Spatial predictions at the community level: from current approaches to future frameworks

Genotype of pioneer plant Miscanthus is not a key factor in the structure of rhizosphere bacterial community in heavy metal polluted sites

Miscanthus is a common pioneer plant with abundant genetic variation in abandoned mines in southern China. However, the extent to which genetic differentiation among species modulates rhizosphere bacterial communities remains unclear. Miscanthus samples were collected from 26 typical abandoned heavy-metal mines with different soil types in southern China, tested using 14 pairs of simple sequence repeats (SSR) primers, and classified into two genotypes based on Nei's genetic distance. The structure and diversity of rhizosphere bacterial communities were examined using 16 S rRNA sequencing. The results showed that among the factors affecting the rhizosphere bacterial community structure of Miscanthus samples, the role of genotype was not significant, and geographical conditions were the most important factors, followed by pH and total organic carbon (TOC). The process of rhizospheric community assembly varied among different genotypes; however, the recruited species and their abundances were similar. Collectively, we provided an approach based on genetic differentiation to quantify the relative contribution of genotypes to the rhizosphere bacterial community, demonstrating that genotypes contribute less than soil conditions. Our findings provide new insights into the role of host genetics in the ecological processes of plant rhizosphere bacterial communities in abandoned mines and provide theoretical support for microbe-assisted phytoremediation.

Future trends of marine fish biomass distributions from the North Sea to the Barents Sea

Climate warming is one of the facets of anthropogenic global change predicted to increase in the future, its magnitude depending on present-day decisions. The north Atlantic and Arctic Oceans are already undergoing community changes, with warmer-water species expanding northwards, and colder-water species retracting. However, the future extent and implications of these shifts remain unclear. Here, we fitted a joint species distribution model to occurrence data of 107, and biomass data of 61 marine fish species from 16,345 fishery independent trawls sampled between 2004 and 2022 in the northeast Atlantic Ocean, including the Barents Sea. We project overall increases in richness and declines in relative dominance in the community, and generalised increases in species' ranges and biomass across three different future scenarios in 2050 and 2100. The projected decline of capelin and the practical extirpation of polar cod from the system, the two most abundant species in the Barents Sea, drove an overall reduction in fish biomass at Arctic latitudes that is not replaced by expanding species. Furthermore, our projections suggest that Arctic demersal fish will be at high risk of extinction by the end of the century if no climate refugia is available at eastern latitudes.

Natural warming differentiates communities and increases diversity in deep-sea Ridge Flank Hydrothermal Systems

Ridge Flank Hydrothermal Systems have discrete pockets of fluid discharge that mimic climate-induced ocean warming. Unlike traditional hydrothermal fluids, those discharged by Ridge Flank Hydrothermal Systems have a chemical composition indistinguishable from background water, enabling evaluation of the effect of warming temperature. Here we link temperature and terrain variables to community composition and biodiversity by combining remotely operated vehicle images of vent and non-vent zone communities with associated environmental variables. We show overall differences in composition, family richness, and biodiversity between zones, though richness and diversity were only significantly greater in vent zones at one location. Temperature was a contributing factor to observed greater biodiversity near vent zones. Overall, our results suggest that warming in the deep sea will affect species composition and diversity. However, due to the diverse outcomes projected for ocean warming, additional research is necessary to forecast the impacts of ocean warming on deep-sea ecosystems.

Mapping multiscale breeding bird species distributions across the United States and evaluating their conservation applications

Species distribution models are vital to management decisions that require understanding habitat use patterns, particularly for species of conservation concern. However, the production of distribution maps for individual species is often hampered by data scarcity, and existing species maps are rarely spatially validated due to limited occurrence data. Furthermore, community-level maps based on stacked species distribution models lack important community assemblage information (e.g., competitive exclusion) relevant to conservation. Thus, multispecies, guild, or community models are often used in conservation practice instead. To address these limitations, we aimed to generate fine-scale, spatially continuous, nationwide maps for species represented in the North American Breeding Bird Survey (BBS) between 1992 and 2019. We developed ensemble models for each species at three spatial resolutions-0.5, 2.5, and 5 km-across the conterminous United States. We also compared species richness patterns from stacked single-species models with those of 19 functional guilds developed using the same data to assess the similarity between predictions. We successfully modeled 192 bird species at 5-km resolution, 160 species at 2.5-km resolution, and 80 species at 0.5-km resolution. However, the species we could model represent only 28%-56% of species found in the conterminous US BBSs across resolutions owing to data limitations. We found that stacked maps and guild maps generally had high correlations across resolutions (median = 84%), but spatial agreement varied regionally by resolution and was most pronounced between the East and West at the 5-km resolution. The spatial differences between our stacked maps and guild maps illustrate the importance of spatial validation in conservation planning. Overall, our species maps are useful for single-species conservation and can support fine-scale decision-making across the United States and support community-level conservation when used in tandem with guild maps. However, there remain data scarcity issues for many species of conservation concern when using the BBS for single-species models.

Insight into the role of niche concept in deciphering the ecological drivers of MPs-associated bacterial communities in mangrove forest

Myriad inherent and variable environmental features are controlling the assembly and succession of bacterial communities colonizing on mangrove microplastics (MPs). However, the mechanisms governing mangrove MPs-associated bacterial responses to environmental changes still remain unknown. Here, we assessed the dissimilarities of MPs-associated bacterial composition, diversity and functionality as well as quantified the niche variations of each taxon on plastispheres along river-mangrove-ocean and mangrove landward-to-seaward gradients in the Beibu Gulf, China, respectively. The bacterial richness and diversity as well as the niche breadth on mangrove sedimentary MPs dramatically decreased from landward to seaward regions. Characterizing the niche variations linked the difference of ecological drivers of MPs-associated bacterial populations and functions between river-mangrove-ocean (microplastic properties) and mangrove landward-to-seaward plastispheres (sediment physicochemical properties) to the trade-offs between selective stress exerted by inherent plastic substrates and microbial competitive stress imposed by environmental conditions. Notably, Rhodococcus erythropolis was predicted to be the generalist species and closely associated to biogeochemical cycles of mangrove plastispheres. Our work provides a reliable pathway for tackling the hidden mechanisms of environmental factors driving MPs-associated microbe from perspectives of niches and highlights the spatial dynamic variations of mangrove MPs-associated bacteria.

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.

Environmental sensitivity assessment and land degradation in southeastern Serbia: application of modified MEDALUS model

This paper aims to improve the methodology and results accuracy of MEDALUS model for assessing land degradation sensitivity through the application of different data detail levels and by introducing the application of Ellenberg indices in metrics related to vegetation drought sensitivity assessment. For that purpose, the MEDALUS model was applied at 2 levels of detail. Level I (municipality level) implied the use of available large-scale databases and level II (watershed) contains more detailed information about vegetation used in the calculation of the VQI and MQI factors (Fig. S6). The comparison was made using data based on CORINE Land Cover (2012) and forest inventory data, complemented with object-based classification. Results showed that data based on forest inventory data with the application of Ellenberg's indices and object-based classification have one class more, critical (C1 and C2) and that the percentage distribution of classes is different in both quantitative (area size of class sensitivity) and qualitative (aggregation and dispersion of sensitivity classes). The use of data from Forest Management Plans and the application of Ellenberg's indices affect the quality of the results and find its application in the model, especially if these results are used for monitoring and land area management on fine scales. Remote sensed data images (Sentinel-2B) were introduced into the methodology as a very important environmental monitoring tool and model results validation.

Geographical shifts in the successional dynamics of inland dune shrub communities

Species' environmental requirements and large-scale spatial and evolutionary processes determine the structure and composition of local communities. However, ecological interactions also have major effects on community assembly at landscape and local scales. We evaluate whether two xerophytic shrub communities occurring in SW Portugal follow constrained ecological assembly dynamics throughout large geographical extents, or their composition is rather driven by species' individualistic responses to environmental and macroecological constraints. Inland dune xerophytic shrub communities were characterized in 95 plots. Then, we described the main gradients of vegetation composition and assessed the relevance of biotic interactions. We also characterized the habitat suitability of the dominant species, Stauracanthus genistoides, and Ulex australis, to map the potential distribution of the xerophytic shrub communities. Finally, we examined the relationships between the vegetation gradients and a broad set of explanatory variables to identify the relative importance of each factor driving changes in community composition. We found that xerophytic shrubs follow uniform successional patterns throughout the whole geographical area studied, but each community responds differently to the main environmental gradients in each region. Soil organic matter is the main determinant of community variations in the northern region, Setúbal Peninsula, whereas aridity is so in the South/South-Western region. In contrast, in the central region, Comporta, the variation between S. genistoides and U. australis communities is explained mainly by aridity and temperature seasonality, followed by the individualistic responses of the dominant species and soil organic matter. Overall, these results indicate that, the relative importance of the main factors causing community-level responses varies according to regional processes and the suitability of the environmental conditions for the dominant species in these communities. These responses are also determined by intrinsic community mechanisms that result in a high degree of similarity in the gradient-driven community stages in different regions.

BetaBayes—A Bayesian Approach for Comparing Ecological Communities

Ecological communities change because of both natural and human factors. Distinguishing between the two is critical to ecology and conservation science. One of the most common approaches for modelling species composition changes is calculating beta diversity indices and then relating index changes to environmental changes. The main difficulty with these analyses is that beta diversity indices are paired comparisons, which means indices calculated with the same community are not independent. Mantel tests and generalised dissimilarity modelling (GDM) are two of the most commonly used statistical procedures for analysing such data, employing randomisation tests to consider the data’s dependence. Here, we introduce a Bayesian model-based approach called BetaBayes that explicitly incorporates the data dependence. This approach is based on the Bradley–Terry model, which is a widely used approach for modelling paired comparisons that involves building a standard regression model containing two varying intercepts, one for each community involved in the beta diversity index, that capture their respective contributions. We used BetaBayes to analyse a famous dataset collected in Panama that contains information on multiple 1 ha plots from the rain forests of Panama. We calculated the Bray–Curtis index between all pairs of plots, analysed the relationship between the index and two covariates (geographic distance and elevation), and compared the results of BetaBayes with those from the Mantel test and GDM. BetaBayes has two distinctive features. The first is its flexibility, which allows the user to quickly change it to fit the data structure; namely, by adding varying effects, incorporating spatial autocorrelation, and modelling complex nonlinear relationships. The second is that it provides a clear path for performing model validation and model improvement. BetaBayes avoids hypothesis testing, instead focusing on recreating the data generating process and quantifying all the model configurations that are consistent with the observed data.

Soil microbial community assembly model in response to heavy metal pollution

Heavy metal pollution affected the stability and function of soil ecosystem. The impact of heavy metals on soil microbial community and the interaction of microbial community has been widely studied, but little was known about the response of community assembly to the heavy metal pollution. In this study, we collected 30 soil samples from non (CON), moderately (CL) and severely (CH) contaminated fields. The prokaryotic community was studied using high-throughput Illumina sequencing of 16s rRNA gene amplicons, and community assembly were quantified using phylogenetic-bin-based null approach (iCAMP). Results showed that diversity and composition of both bacterial and archaeal community changed significantly in response to heavy metal pollution. The microbial community assembly tended to be more deterministic with the increase of heavy metal concentration. Among the assembly processes, the relative importance of homogeneous selection (deterministic process) increased significantly (increased by 16.2%), and the relative importance of drift and dispersal limitation (stochastic process) decreased significantly (decreased by 11.4% and 5.4%, respectively). The determinacy of bacterial and archaeal community assembly also increased with heavy metal stress, but the assembly models were different. The deterministic proportion of microorganisms tolerant to heavy metals, such as Thiobacillus, Euryarchaeota and Crenarchaeota (clustered in bin 32, bin59 and bin60, respectively) increased, while the stochastic proportion of microorganisms sensitive to heavy metals, such as Koribacteraceae (clustered in bin23) increased. Therefore, the heavy metal stress made the prokaryotic community be deterministic, however, the effects on the assembly process of different microbial groups differed obviously.

Is scat marking a reliable tool for otter census and surveys at the landscape scale?

Biological significance of scat marking by otters has been a controversial subject among scientists. Using multiyear (2014-2017) data of otter spraint counts in South Korea, this study aimed to test whether the observed pattern of spraint presence/absence is driven by detection error and if/how scat counts can be a proxy for otter abundance at the landscape scale. To test the first hypothesis, spraint presence/absence was analyzed through occupancy models, which relied on environmental variables related to otter detectability and presence. Spraint count models were used to test the second hypothesis against resource-related covariates in combination with landscape, anthropogenic, and climate variables through machine learning algorithms (MLAs). The detection probability has specifically decreased in areas characterized by high rainfall and human population densities, whereas the probability has increased near food-rich sites, characterized by high marking frequencies. The temporal trends of spraint count predictions were in line with changes in the diversity of fish communities in 2014-2017 instead of fish biomass, suggesting that the availability of feeding resources is higher where fish communities are more diverse. Because diverse fish communities can attract otters, fish diversity conservation is critical for preserving this mammal's populations. This fine scale four-year monitoring has contributed to the disentanglement of the role of spraint presence/absence and spraint counts in detectability and population trends. This will assist in identifying key resource areas and planning strategies to promote otter conservation and dispersal dynamics.

Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate?

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

A Science Agenda to Inform Natural Resource Management Decisions in an Era of Ecological Transformation

Earth is experiencing widespread ecological transformation in terrestrial, freshwater, and marine ecosystems that is attributable to directional environmental changes, especially intensifying climate change. To better steward ecosystems facing unprecedented and lasting change, a new management paradigm is forming, supported by a decision-oriented framework that presents three distinct management choices: resist, accept, or direct the ecological trajectory. To make these choices strategically, managers seek to understand the nature of the transformation that could occur if change is accepted while identifying opportunities to intervene to resist or direct change. In this article, we seek to inspire a research agenda for transformation science that is focused on ecological and social science and based on five central questions that align with the resist–accept–direct (RAD) framework. Development of transformation science is needed to apply the RAD framework and support natural resource management and conservation on our rapidly changing planet.

Predicting spatial patterns of soil bacteria under current and future environmental conditions

Soil bacteria are largely missing from future biodiversity assessments hindering comprehensive forecasts of ecosystem changes. Soil bacterial communities are expected to be more strongly driven by pH and less by other edaphic and climatic factors. Thus, alkalinisation or acidification along with climate change may influence soil bacteria, with subsequent influences for example on nutrient cycling and vegetation. Future forecasts of soil bacteria are therefore needed. We applied species distribution modelling (SDM) to quantify the roles of environmental factors in governing spatial abundance distribution of soil bacterial OTUs and to predict how future changes in these factors may change bacterial communities in a temperate mountain area. Models indicated that factors related to soil (especially pH), climate and/or topography explain and predict part of the abundance distribution of most OTUs. This supports the expectations that microorganisms have specific environmental requirements (i.e., niches/envelopes) and that they should accordingly respond to environmental changes. Our predictions indicate a stronger role of pH over other predictors (e.g. climate) in governing distributions of bacteria, yet the predicted future changes in bacteria communities are smaller than their current variation across space. The extent of bacterial community change predictions varies as a function of elevation, but in general, deviations from neutral soil pH are expected to decrease abundances and diversity of bacteria. Our findings highlight the need to account for edaphic changes, along with climate changes, in future forecasts of soil bacteria.

Climate change may induce connectivity loss and mountaintop extinction in Central American forests

The tropical forests of Central America serve a pivotal role as biodiversity hotspots and provide ecosystem services securing human livelihood. However, climate change is expected to affect the species composition of forest ecosystems, lead to forest type transitions and trigger irrecoverable losses of habitat and biodiversity. Here, we investigate potential impacts of climate change on the environmental suitability of main plant functional types (PFTs) across Central America. Using a large database of occurrence records and physiological data, we classify tree species into trait-based groups and project their suitability under three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) with an ensemble of state-of-the-art correlative modelling methods. Our results forecast transitions from wet towards generalist or dry forest PFTs for large parts of the study region. Moreover, suitable area for wet-adapted PFTs is projected to latitudinally diverge and lose connectivity, while expected upslope shifts of montane species point to high risks of mountaintop extinction. These findings underline the urgent need to safeguard the connectivity of habitats through biological corridors and extend protected areas in the identified transition hotspots.

What can intraspecific trait variability tell us about fungal communities and adaptations?

Analyses of species functional traits are suitable to better understand the coexistence of species in a given environment. Trait information can be applied to investigate diversity patterns along environmental gradients and subsequently to predict and mitigate threats associated with climate change and land use. Species traits are used to calculate community trait means, which can be related to environmental gradients. However, while species traits can provide insights into the mechanisms underlying community assembly, they can lead to erroneous inferences if mean trait values are used. An alternative is to incorporate intraspecific trait variability (ITV) into calculating the community trait means. This approach gains increasing acceptance in plant studies. For macrofungi, functional traits have recently been applied to examine their community ecology but, to our knowledge, ITV has yet to be incorporated within the framework of community trait means. Here, we present a conceptual summary of the use of ITV to investigate the community ecology of macrofungi, including the underlying ecological theory. Inferences regarding community trait means with or without the inclusion of ITV along environmental gradients are compared. Finally, an existing study is reconsidered to highlight the variety of possible outcomes when ITV is considered. We hope this Opinion will increase awareness of the potential for within-species trait variability and its importance for statistical inferences, interpretations, and predictions of the mechanisms structuring communities of macro- and other fungi.

Forest management can mitigate negative impacts of climate and land-use change on plant biodiversity: Insights from the Republic of Korea

Over the past century, the decline in biodiversity due to climate change and habitat loss has become unprecedentedly serious. Multiple drivers, including climate change, land-use/cover change, and qualitative change in habitat need to be considered in an integrated approach, which has rarely been taken, to create an effective conservation strategy. The purpose of this study is to quantitatively evaluate and map the combined impacts of those multiple drivers on biodiversity in the Republic of Korea (ROK). To this end, biodiversity persistence (BP) was simulated by employing generalized dissimilarity modeling with estimates of habitat conditions. Habitat Condition Index was newly developed based on national survey datasets to represent the changes in habitat quality according to the land cover changes and forest management, especially after the ROK's National Reforestation Programme. The changes in habitat conditions were simulated for a period ranging from the 1960s to the 2010s; additionally, future (2050s) spatial scenarios were constructed. By focusing on the changes in forest habitat quality along with climate and land use, this study quantitatively and spatially analyzed the changes in BP over time and presented the effects of reforestation and forest management. The results revealed that continuous forest management had a positive impact on BP by offsetting the negative effects of past urbanization. Improvements in forest habitat quality also can effectively reduce the negative impacts of climate change. This quantitative analysis of successful forest restoration in Korea proved that economic development and urbanization could be in parallel with biodiversity enhancement. Nevertheless, current forest management practices were found to be insufficient in fully offsetting the decline in future BP caused by climate change. This indicates that there is a need for additional measures along with mitigation of climate change to maintain the current biodiversity level.

Using Climatic Credits to Pay the Climatic Debt

Many organisms are accumulating climatic debt as they respond more slowly than expected to rising global temperatures, leading to disequilibrium of species diversity with contemporary climate. The resulting transient dynamics are complex and may cause overoptimistic biodiversity assessments. We propose a simple budget framework to integrate climatic debt with two classes of intervention: (i) climatic credits that pay some of the debt, reducing the overall biological change required to reach a new equilibrium; and (ii) options to adjust the debt repayment rate, either making a system more responsive by increasing the rate or temporarily reducing the rate to buy more time for local adaptation and credit implementation. We illustrate how this budget can be created and highlight limitations and challenges.

Using functional traits to model annual plant community dynamics

Predicting the response of biological communities to changes in the environment or management is a fundamental pursuit of community ecology. Meeting this challenge requires the integration of multiple processes: habitat filtering, niche differentiation, biotic interactions, competitive exclusion, and stochastic demographic events. Most approaches to this long-standing problem focus either on the role of the environment, using trait-based filtering approaches, or on quantifying biotic interactions with process-based community dynamics models. We introduce a novel approach that uses functional traits to parameterize a process-based model. By combining the two approaches we make use of the extensive literature on traits and community filtering as a convenient means of reducing the parameterization requirements of a complex population dynamics model whilst retaining the power to capture the processes underlying community assembly. Using arable weed communities as a case study, we demonstrate that this approach results in predictions that show realistic distributions of traits and that trait selection predicted by our simulations is consistent with in-field observations. We demonstrate that trait-based filtering approaches can be combined with process-based models to derive the emergent distribution of traits. While initially developed to predict the impact of crop management on functional shifts in weed communities, our approach has the potential to be applied to other annual plant communities if the generality of relationships between traits and model parameters can be confirmed.

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.

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.

Soil heterogeneity increases plant diversity after 20 years of manipulation during grassland restoration

The "environmental heterogeneity hypothesis" predicts that variability in resources promotes species coexistence, but few experiments support this hypothesis in plant communities. A previous 15-yr test of this hypothesis in a prairie restoration experiment demonstrated a weak effect of manipulated soil resource heterogeneity on plant diversity. This response was attributed to a transient increase in richness following a post-restoration supplemental propagule addition, occasionally higher diversity under nutrient enrichment, and reduced cover of a dominant species in a subset of soil treatments. Here, we report community dynamics under continuous propagule addition in the same experiment, corresponding to 16-20 yr of restoration, in response to altered availability and heterogeneity of soil resources. We also quantified traits of newly added species to determine if heterogeneity increases the amount and variety of niches available for new species to exploit. The heterogeneous treatment contained a factorial combination of altered nutrient availability and soil depth; control plots had no manipulations. Total diversity and richness were higher in the heterogeneous treatment during this 5-yr study due to higher cover, diversity, and richness of previously established forbs, particularly in the N-enriched subplots. All new species added to the experiment exhibited unique trait spaces, but there was no evidence that heterogeneous plots contained a greater variety of new species representing a wider range of trait spaces relative to the control treatment. The richness and cover of new species was higher in N-enriched soil, but the magnitude of this response was small. Communities assembling under long-term N addition were dominated by different species among subplots receiving added N, leading to greater dispersion of communities among the heterogeneous relative to control plots. Contrary to the deterministic mechanism by which heterogeneity was expected to increase diversity (greater variability in resources for new species to exploit), higher diversity in the heterogeneous plots resulted from destabilization of formerly grass-dominated communities in N-enriched subplots. While we do not advocate increasing available soil N at large scales, we conclude that the positive effect of environmental heterogeneity on diversity can take decades to materialize and depend on development of stochastic processes in communities with strong establishment limitation.

Thermoregulatory traits combine with range shifts to alter the future of montane ant assemblages

Predicting and understanding the biological response to future climate change is a pressing challenge for humanity. In the 21st century, many species will move into higher latitudes and higher elevations as the climate warms. In addition, the relative abundances of species within local assemblages are likely to change. Both effects have implications for how ecosystems function. Few biodiversity forecasts, however, take account of both shifting ranges and changing abundances. We provide a novel analysis predicting the potential changes to assemblage-level relative abundances in the 21st century. We use an established relationship linking ant abundance and their colour and size traits to temperature and UV-B to predict future abundance changes. We also predict future temperature driven range shifts and use these to alter the available species pool for our trait-mediated abundance predictions. We do this across three continents under a low greenhouse gas emissions scenario (RCP2.6) and a business-as-usual scenario (RCP8.5). Under RCP2.6, predicted changes to ant assemblages by 2100 are moderate. On average, species richness will increase by 26%, while species composition and relative abundance structure will be 26% and 30% different, respectively, compared with modern assemblages. Under RCP8.5, however, highland assemblages face almost a tripling of species richness and compositional and relative abundance changes of 66% and 77%. Critically, we predict that future assemblages could be reorganized in terms of which species are common and which are rare: future highland assemblages will not simply comprise upslope shifts of modern lowland assemblages. These forecasts reveal the potential for radical change to montane ant assemblages by the end of the 21st century if temperature increases continue. Our results highlight the importance of incorporating trait-environment relationships into future biodiversity predictions. Looking forward, the major challenge is to understand how ecosystem processes will respond to compositional and relative abundance changes.

Dynamic and diverse amphibian assemblages: Can we differentiate natural processes from human induced changes?

Amphibians are sensitive to anthropogenic habitat alterations but also respond to natural drivers of assemblage composition at many levels. Additionally, they are usually hard to detect in field inventories. We used a multiscale approach, from microhabitat to the landscape levels, to try to understand the effects of natural changes, and try to distinguish them from the effects of landscape level anthropogenic changes, on dynamic and diverse anuran assemblages, taking imperfect detection into account. We conducted thorough field inventories in 16 streams at Serra do Cipó, in the southern portion of Espinhaço Mountain Range, southeastern Brazil, during two time periods separated by 16 years. We compared species richness and diversity between periods, sampling both tadpoles and adult frogs. We quantified tadpole microhabitat availability, alterations in immediate riparian vegetation, and changes in classes of land cover within buffers around streams (adult habitats) to test for their effects on species composition. We also tested for effects of human occupancy around streams on nestedness and turnover components of species diversity. Microhabitats and riparian vegetation explained some of the changes in species composition (or detection) between time periods. Nestedness seemed to be influenced by the stability of the landscape. Detectabilities were too low to support robust occupancy estimates for most species. Natural changes in local habitats occupied by anurans in montane meadows are likely to influence species distribution. Some species with robust estimates experienced change in their occupancy over the studied 16-year interval, although no anthropogenic causes could be directly associated with such changes. The low detectability of most species, even with thorough sampling effort, makes it very hard to detect amphibian declines and possibly tell them apart from natural population fluctuations. New techniques are needed that improve species detectability in such diverse tropical habitats.

Ecological indicator values reveal missing predictors of species distributions

The questions of how much abiotic environment contributes to explain species distributions, and which abiotic factors are the most influential, are key when projecting species realized niches in space and time. Here, we show that answers to these questions can be obtained by using species' ecological indicator values (EIVs). By calculating community averages of plant EIVs (397 plant species and 3988 vegetation plots), we found that substituting mapped environmental predictors with site EIVs led to a doubling of explained variation (22.5% to 44%). EIVs representing light and soil showed the highest model improvement, while EIVs representing temperature did not explain additional variance, suggesting that current temperature maps are already fairly accurate. Therefore, although temperature is frequently reported as having a dominant effect on species distributions over other factors, our results suggest that this might primarily result from limitations in our capacity to map other key environmental factors, such as light and soil properties, over large areas.

Univariate Community Assembly Analysis (UniCAA): Combining hierarchical models with null models to test the influence of spatially restricted dispersal, environmental filtering, and stochasticity on community assembly

Identifying the influence of stochastic processes and of deterministic processes, such as dispersal of individuals of different species and trait-based environmental filtering, has long been a challenge in studies of community assembly. Here, we present the Univariate Community Assembly Analysis (UniCAA) and test its ability to address three hypotheses: species occurrences within communities are (a) limited by spatially restricted dispersal; (b) environmentally filtered; or (c) the outcome of stochasticity-so that as community size decreases-species that are common outside a local community have a disproportionately higher probability of occurrence than rare species. The comparison with a null model allows assessing if the influence of each of the three processes differs from what one would expect under a purely stochastic distribution of species. We tested the framework by simulating "empirical" metacommunities under 15 scenarios that differed with respect to the strengths of spatially restricted dispersal (restricted vs. not restricted); habitat isolation (low, intermediate, and high immigration rates); and environmental filtering (strong, intermediate, and no filtering). Through these tests, we found that UniCAA rarely produced false positives for the influence of the three processes, yielding a type-I error rate ≤5%. The type-II error rate, that is, production of false negatives, was also acceptable and within the typical cutoff (20%). We demonstrate that the UniCAA provides a flexible framework for retrieving the processes behind community assembly and propose avenues for future developments of the framework.