Estimating genus-specific effects of non-native honey bees and urbanization on wild bee communities: A case study in Maryland, United States.

Non-native species have the potential to detrimentally affect native species through resource competition, disease transmission, and other forms of antagonism. The western honey bee (Apis mellifera) is one such species that has been widely introduced beyond its native range for hundreds of years. There are strong concerns in the United States, and other countries, about the strain that high-density, managed honey bee populations could pose to already imperiled wild bee communities. While there is some experimental evidence of honey bees competing with wild bees for resources, few studies have connected landscape-scale honey bee apiary density with down-stream consequences for wild bee communities. Here, using a dataset from Maryland, US and joint species distribution models, we provide the largest scale, most phylogenetically resolved assessment of non-native honey bee density effects on wild bee abundance to date. As beekeeping in Maryland primarily consists of urban beekeeping, we also assessed the relative impact of developed land on wild bee communities. Six of the 33 wild bee genera we assessed showed a high probability (> 90 %) of a negative association with apiary density and/or developed land. These bees were primarily late-season, specialist genera (several long-horned genera represented) or small, ground nesting, season-long foragers (including several sweat bee genera). Conversely, developed land was associated with an increase in relative abundance for some genera including invasive Anthidium and other urban garden-associated genera. We discuss several avenues to ameliorate potentially detrimental effects of beekeeping and urbanization on the most imperiled wild bee groups. We additionally offer methodological insights based on sampling efficiency of different methods (hand netting, pan trapping, vane trapping), highlighting large variation in effect sizes across genera. The magnitude of sampling effect was very high, relative to the observed ecological effects, demonstrating the importance of integrated sampling, particularly for multi-species or community level assessments.

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.

Experimental evidence that root-associated fungi improve plant growth at high altitude.

Unravelling how species communities change along environmental gradients requires a dual understanding: the direct responses of the species to their abiotic surroundings and the indirect variation of these responses through biotic interactions. Here, we focus on the interactive relationships between plants and their symbiotic root-associated fungi (RAF) along stressful abiotic gradients. We investigate whether variations in RAF community composition along altitudinal gradients influence plant growth at high altitudes, where both plants and fungi face harsher abiotic conditions. We established a translocation experiment between pairs of Bistorta vivipara populations across altitudinal gradients. To separate the impact of shifting fungal communities from the overall influence of changing abiotic conditions, we used a root barrier to prevent new colonization by RAF following translocation. To characterize the RAF communities, we applied DNA barcoding to the root samples. Through the utilization of joint species distribution modelling, we assessed the relationship between changes in plant functional traits resulting from experimental treatments and the corresponding changes in the RAF communities. Our findings indicate that RAF communities influence plant responses to stressful abiotic conditions. Plants translocated from low to high altitudes grew more when they were able to associate with the resident high-altitude RAF compared to those plants that were not allowed to associate with the resident RAF. We conclude that interactions with RAF impact how plants respond to stressful abiotic conditions. Our results provide experimental support that interactions with RAF improve plant stress tolerance to altitudinal stressors such as colder temperatures and less nutrient availability.

Pólya-splitting distributions as stationary solutions of multivariate birth-death processes under extended neutral theory.

Multivariate count distributions are crucial for the inference of ecological processes underpinning biodiversity. In particular, neutral theory provides useful null distributions allowing the evaluation of adaptation or natural selection. In this paper, we build a broader family of multivariate distributions: the Polya-splitting distributions. We show that they emerge naturally as stationary distributions of a multivariate birth-death process. This family of distributions is a consistent extension of non-zero sum neutral models based on a master equation approach. It allows considering both total abundance of the community and relative abundances of species. We emphasize that this family is large enough to encompass various dependence structures among species. We also introduce the strong closure under addition property that can be useful to generate nested multi-level dependence structures. Although all Pólya splitting distributions do not share this property, we provide numerous example verifying it. They include the previously known example with independent species, and also new ones with alternative dependence structures. Overall, we advocate that Polya-splitting distribution should become a part of the classic toolbox for the analysis of multivariate count data in ecology, providing alternative approaches to joint species distribution framework. Comparatively, our approach allows to model dependencies between species at the observation level, while the classical JSDM's model dependencies at the latent process strata.

A cloudy forecast for species distribution models: Predictive uncertainties abound for California birds after a century of climate and land-use change.

Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.

Evaluating the predictive performance of presence-absence models: Why can the same model appear excellent or poor?

When comparing multiple models of species distribution, models yielding higher predictive performance are clearly to be favored. A more difficult question is how to decide whether even the best model is "good enough". Here, we clarify key choices and metrics related to evaluating the predictive performance of presence-absence models. We use a hierarchical case study to evaluate how four metrics of predictive performance (AUC, Tjur's R 2, max-Kappa, and max-TSS) relate to each other, the random and fixed effects parts of the model, the spatial scale at which predictive performance is measured, and the cross-validation strategy chosen. We demonstrate that the very same metric can achieve different values for the very same model, even when similar cross-validation strategies are followed, depending on the spatial scale at which predictive performance is measured. Among metrics, Tjur's R 2 and max-Kappa generally increase with species' prevalence, whereas AUC and max-TSS are largely independent of prevalence. Thus, Tjur's R 2 and max-Kappa often reach lower values when measured at the smallest scales considered in the study, while AUC and max-TSS reaching similar values across the different spatial levels included in the study. However, they provide complementary insights on predictive performance. The very same model may appear excellent or poor not only due to the applied metric, but also how predictive performance is exactly calculated, calling for great caution on the interpretation of predictive performance. The most comprehensive evaluation of predictive performance can be obtained by evaluating predictive performance through the combination of measures providing complementary insights. Instead of following simple rules of thumb or focusing on absolute values, we recommend comparing the achieved predictive performance to the researcher's own a priori expectations on how easy it is to make predictions related to the same question that the model is used for.

Alternative measures of trait-niche relationships: A test on dispersal traits in saproxylic beetles.

Functional trait approaches are common in ecology, but a lack of clear hypotheses on how traits relate to environmental gradients (i.e., trait-niche relationships) often makes uncovering mechanisms difficult. Furthermore, measures of community functional structure differ in their implications, yet inferences are seldom compared among metrics. Community-weighted mean trait values (CWMs), a common measure, are largely driven by the most common species and thus do not reflect community-wide trait-niche relationships per se. Alternatively, trait-niche relationships can be estimated across a larger group of species using hierarchical joint species distribution models (JSDMs), quantified by a parameter Γ. We investigated how inferences about trait-niche relationships are affected by the choice of metric. Using deadwood-dependent (saproxylic) beetles in fragmented Finnish forests, we followed a protocol for investigating trait-niche relationships by (1) identifying environmental filters (climate, forest age, and deadwood volume), (2) relating these to an ecological function (dispersal ability), and (3) identifying traits related to this function (wing morphology). We tested 18 hypothesized dispersal relationships using both CWM and Γ estimates across these environmental gradients. CWMs were more likely than Γ to show support for trait-niche relationships. Up to 13% of species' realized niches were explained by dispersal traits, but the directions of effects were consistent with fewer than 11%-39% of our 18 trait-niche hypotheses (depending on the metric used). This highlights the difficulty in connecting morphological traits and ecological functions in insects, despite the clear conceptual link between landscape connectivity and flight-related traits. Caution is thus warranted in hypothesis development, particularly where apparent trait-function links are less clear. Inferences differ when CWMs versus Γ estimates are used, necessitating the choice of a metric that reflects study questions. CWMs help explain the effects of environmental gradients on community trait composition, whereas the effects of traits on species' niches are better estimated using hierarchical JSDMs.

Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf.

The eastern North Pacific right whale (NPRW) is the most endangered population of whale and has been observed north of its core feeding ground in recent years with low sea ice extent. Sea ice and water temperature are important drivers for zooplankton dynamics within the whale's core feeding ground in the southeastern Bering Sea, seasonally forming stable fronts along the shelf that give rise to distinct zooplankton communities. A northward shift in NPRW distribution driven by changing distribution of prey resources could put this species at increased risk of entanglement and vessel strikes. We modeled the abundance of NPRW prey, Calanus glacialis, Neocalanus, and Thysanoessa species, using a dynamic biophysical food web model of nine zooplankton guilds in the Bering shelf zooplankton community during a period of warming (2006-2016). This model is unique among prior zooplankton studies from the region in that it includes density dependence, thereby allowing us to ask whether species interactions influence zooplankton dynamics. Modeling confirmed the importance of sea ice and ocean temperature to zooplankton dynamics in the region. Density-independent growth drove community dynamics, while dependent factors were comparatively minimal. Overall, Calanus responded to environment terms, with the strength and direction of response driven by copepodite stage. Neocalanus and Thysanoessa responses were weaker, likely due to their primary occurrence on the outer shelf. We also modeled the steady-state (equilibrium) abundance of Calanus in conditions with and without wind gusts to test whether advection of outer shelf species might disrupt the steady-state dynamics of Calanus abundance; the results did not support disruption. Given the annual fall sampling design, we interpret our results as follows: low-ice-extent winters induced stronger spring winds and weakened fronts on the shelf, thereby advecting some outer shelf species into the study region; increased development rates in these warm conditions influenced the proportion of C. glacialis copepodite stages over the season. Residual correlation suggests missing drivers, possibly predators, and phytoplankton bloom composition. Given the continued loss of sea ice in the region and projected continued warming, our findings suggest that C. glacialis will move northward, and thus, whales may move northward to continue targeting them.

Surface and subsurface oceanographic features drive forage fish distributions and aggregations: Implications for prey availability to top predators in the US Northeast Shelf ecosystem.

Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface-level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface- and shallow-foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator-prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface-level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species- and community-level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub-mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data.

Fuel connectivity, burn severity, and seed bank survivorship drive ecosystem transformation in a semiarid shrubland.

A key challenge in ecology is understanding how multiple drivers interact to precipitate persistent vegetation state changes. These state changes may be both precipitated and maintained by disturbances, but predicting whether the state change will be fleeting or persistent requires an understanding of the mechanisms by which disturbance affects the alternative communities. In the sagebrush shrublands of the western United States, widespread annual grass invasion has increased fuel connectivity, which increases the size and spatial contiguity of fires, leading to postfire monocultures of introduced annual grasses (IAG). The novel grassland state can be persistent and is more likely to promote large fires than the shrubland it replaced. But the mechanisms by which prefire invasion and fire occurrence are linked to higher postfire flammability are not fully understood. A natural experiment to explore these interactions presented itself when we arrived in northern Nevada immediately after a 50,000 ha wildfire was extinguished. We hypothesized that the novel grassland state is maintained via a reinforcing feedback where higher fuel connectivity increases burn severity, which subsequently increases postfire IAG dispersal, seed survivorship, and fuel connectivity. We used a Bayesian joint species distribution model and structural equation model framework to assess the strength of the support for each element in this feedback pathway. We found that prefire fuel connectivity increased burn severity and that higher burn severity had mostly positive effects on the occurrence of IAG and another nonnative species and mostly negative or neutral relationships with all other species. Finally, we found that the abundance of IAG seeds in the seed bank immediately after a fire had a positive effect on the fuel connectivity 3 years after the fire, completing a positive feedback promoting IAG. These results demonstrate that the strength of the positive feedback is controlled by measurable characteristics of ecosystem structure, composition, and disturbance. Further, each node in the loop is affected independently by multiple global change drivers. It is possible that these characteristics can be modeled to predict threshold behavior and inform management actions to mitigate or slow the establishment of the grass-fire cycle, perhaps via targeted restoration applications or prefire fuel treatments.

Responses of coastal fishery resources to rapid environmental changes.

Coastal systems experience strong impacts of ongoing environmental change, affecting fish communities and subsequently fishery yields. In the Baltic Sea, the combined effects of climate-induced changes and eutrophication-related pressures constitute major threats to its living resources. Although much work has been devoted to uncovering environmental impacts on the commercially most valuable fish stocks, only little is known about community-wide responses of fished species and how environmental change may affect their yield. In this study, the authors use a joint species distribution modelling framework to disentangle environmental impacts on species-specific fishery yields of 16 fished species along the coast of Finland over four decades. The authors show that environmental covariates substantially contributed to variations in fishery yields and are likely to have strong impacts on fished resources also in the future. Salinity and near-bottom oxygen concentration emerged as the strongest environmental drivers of yields at the community level, whereas temperature was particularly important for cod (Gadus morhua) and sprat (Sprattus sprattus) yields. The authors found shore density to be an important predictor for fisheries resources especially for freshwater fish. The results of this study suggest that the changes in environmental conditions during the past four decades had a positive effect on the yields of freshwater and warm-affinity species, whereas yields of marine cold-affinity species have been mainly negatively affected by contracting favourable habitats, becoming warmer and less saline.

Microbial Interactions - Underexplored Links Between Public Health Relevant Bacteria and Protozoa in Coastal Environments.

The co-existence of bacteria and protozoa in aquatic environments has led to the evolution of predation defense mechanisms by the bacteria. Some of the predation-resistant bacteria (PRB) are also pathogenic to humans and other mammals. The links between PRB and protozoa in natural aquatic systems are poorly known, but they are important in predicting outbreaks and determining the long-term consequences of a contamination event. To elucidate co-occurrence patterns between PRB (16S rRNA) and bacterivorous protozoa (18S rRNA), we performed a field study in a coastal area in the northern Baltic Sea. Interactions between bacteria and protozoa were explored by using two complementary statistical tools. We found co-occurrence patterns between specific PRB and protozoa, such as Legionella and Ciliophora, and we also found that the interactions are genotype-specific as, for example, Rickettsia. The PRB sequence diversity was larger in bays and freshwater inlets compared to offshore sites, indicating local adaptions. Considering the PRB diversity in the freshwater in combination with the large spring floods in the area, freshwater influxes should be considered a potential source of PRB in the coastal northern Baltic Sea. These findings are relevant for the knowledge of survival and dispersal of potential pathogens in the environment.

[Influence of species interaction on species distribution simulation and modeling methods.] / 种间作用对物种分布模拟的影响及建模方法.

The species distribution models (SDMs) simulate and predict the potential distribution of species in geographical space by quantifying the relationships between species distribution and environmental variables, and extrapolating these relationships to unknown landscape units, which makes them important tools in ecology, biogeo-graphy, and conservation biology. Current SDMs mainly take abiotic factors as prediction variables, whereas biotic factors, especially species interactions, are often ignored due to the difficulties in data quantification and modeling. Incorporating species interactions into SDMs is considered as the main challenge of SDMs. We reviewed the influence of species interactions on species distribution simulations, clarified the necessity of incorporating species interactions into SDMs, summarized four main ways to incorporate species interactions into SDMs, analyzed their strengths and limitations, and discussed the future development direction of incorporating species interactions into SDMs. The study showed that incorporating species interaction into SDMs was based on the premise that the spatial scale of species distribution simulation was consistent with that of species interactions, and that the training data should be collected from large environmental heterogeneous space to ensure the diversity of species interactions in heterogeneous habitats. In order to eliminate the influence of multicollinearity on the prediction of SDMs, all abiotic and biotic factors should be fully considered and accurately quantified. Modeling the complex population/community dynamics would be an important development direction of incorporating species interactions into SDMs.

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

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

Changes in temperature alter competitive interactions and overall structure of fig wasp communities.

Organisms exist within ecological networks, connected through interactions such as parasitism, predation and mutualism which can modify their abundance and distribution within habitat patches. Differential species responses make it hard to predict the influence of climate change at the community scale. Understanding the interplay between climate and biotic interactions can improve our predictions of how ecosystems will respond to current global warming. We aim to understand how climate affects the multitrophic biotic interactions as well as the community structure using the enclosed communities of wasps associated with figs as study system. To examine the presence and strength of multitrophic species interactions, we first characterized the multitrophic community of fig wasps associated with Ficus racemosa and then applied hierarchical joint species distribution models, fitted to community monitoring data. We further evaluated the effect of climate on individual species trends as well as interspecific interactions. We found that the competitive balance shifted to favour non-pollinating galling wasps and disadvantage the dominant pollinator in suboptimal conditions. Furthermore, suboptimal conditions for galling wasps facilitated the occurrence of their specialized parasitoid, as changes cascaded across trophic levels and led to alternative community structures. Our results highlight the role of how species interactions can be modified across multiple trophic levels in a fig wasp community according to climate.

Do different growth rates of trees cause distinct habitat qualities for saproxylic assemblages?

In production forests, a common silvicultural objective is enhancing tree growth rates. The growth rate influences both mechanical and biochemical properties of wood, which may have an impact on dead wood inhabiting (i.e. saproxylic) species. In this study, we tested for the first time whether tree growth rates affect dead-wood associated assemblages in general and the occurrence of red-listed species in particular. We sampled saproxylic beetles (eclector traps) and fungi (DNA metabarcoding of wood samples) in dead trunks of Norway spruce (Picea abies), which had different growth rates within the same hemiboreal forests in Sweden. A high proportion of fungi showed a positive association to increasing tree growth. This resulted in higher fungal richness in fast-grown trees both at the trunk scale and across multiple studied trunks. Such patterns were not observed for saproxylic beetles. However, a set of species (both beetles and fungi) preferred slow-grown wood. Moreover, the total number of red-listed species was highest in slow-grown trunks. We conclude that dead wood from slow-grown trees hosts relatively fewer saproxylic species, but a part of these may be vulnerable to production forestry. It implies that slow-grown trees should be a target in nature conservation. However, where slow-grown trees are absent, for instance in forests managed for a high biomass production, increasing the volumes of dead wood from fast-grown trees may support many species.

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.

Successional syndromes of saplings in tropical secondary forests emerge from environment-dependent trait-demography relationships.

Identifying generalisable processes that underpin population dynamics is crucial for understanding successional patterns. While longitudinal or chronosequence data are powerful tools for doing so, the traditional focus on community-level shifts in taxonomic and functional composition rather than species-level trait-demography relationships has made generalisation difficult. Using joint species distribution models, we demonstrate how three traits-photosynthetic rate, adult stature, and seed mass-moderate recruitment and sapling mortality rates of 46 woody species during secondary succession. We show that the pioneer syndrome emerges from higher photosynthetic rates, shorter adult statures and lighter seeds that facilitate exploitation of light in younger secondary forests, while 'long-lived pioneer' and 'late successional' syndromes are associated with trait values that enable species to persist in the understory or reach the upper canopy in older secondary forests. Our study highlights the context dependency of trait-demography relationships, which drive successional shifts in sapling's species composition in secondary forests.

Higher host plant specialization of root-associated endophytes than mycorrhizal fungi along an arctic elevational gradient.

How community-level specialization differs among groups of organisms, and changes along environmental gradients, is fundamental to understanding the mechanisms influencing ecological communities. In this paper, we investigate the specialization of root-associated fungi for plant species, asking whether the level of specialization varies with elevation. For this, we applied DNA barcoding based on the ITS region to root samples of five plant species equivalently sampled along an elevational gradient at a high arctic site. To assess whether the level of specialization changed with elevation and whether the observed patterns varied between mycorrhizal and endophytic fungi, we applied a joint species distribution modeling approach. Our results show that host plant specialization is not environmentally constrained in arctic root-associated fungal communities, since there was no evidence for changing specialization with elevation, even if the composition of root-associated fungal communities changed substantially. However, the level of specialization for particular plant species differed among fungal groups, root-associated endophytic fungal communities being highly specialized on particular host species, and mycorrhizal fungi showing almost no signs of specialization. Our results suggest that plant identity affects associated mycorrhizal and endophytic fungi differently, highlighting the need of considering both endophytic and mycorrhizal fungi when studying specialization in root-associated fungal communities.

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

BACKGROUND: Species distribution models are commonly used tools to describe diversity patterns and support conservation measures. There is a wide range of approaches to developing SDMs, each highlighting different characteristics of both the data and the ecology of the species or assemblages represented by the data. Yet, signals of species co-occurrences in community data are usually ignored, due to the assumption that such structuring roles of species co-occurrences are limited to small spatial scales and require experimental studies to be detected. Here, our aim is to explore associations among marine sandy-bottom sediment inhabitants and test for the structuring effect of seagrass on co-occurrences among these species across a New Zealand intertidal sandflat, using a joint species distribution model (JSDM). RESULTS: We ran a JSDM on a total of 27 macrobenthic species co-occurring in 300,000 m2 of sandflat. These species represented all major taxonomic groups, i.e. polychaetes, bivalves and crustaceans, collected in 400 sampling locations. A number of significant co-occurrences due to shared habitat preferences were present in vegetated areas, where negative and positive correlations were approximately equally common. A few species, among them the gastropods Cominella glandiformis and Notoacmea scapha, co-occurred randomly with other seagrass benthic inhabitants. Residual correlations were less apparent and mostly positive. In bare sand flats shared habitat preferences resulted in many significant co-occurrences of benthic species. Moreover, many negative and positive residual patterns between benthic species remained after accounting for habitat preferences. Some species occurring in both habitats showed similarities in their correlations, such as the polychaete Aglaophamus macroura, which shared habitat preferences with many other benthic species in both habitats, yet no residual correlations remained in either habitat. CONCLUSIONS: Firstly, analyses based on a latent variable approach to joint distributions stressed the structuring role of species co-occurrences beyond experimental scales. Secondly, results showed context dependent interactions, highlighted by species having more interconnected networks in New Zealand bare sediment sandflats than in seagrass meadows. These findings stress the critical importance of natural history to modelling, as well as incorporating ecological reality in SDMs.