Biotic Predictors Research Articles

Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important?

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus‐field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an information‐theoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co‐occurrences are a stronger driver of plant distributions than plant–bacteria co‐occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.

The Influence of Benthic Macrofauna on the Erodibility of Intertidal Sediments with Varying mud Content in Three New Zealand Estuaries

Fine sediment inputs can alter estuarine ecosystem structure and function. However, natural variations in the processes that regulate sediment transport make it difficult to predict their fate. In this study, sediments were sampled at different times (2011–2012) from 45 points across intertidal sandflat transects in three New Zealand estuaries (Whitford, Whangamata, and Kawhia) encompassing a wide range in mud (≤63 μm) content (0–56 %) and macrofaunal community structure. Using a core-based erosion measurement device (EROMES), we calculated three distinct measures of sediment erosion potential: erosion threshold (Ʈ c ; N m−2), erosion rate (ER; g m−2 s−1), and change in erosion rate with increasing bed shear stress (m e ; g N−1 s−1). Collectively, these measures characterized surface (Ʈ c and ER) and sub-surface (m e ) erosion. Benthic macrofauna were grouped by functional traits (size and motility) and data pooled across estuaries to determine relationships between abiotic (mud content, mean grain size) and biotic (benthic macrofauna, microbial biomass) variables and erosion measures. Results indicated that small bioturbating macrofauna (predominantly freely motile species <5 mm in size) destabilized surface sediments, explaining 23 % of the variation in Ʈ c (p ≤ 0.01) and 59 % of the variation in ER (p ≤ 0.01). Alternatively, mud content and mean grain size cumulatively explained 61 % of the variation in m e (p ≤ 0.01), where increasing mud and grain size stabilized sub-surface sediments. These results highlight that the importance of biotic and abiotic predictors vary with erosion stage and that functional group classifications are a useful way to determine the impact of benthic macrofauna on sediment erodibility across communities with different species composition.

Contrasting effects of biotic interactions on richness and distribution of vascular plants, bryophytes and lichens in an arctic–alpine landscape

Biotic interactions may strongly affect the distribution of individual species and the resulting patterns of species richness. However, the impacts can vary depending on the species or taxa examined, suggesting that the influences of interactions on species distributions and diversity are not always straightforward and can be taxon-contingent. The aim of this study was therefore to examine how the importance of biotic interactions varies within a community. We incorporated three biotic predictors (cover of the dominant vascular species) into two correlative species richness modelling frameworks to predict spatial variation in the number of vascular plants, bryophytes and lichens in arctic–alpine Fennoscandia, in N Europe. In addition, predictions based on single-species distribution models were used to determine the nature of the impact (negative vs. positive outcome) of the three dominant species on individual vascular plant, bryophyte and lichen species. Our results suggest that biotic variables can be as important as abiotic variables, but their relative contributions in explaining the richness of sub-dominant species vary among dominant species, species group and the modelling framework implemented. Similarly, the impacts of biotic interactions on individual species varied among the three species groups and dominant species, with the observed patterns partly reflecting species’ biogeographic range. Our study provides additional support for the importance of biotic interactions in modifying arctic–alpine biodiversity patterns and highlights that the impacts of interactions are not constant across taxa or biotic drivers. The influence of biotic interactions, including the taxon contingency and range-based impacts, should therefore be accounted for when developing biodiversity forecasts.

Integrating Nonnative Species in Niche Models to Prioritize Native Fish Restoration Activity Locations along a Desert River Corridor

AbstractThe efficient allocation of restoration resources is critical for the effective conservation of species. Here, we developed an ecological niche model to predict the response of three imperiled desert fishes to potential restoration actions along the longitudinal gradient of a desert river. The San Rafael River, Utah, is home to a complex of three endemic fishes (Flannelmouth Sucker Catostomus latipinnis, Bluehead Sucker C. discobolus, and Roundtail Chub Gila robusta; referred to as the “three species”). Like many Colorado River basin tributaries, the river is overallocated for human use, has experienced extensive physical degradation, and is now home to several nonnative fish species. To determine the factors most limiting to the three species, we first fit random forest models to fish CPUE and habitat data. We next combined these models with a longitudinal habitat survey and modeled nonnative species abundance to predict the continuous distribution of the three species in the lower San Rafael River, under current conditions and following simulated restoration. Nonnative fishes were important negative predictors of Flannelmouth Sucker and Bluehead Sucker relative abundance, and broadscale habitat variables were important positive predictors for all of the three species. Each of the three species was predicted to experience a significant increase in abundance following simulated eradication of nonnative fishes. Responses to simulated reach‐specific habitat restoration were dependent on the reach restored, demonstrating that the choice of restoration location is critical. Nonnative species removal and restoring degraded reaches near already suitable habitat were predicted to be the most beneficial to the three species. Ecological niche models developed at the river scale, and incorporating both physical and biotic predictor variables, can provide spatially explicit information that appropriately parallels the spatial distribution of the needs of endemic fishes and can facilitate effective management and conservation decisions.

Biotic and abiotic predictors of fungal colonization in grasses of the Colorado Rockies

AbstractAimFungal symbionts are ubiquitous in plants and can mitigate abiotic stressors associated with climate change. Predicting fungal symbiont distributions under future climates first requires knowledge of current distributions and their potential drivers.LocationWe documented colonization by fungal symbionts in perennial, cool‐season grasses along altitudinal gradients in the Rocky Mountains of Colorado, USA.MethodsAcross seven replicate altitudinal gradients, spanning c. 1400 vertical meters, we scored fungal colonization for 46 grass species. We documented altitudinal clines in colonization by both above‐ground and below‐ground fungal symbionts for the first time, including localized foliar endophytes (LFE) and systemic endophytes (epichloae) in leaves and arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE) in roots. For a subset of 16 well‐sampled grass species, we used model selection procedures to evaluate the relative importance of geography, edaphic factors and host plant identity. We also assessed the influence of host phylogenetic relatedness and colonization by co‐infecting fungi.ResultsLevels of fungal colonization varied strongly with host plant identity, but the effects of particular host species were not consistent across fungal groups. In addition to the influence of host identity, epichloae colonization declined with elevation and varied with geography (latitude/longitude) and edaphic factors. Geography and collection date were important predictors of LFE colonization, with higher colonization later in the growing season. Colonization estimates for the obligately plant‐associated fungi (epichloae, AMF) were phylogenetically conserved across the grass supertree. Positive correlations between AMF and DSE, which remained even after accounting for host plant relatedness, suggested possible synergisms between these fungal groups.Main conclusionsOur survey showed greater host specificity in patterns of fungal colonization than prior reports and revealed that different fungal symbiont groups do not share similar drivers. Conserving plant–fungal symbioses under future climates may require unique strategies for different plant species and fungal symbiont types.

Spatial extent of biotic interactions affects species distribution and abundance in river networks: the freshwater pearl mussel and its hosts

AbstractAimAt what spatial extent are biotic interactions discernible influences on the distribution and abundance of species in river networks? We address this question with analyses of data from river networks for Margaritifera margaritifera, a freshwater mussel that passes its larval stage attached to a host fish.LocationTwenty river networks in Galicia, north‐western Spain.MethodsA maximum‐entropy approach was implemented to model the species’ distribution. Geostatistical mixed models were used to analyse the mussel's abundance in dendritic river networks. Predictor variables included the abundance and biomass of host fish (biotic interactions) and abiotic predictors for climate, geology and land‐form.ResultsMaxent models of species distribution were improved by 4.5% in terms of the area under the receiver‐operating characteristic curve (AUC) by the inclusion of biotic interactions. Host‐fish predictors contributed 63% of the Maxent model prediction of mussel presence. A geostatistical mixed model explained 52% of the variance in mussel abundance when including all the mussel abundance sites in the study area; abiotic predictors had no significance and salmonid biomass and resident trout population density were the only significant biotic predictors, together explaining 2.4% of the variance. An autocovariate representing biotic interactions between mussels and fish explained 11.7% of the variance. Using only sites where migratory host fish were present (n = 149), a mixed model explained more variance (78%) and the contribution from the autocovariate for parasite–host interactions was about three times larger than for the model including all sites (n = 419). The spatial autocorrelation from mussel–fish interactions had a spatial extent (geostatistical range) greater than 15 km.Main conclusionsInteractions between mussels and their larval hosts in river networks are manifested in spatial patterns of species distribution and abundance in this region, encompassing 20 river networks. The directional topology of dendritic river networks strongly supports the upstream dispersal of mussels by parasitized host fish as a component of spatial autocorrelation in mussel abundance.

A role for biotic filtering in driving phylogenetic clustering in soil bacterial communities

AbstractAimPhylogenetic clustering, the coexistence of evolutionarily related organisms, appears to be common in soil bacteria. This pattern has traditionally been attributed to the habitat‐filtering of bacteria that are able to survive under particular abiotic settings. According to the modern coexistence theory, however, phylogenetic clustering can also arise from biotic interactions such as the competitive exclusion of large clades with low competitive abilities. Here, we used phylogeny‐based methods to discern whether the coexistence of evolutionarily related soil bacteria results from abiotic and/or biotic filtering.LocationWorldwide.MethodsWe performed a Bayesian meta‐analysis based on a literature review (n = 231) to assess whether the net relatedness index (NRI) or the nearest taxon index (NTI), two measures of the phylogenetic relatedness of taxa in local assemblages, deviate from those in randomly configured communities. We then sought the best abiotic (pH, total organic carbon and total nitrogen) and biotic predictors (relative abundance of Proteobacteria, Actinobacteria and Acidobacteria) of NRI and NTI.ResultsPhylogenetic clustering is pervasive in soil bacterial communities regardless of the spatial and taxonomic scales (NRI = 2.29; 95% CI [1.43, 3.29]; P &lt; 0.001). Clustering is accentuated by productivity; that is, more fertile soils hold communities with more closely related bacteria (estimate = 1.05 [0.03, 2.15]; P &lt; 0.05). Proteobacterial abundance, which increases with organic carbon enrichment, leads to higher relatedness among coexisting bacteria (estimate = 0.1 [0.02, 0.17]; P &lt; 0.01) through the competitive exclusion of distantly related deep‐branching clades.Main conclusionsOur results, together with the dominance of proteobacterial lineages in soils worldwide, suggest that the overrepresentation of this clade underlies the widespread coexistence of phylogenetically related bacteria. These results are consistent with phylogenetic clustering arising via differences in competitive ability as predicted by the coexistence theory. This supports the idea that biotic filtering might have a role in driving the phylogenetic community assembly of soil prokaryotes.

Biotic and abiotic predictors of ecosystem engineering traits of the dune building grass, Ammophila breviligulata

Ecosystem engineers are species that fundamentally influence their community and ecosystem by creating or altering the physical structure of habitats. However, the function of ecosystem engineers is variable and can depend on abiotic and biotic factors. In this study, we characterized the ecosystem engineering traits of plant size and tiller density for the dune grass, Ammophila breviligulata, in 37 sites across a broad geographic gradient in the western Great Lakes region. We also measured 20 biotic and abiotic factors related to climate, soil chemistry, and fungal symbionts associated with these survey sites and assessed their relationships with A. breviligulata population traits and dune plant species richness. Climate factors, especially temperature and precipitation, were positively associated with A. breviligulata tiller size, while soil organic matter was the only factor associated with tiller density. Several factors, including temperature, soil nitrogen, and mycorrhizal colonization, were associated with plant richness across our sites. Our results suggest that climate can influence at least some ecosystem engineering traits (i.e., plant size) of an important dune building species, although general conclusions from our work indicate that the population trait tied most closely with dune building ability—population density—is not strongly influenced by climate at the regional scale. This offers insight for conservationists interested in preserving intact dune ecosystems in a changing climate, but further work is needed to reconcile conflicting lab and field studies.

Occupancy dynamics of escaped farmed Atlantic salmon in Canadian Pacific coastal salmon streams: implications for sustained invasions

Farmed non-native Atlantic salmon (Salmo salar) is the largest agriculture export product of British Columbia, Canada. Chronic low-volume escapes of salmon from farms into Pacific waters (“leakage”) are typically undetectable (Britton et al. 2011). Analysis of escape-reporting from farmers indicates that reports greatly underestimate the true number of Atlantic salmon inadvertently released from open-net pen rearing sites (Morton and Volpe 2002). To quantify the spatial extent of escaped Atlantic salmon in Canadian Pacific rivers, we systematically snorkel-surveyed 41 known Pacific salmon (Oncorhynchus spp.)-supporting rivers and creeks on Vancouver Island over a span of 3 years. We estimated and accounted for imperfect detections using multi-season occupancy models. We detected Atlantic salmon in 36.6 % of surveyed rivers. After accounting for imperfect detection, occupancy models estimated that over half of surveyed streams across the study area contained Atlantic salmon, and that 97 % of streams with high native salmon diversity were occupied by Atlantic salmon. Even in intensive snorkel surveys, Atlantic salmon are detected in occupied streams only 2/3 the time, suggesting abundance and distribution of non-native salmon is greater than indicated by the only existing data. Further, Atlantic salmon are more likely to occupy streams with high native Pacific salmon diversity—and more likely to maintain occupancy across years—potentially increasing competitive pressure on native salmonids. Understanding local biotic and abiotic predictors of Atlantic salmon occupancy, stream colonization, and local extinction requires more data; the same is true for the effects of escaped Atlantic salmon on local salmon diversity and sustainability. These data for the first time show that Atlantic salmon occupy Pacific coastal rivers for multiple years. The impact of Atlantic salmon occupancy in British Columbia rivers must be factored into policy decisions regarding the future of salmon farming in the provincial waters.

Effects of co-occurring non-native invasive plant species on old-field succession

Old fields are diverse forb-dominated ecosystems transitioning into woody-dominated forested ecosystems. However, the susceptibility of old fields to high levels of plant invasion can lead to the co-occurrence of multiple non-native invasive plant species at the fine-scale of plant neighborhoods, which can alter native species co-occurrence patterns and successional trajectories into forest communities. Community disassembly by invasive species occurs when the presence of one or more invaders shifts co-occurrence patterns of native species from structured to random. Disassembly can imply a loss of existing co-evolved interactions among native species, which has ramifications for community dynamics and trajectories of invaded ecosystems. Here, we quantify relationships among multiple invasive plant species and two indicators of community succession in old-field plant communities in East Tennessee: co-occurrence patterns of native and non-native species and successional trajectories. First, we examine how biotic and abiotic factors shape the abundance of invasive species, as well as native and invasive functional groups across old fields. Second, we ask whether invasive species influence co-occurrence patterns among native species and whether invasive species are associated with altered herbaceous:woody foliar cover ratios. We found that biotic and abiotic predictors associated with invasive species were not consistent in identity or direction of association, indicating that predicting which sites or suite of biotic and abiotic variables are associated with invasive species will be challenging. Importantly, as the number of invasive plants increased in 1-m2 plots, native species co-occurrence patterns shifted from structured to random, whereas invasive species co-occurrence patterns remained random irrespective of the number of invasive species. Plots containing three or more invaders had significant changes in native and invasive herbaceous:woody foliar cover ratios. The herbaceous:woody foliar cover ratio of native species was 4× lower and of invasive species was 2× greater compared to plots with one or two invaders, and this shift was in part explained by an increase in foliar cover of non-native woody species. Our data suggest that increased number of invasive species in old fields alters both native species interactions and the trajectories of old-field communities, which could influence the developing understory community as old fields transition into forests. We recommend that management of fields during succession should focus on decreasing the total number of invasive species to restore species co-occurrence patterns and prevent altered successional trajectories, including accelerated succession of non-native woody species.

The Influence of Environmental, Biotic and Spatial Factors on Diatom Metacommunity Structure in Swedish Headwater Streams

Stream assemblages are structured by a combination of local (environmental filtering and biotic interactions) and regional factors (e.g., dispersal related processes). The relative importance of environmental and spatial (i.e., regional) factors structuring stream assemblages has been frequently assessed in previous large-scale studies, but biotic predictors (potentially reflecting local biotic interactions) have rarely been included. Diatoms may be useful for studying the effect of trophic interactions on community structure since: (1) a majority of experimental studies shows significant grazing effects on diatom species composition, and (2) assemblages can be divided into guilds that have different susceptibility to grazing. We used a dataset from boreal headwater streams in south-central Sweden (covering a spatial extent of ∼14000 km2), which included information about diatom taxonomic composition, abundance of invertebrate grazers (biotic factor), environmental (physicochemical) and spatial factors (obtained through spatial eigenfunction analyses). We assessed the relative importance of environmental, biotic, and spatial factors structuring diatom assemblages, and performed separate analyses on different diatom guilds. Our results showed that the diatom assemblages were mainly structured by environmental factors. However, unique spatial and biological gradients, specific to different guilds and unrelated to each other, were also evident. We conclude that biological predictors, in combination with environmental and spatial variables, can reveal a more complete picture of the local vs. regional control of species assemblages in lotic environments. Biotic factors should therefore not be overlooked in applied research since they can capture additional local control and therefore increase accuracy and performance of predictive models. The inclusion of biotic predictors did, however, not significantly influence the unique fraction explained by spatial factors, which suggests low bias in previous assessments of unique regional control of stream assemblages.

Implementing and interpreting local‐scale invasive species distribution models

AbstractAimUse of local‐scale non‐native plant species (NNS) distribution models has the potential to decrease survey effort and improve population prioritization for management. We developed and evaluated data collection methods and minimum sampling requirements to inform local‐scale models of NNS distribution. We also evaluated overall model predictive performance for 16 species at two sites and determined how classes of variables contributed to model performance and suggest invasion drivers.LocationWyoming and Idaho, USAMethodsA simulation study was used to test the efficiency of different sampling methods to predict imposed species distributions. Empirical distribution models of species occurrence data from two environmentally disparate sites were cross‐validated at increasing sample sizes, and the asymptotic maximum predictive performance and relative contribution of classes of variables were determined for 16 NNS.ResultsTransect sampling was the most efficient method for maximizing model performance after accounting for logistics. Minimum sample sizes to reach model maximum predictive performance were similar for the simulation (&lt; 0.5% of study area) and empirical studies (mean of 0.13% using transects). Maximum predictive performance tended to be greater at the site with steeper environmental gradients, and topo‐climatic/biotic variables were most important to model improvement.Main conclusionsLocal‐scale SDMs can be useful to NNS managers. Using transect methodology, enough data can be collected (ca. 0.13% of the management area) to fit models within logistical/budgetary constraints. These models are most predictive for well‐established species as opposed to new invaders and in areas with steeper environmental gradients. Finally, topo‐climatic/biotic predictors are the most important variables for predicting more established species, but disturbance and dispersal limitation should be considered and quantified to ensure variables associated with dominant processes are included in the SDM.

Biodiversity tracks temperature over time

The geographic distribution of life on Earth supports a general pattern of increase in biodiversity with increasing temperature. However, some previous analyses of the 540-million-year Phanerozoic fossil record found a contrary relationship, with paleodiversity declining when the planet warms. These contradictory findings are hard to reconcile theoretically. We analyze marine invertebrate biodiversity patterns for the Phanerozoic Eon while controlling for sampling effort. This control appears to reverse the temporal association between temperature and biodiversity, such that taxonomic richness increases, not decreases, with temperature. Increasing temperatures also predict extinction and origination rates, alongside other abiotic and biotic predictor variables. These results undermine previous reports of a negative biodiversity-temperature relationship through time, which we attribute to paleontological sampling biases. Our findings suggest a convergence of global scale macroevolutionary and macroecological patterns for the biodiversity-temperature relationship.

Factors driving growth responses to drought in Mediterranean forests

We lack information regarding the main factors driving growth responses to drought in tree species with different vulnerability against this stressor and considering sites with contrasting climatic conditions. In this paper, we identify the main drivers controlling growth response to a multi-scalar drought index (Standardized Precipitation Index, SPI) in eight tree species (Abies alba, Pinus halepensis, Quercus faginea, Pinus sylvestris, Quercus ilex, Pinus pinea, Pinus nigra, Juniperus thurifera). We sampled forests growing across a pronounced climatic gradient under Mediterranean conditions in north-eastern Spain. To summarize the patterns of growth responses to drought, we used principal component analysis (PCA). To determine the main factors affecting growth responses to drought, correlation and regression analyses were carried out using a set of abiotic (climate, topography, soil type) and biotic (Normalized Difference Vegetation Index, Enhanced Vegetation Index, tree-ring width, diameter at breast height) predictors and the PCs loadings as response variables. The PCA analysis detected two patterns of growth responses to drought corresponding to xeric and mesic sites, respectively. The regression analyses indicated that growth responses to drought in xeric forests were mainly driven by the annual precipitation, while in mesic sites the annual water balance was the most important driver. The management of Mediterranean forests under the forecasted warmer and drier conditions should focus on the main local factors modulating the negative impacts of drought on tree growth in xeric and mesic sites.

Use of ciliate and phytoplankton taxonomic composition for the estimation of eicosapentaenoic acid concentration in lakes

Summary1. The polyunsaturated fatty acid eicosapentaenoic acid (EPA) plays an important role in aquatic food webs, in particular at the primary producer–consumer interface where keystone species such as daphnids may be constrained by its dietary availability. Such constraints and their seasonal and interannual changes may be detected by continuous measurements of EPA concentrations. However, such EPA measurements became common only during the last two decades, whereas long‐term data sets on plankton biomass are available for many well‐studied lakes. Here, we test whether it is possible to estimate EPA concentrations from abiotic variables (light and temperature) and the biomass of prey organisms (e.g. ciliates, diatoms and cryptophytes) that potentially provide EPA for consumers.2. We used multiple linear regression to relate size‐ and taxonomically resolved plankton biomass data and measurements of temperature and light intensity to directly measured EPA concentrations in Lake Constance during a whole year. First, we tested the predictability of EPA concentrations from the biomass of EPA‐rich organisms (diatoms, cryptophytes and ciliates). Secondly, we included the variables mean temperature and mean light intensity over the sampling depth (0–20 m) and depth (0–8 and 8–20 m) as factors in our model to check for large‐scale seasonal‐ and depth‐dependent effects on EPA concentrations. In a third step, we included the deviations of light and temperature from mean values in our model to allow for their potential influence on the biochemical composition of plankton organisms. We used the Akaike Information Criterion to determine the best models.3. All approaches supported our proposition that the biomasses of specific plankton groups are variables from which seston EPA concentrations can be derived. The importance of ciliates as an EPA source in the seston was emphasised by their high weight in our models, although ciliates are neglected in most studies that link fatty acids to seston taxonomic composition. The large‐scale seasonal variability of light intensity and its interaction with diatom biomass were significant predictors of EPA concentrations. The deviation of temperature from mean values, accounting for a depth‐dependent effect on EPA concentrations, and its interaction with ciliate biomass were also variables with high predictive power.4. The best models from the first and second approaches were validated with measurements of EPA concentrations from another year (1997). The estimation with the best model including only biomass explained 80%, and the best model from the second approach including mean temperature and depth explained 87% of the variability in EPA concentrations in 1997.5. We show that it is possible to predict EPA concentrations reliably from plankton biomass, while the inclusion of abiotic factors led to results that were only partly consistent with expectations from laboratory studies. Our approach of including biotic predictors should be transferable to other systems and allow checking for biochemical constraints on primary consumers.

Can we disentangle predator–prey interactions from species distributions at a macro‐scale? A case study with a raptor species

There is a debate on whether an influence of biotic interactions on species distributions can be reflected at macro‐scale levels. Whereas the influence of biotic interactions on spatial arrangements is beginning to be studied at local scales, similar studies at macro‐scale levels are scarce. There is no example disentangling, from other similarities with related species, the influence of predator–prey interactions on species distributions at macro‐scale levels. In this study we aimed to disentangle predator–prey interactions from species distribution data following an experimental approach including a factorial design. As a case of study we selected the short‐toed eagle because of its known specialization on certain prey reptiles. We used presence–absence data at a 100 km2 spatial resolution to extract the explanatory capacity of different environmental predictors (five abiotic and two biotic predictors) on the short‐toed eagle species distribution in peninsular Spain. Abiotic predictors were relevant climatic and topographic variables, and relevant biotic predictors were prey richness and forest density. In addition to the short‐toed eagle, we also obtained the predictor's explanatory capacities for 1) species of the same family Accipitridae (as a reference), 2) for other birds of different families (as controls) and 3) artificial species with randomly selected presences (as null models). We run 650 models to test for similarities of the short‐toed eagle, controls and null models with reference species, assessed by regressions of explanatory capacities. We found higher similarities between the short‐toed eagle and other species of the family Accipitridae than for the other two groups. Once corrected by the family effect, our analyses revealed a signal of predator–prey interaction embedded in species distribution data. This result was corroborated with additional analyses testing for differences in the concordance between the distributions of different bird categories and the distributions of either prey or non‐prey species of the short‐toed eagle. Our analyses were useful to disentangle a signal of predator–prey interactions from species distribution data at a macro‐scale. This study highlights the importance of disentangling specific features from the variation shared with a given taxonomic level.

Biotic interactions affect the elevational ranges of high‐latitude plant species

Ecological theory suggests that positive plant–plant interactions can extend species distributions into areas that would otherwise be unfavourable. However, few studies have tested this hypothesis, and none have explicitly examined the associated prediction that inter‐specific interactions between plants may broaden species altitudinal distributions. Here we test this prediction, using fine‐scale species distribution data for 156 bryophytes, lichens and vascular plants spanning a 900 m elevational gradient in north‐western Finland and Norway, analysed with a niche modelling approach. Species altitudinal ranges of all three groups of plants were more accurately predicted when including the cover of any of the 24 most wide‐spread and abundant species (‘dominants’) than when using abiotic variables alone, emphasizing the importance of including relevant biotic predictors in species distribution models. Half of the models showed that species had very low probabilities of occurrence under high cover of dominants, suggesting a strong negative impact of dominant species. Similarly, for species that are predicted to occur irrespective of dominant species cover, 62% of models showed narrower species altitudinal distributions when occurring under high dominant cover, with contractions of species’ lower and upper elevational limits being common. Nonetheless, high cover of dominant species was associated with upslope range extension in 43 species, and a net range expansion in nearly 10% of all models. Species distributional responses to dominants were only weakly related to species traits, with larger range contractions associated with arctic‐alpine dominants. Therefore, dominant species appear to exert a strong influence on the elevational distribution of other species in high latitude environments.

Modeling alpine plant distributions at the landscape scale: Do biotic interactions matter?

Species Distribution Models (SDMs) generally ignore biotic processes. However, it has been shown that biotic interactions from lowland flora contribute to shape the “rear edge” of alpine plant distributions. In this study, we explored the potential effect of accounting for interactions from dominant lowland congeners representative for the lowland flora for predicting landscape scale distribution (1km grain) of two alpine plant species, Viola biflora and Veronica alpina.In a first classical approach, we include the lowland species probabilities of occurrence as covariates in the alpine species landscape scale models (covariate models). In a second novel approach, we first used SDMs to predict the distribution of the two alpine plants at the landscape scale. We then searched for interactive effects with the lowland species, and used this information to re-predict the landscape parts where alpine and lowland species were previously predicted to co-occur (abiotic+biotic models).Our ‘abiotic+biotic’ model improved model precision for both alpine species; but statistically significantly for Viola biflora only. In contrast, the classical covariate approach did not affect the prediction accuracy of Viola biflora and decreased the prediction accuracy for Veronica alpina. This seemed to be caused by collinearity between abiotic and biotic predictors, highlighting potential problems with the conventional method used to account for biotic interactions in SDM.Including potential effects of biotic interactions can improve predictions of alpine species’ ranges at the landscape scale. Ignoring biotic interactions in SDM may lead to biased predictions that are likely to overestimate realized climatic niches and so species distributions. The abiotic+biotic approach can constitute a robust method to account for biotic interactions in SDM.

Dominant species and dispersal limitation regulate tree species distributions in a 20‐ha plot in Xishuangbanna, southwest China

Habitat heterogeneity and dispersal limitation are widely considered to be the two major mechanisms in determining tree species distributions. However, few studies have quantified the relative importance of these two mechanisms at different life stages of trees. Moreover, rigorous quantification of the effects of dominant tree species in determining species distributions has seldom been explored. In the present study, we tested the hypothesis that the distribution of tree species is regulated by different mechanisms at different life history stages. In particular, we hypothesised that dispersal limitation regulates the distribution of trees at early life stages and that environmental factors control the distribution of trees as they grow, because of niche differentiation resulting from environmental filtering. To test this, trees in 400‐m2 quadrats in a 20‐ha plot in Xishuangbanna, southwest China were grouped into four classes on the basis of the diameter at breast height (DBH) that roughly represent different stages in the life history of trees. A neighbourhood index was computed to represent a neutral spatial autocorrelation effect. We used both biotic (dominant species) and abiotic (topography and soil) predictor variables to model the distribution of each target species while controlling for spatial autocorrelation within each of the DBH classes. To determine which factors played the largest role in regulating target species distribution, the simulated annealing method was used in model selection based on Akaike information criterion (AIC) values. The results showed that the relative importance of neutral and niche processes in regulating species distribution varied across life stages. The neutral neighbourhood index played the most important role in determining the distributions of small trees (1 cm ≤ DBH ≤ 10 cm), and dominant species, as biotic environmental predictor variables, were the next most important regulators for trees of this size. Environmental predictor variables played the most important role in determining the distributions of large trees (10 cm ≤ DBH). This finding builds on previous research into the relative importance of neutral and niche processes in determining species distributions regardless of life stages or DBH classes.

Topographic and biotic regulation of aboveground carbon storage in subtropical broad-leaved forests of Taiwan

There is a growing need to understand, and ultimately manage, carbon storage by forest ecosystems. Broad-leaved evergreen forests of Taiwan provide an outstanding opportunity to examine factors that regulate ecosystem carbon storage. We utilized data from three Taiwan Forest Dynamics Plots (FS, LHC, and PTY) in which every tree is identified, measured, tagged and mapped, to examine factors regulating carbon storage as estimated from aboveground biomass. Allometric equations were used to estimate the aboveground biomass of each tree, and a model building procedure was used to examine relationships between plot-level aboveground biomass (AGB; Mg/ha) and a suite of topographic and biotic factors. We found that our study sites have AGB values comparable to some of the most carbon dense forests in the world. Across all three sites, maximum biomass was contained in the taxonomic families Fagaceae, Lauraceae and Theaceae. In the FS site, we identified slope convexity ( P = 0.03) and elevation ( P < 0.001) as topographic predictors of AGB and found that maximum AGB was found in topographically flat areas. In FS, stem density ( P < 0.001) was a significant biotic predictor of AGB and the maxima occurred at intermediate densities. In LHC, we found that convexity ( P < 0.001) and slope ( P < 0.001) were significantly related to AGB which was maximized along a topographic ridge in the plot. Species richness ( P < 0.001) was a significant biotic predictor of AGB in LHC, and the relationship indicated slightly higher AGB at higher levels of species richness. The only significant factor related to AGB in PTY was species richness ( P = 0.02). Further work is needed to seek a mechanistic understanding of topographic factors and species richness as drivers of carbon storage in forests.