodes ricinus, along ecological gradients in the Swiss Alps. Although our study captured extensive environmental variation along elevational clines, the explanatory power of such large-scale ecological factors was comparably weak, suggesting that tick-specific traits and behaviours, microhabitat and -climate experienced by ticks, and interactions among microbes play an important role in shaping tick microbial communities. Indeed, when accounting for shared environmental preferences, evidence for significant patterns of positive or negative co-occurrence among microbes was found, which is indicative of competition or facilitation processes. Signals of facilitation were observed primarily among human pathogens, leading to co-infection within ticks, whereas signals of competition were observed between the tick endosymbiont Spiroplasma and human pathogens. These findings highlight the important role of small-scale ecological variation and microbe-microbe interactions in shaping tick microbial communities and the dynamics of tick-borne disease.

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.

Efficient estimation of generalized linear latent variable models

Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species are collected from a set of sites. Until very recently, the main challenge in fitting GLLVMs has been the lack of computationally efficient estimation methods. For likelihood based estimation, several closed form approximations for the marginal likelihood of GLLVMs have been proposed, but their efficient implementations have been lacking in the literature. To fill this gap, we show in this paper how to obtain computationally convenient estimation algorithms based on a combination of either the Laplace approximation method or variational approximation method, and automatic optimization techniques implemented in R software. An extensive set of simulation studies is used to assess the performances of different methods, from which it is shown that the variational approximation method used in conjunction with automatic optimization offers a powerful tool for estimation.