Reevaluation of the biological status of Juniperus Deppeana var. sperryi Correll

Foliage and bark samples were collected from the tree that provided the type specimen forJuniperus deppeana var.sperryi Correll, as well as from trees from populations ofJ. pinchotii Sudw.,J. flacida Schl., andJ. deppeana Steud. var.deppeana. These four taxa were compared using terpenoid and morphological characters. The terpenoid data suggest thatJ. deppeana var.sperryi is most closely related toJ. deppeana var.deppeana; no evidence of relict or present hybridization withJ. flaccida was detected. The morphological data showedJ. deppeana var.sperryi to be intermediate in several characters betweenJ. deppeana var.deppeana andJ. flacdda. The probability of a hybrid origin for this taxon is discussed. Due to the scattered occurrence of trees referable toJ. deppeana var.sperryi, it is proposed that this taxon be reduced in rank toJ. deppeana formasperryi.     

No‐analog climates and shifting realized niches during the late quaternary: implications for 21st‐century predictions by species distribution models

Empirically derived species distributions models (SDMs) are increasingly relied upon to forecast species vulnerabilities to future climate change. However, many of the assumptions of SDMs may be violated when they are used to project species distributions across significant climate change events. In particular, SDM's in theory assume stable fundamental niches, but in practice, they assume stable realized niches. The assumption of a fixed realized niche relative to climate variables remains unlikely for various reasons, particularly if novel future climates open up currently unavailable portions of species’ fundamental niches. To demonstrate this effect, we compare the climate distributions for fossil‐pollen data from 21 to 15 ka bp (relying on paleoclimate simulations) when communities and climates with no modern analog were common across North America to observed modern pollen assemblages. We test how well SDMs are able to project 20th century pollen‐based taxon distributions with models calibrated using data from 21 to 15 ka. We find that taxa which were abundant in areas with no‐analog late glacial climates, such as Fraxinus, Ostrya/Carpinus and Ulmus, substantially shifted their realized niches from the late glacial period to present. SDMs for these taxa had low predictive accuracy when projected to modern climates despite demonstrating high predictive accuracy for late glacial pollen distributions. For other taxa, e.g. Quercus, Picea, Pinus strobus, had relatively stable realized niches and models for these taxa tended to have higher predictive accuracy when projected to present. Our findings reinforce the point that a realized niche at any one time often represents only a subset of the climate conditions in which a taxon can persist. Projections from SDMs into future climate conditions that are based solely on contemporary realized distributions are potentially misleading for assessing the vulnerability of species to future climate change.     

Predicting potential impacts of climate change on freshwater fish in Korea

Climate change is expected to have profound effects on the distribution and phenology of species and the productivity of aquatic ecosystem. In this study, we projected the impacts of climate change on the distributions of 22 endemic fish species in Korea with climatic and geographical variables by using species distribution models (SDMs). Six different SDMs – linear discriminant analysis, generalized linear model, classification and regression trees, random forest, support vector machine, and multivariate adaptive regression splines – were implemented for the prediction, and compared for their prediction capacity. The results showed that the random forest displayed the highest predictive power for the prediction of current species distributions. Therefore, the random forest was used to assess the potential impacts of climate change on the distributions of 22 endemic fish species. The results revealed that five species (Acheilognathus yamatsutae, Sarcocheilichthys variegatus wakiyae, Squalidus japonicus coreanus, Microphysogobio longidorsalis, and Liobagrus andersoni) have a high probability of becoming extinct in their respective habitable sub-watersheds by the 2080s due to climate change. The sensitivity analysis of the model showed that geo-hydrological variables such as stream order and altitude and temperature-related variables such as mean temperature in January and difference between the minimum and maximum temperatures exhibited relatively higher importance in their contributions for the prediction of species occurrence than that other variables. The decline of endemic fish species richness, and their occurrence probability due to climate change, would lead to poleward and upward shifts, as well as extinctions of species. Finally, we believe that our projections are useful for understanding how climate change affects the distribution range of endemic species in Korea, while also providing the necessary information to develop preservation and conservation strategies for maintaining endemic fish.     

Potential Distribution of Fossil Xenarthrans in South America during the Late Pleistocene: co-Occurrence and Provincialism

Species distribution models (SDMs) are helpful for understanding actual and potential biogeographical traits of organisms. These models have recently started to be applied in the study of fossil xenarthrans. SDMs were generated for 15 South American late Pleistocene xenarthrans: eight Cingulata (Glyptodon clavipes, Doedicurus clavicaudatus, Panochthus tuberculatus, Neosclerocalyptus paskoensis, Pampatherium typum, Pampatherium humboldtii, Holmesina paulacoutoi, and Holmesina occidentalis) and seven Folivora (Glossotherium robustum, Lestodon armatus, Mylodon darwinii, Catonyx cuvieri, Catonyx (=Scelidodon) chilensis, Megatherium americanum, and Eremotherium laurillardi). Models were evaluated for three periods: the last interglacial (LIG), the last glacial maximum (LGM), and the Holocene climatic optimum (HCO). Co-occurrence records were studied based on the overlap of the potential distributions and compared with the available biome reconstructions of South America during the LGM to analyze species distribution patterns, ecological requirements, and possible interactions. Our results suggest the existence of provincialization within xenarthran megamammals grouped in at least three bioregions. Northern and southwestern taxa overlap in the Río de la Plata region where also some endemic taxa are found. We observed overlapping potential distributions but separated and continuous realized distributions between closely related xenarthrans suggesting competitive exclusion. A generalized reduction in potential habitats at the end of the Pleistocene was not obvious as some taxa show stable potential areas during HCO when comparing with LGM. Nonetheless, fragmentation of the most suitable areas due to climate variation and the impact of reduction in available land due to sea level changes cannot be ruled out as involved in the extinction.     

Modelling current and future potential distributions of two desert jerboas under climate change in Iran

Species distribution models (SDMs) are excellent tools to understand the factors that affect the potential distribution of several organisms at different scale. In this study, we analyzed the current potential distribution of the Blanford's Jerboa Jaculus blanfordi and the Arabian Jerboa Jaculus loftusi (Mammalia: Rodentia) in Iran and predicted the impact of climate change on their future potential distributions using two different modelling software packages: Maxent and sdm. Our results showed that precipitation was the most important variable affecting the potential distributions of J. blanfordi and J. loftusi in Iran. We also showed that the potential distributions of the two jerboas species are unlikely to be affected by climate change. All our models showed high levels of predictive performances. Thus, SDMs are a promising tool to complement data from laboratory and field studies to illuminate the biology and ecology of jerboa and inform management decisions.     

Static species distribution models in dynamically changing systems: how good can predictions really be?

It is widely acknowledged that species respond to climate change by range shifts. Robust predictions of such changes in species’ distributions are pivotal for conservation planning and policy making, and are thus major challenges in ecological research. Statistical species distribution models (SDMs) have been widely applied in this context, though they remain subject to criticism as they implicitly assume equilibrium, and incorporate neither dispersal, demographic processes nor biotic interactions explicitly. In this study, the effects of transient dynamics and ecological properties and processes on the prediction accuracy of SDMs for climate change projections were tested. A spatially explicit multi‐species dynamic population model was built, incorporating species‐specific and interspecific ecological processes, environmental stochasticity and climate change. Species distributions were sampled in different scenarios, and SDMs were estimated by applying generalised linear models (GLMs) and boosted regression trees (BRTs). Resulting model performances were related to prevailing ecological processes and temporal dynamics. SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far‐dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short‐dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.     

Evaluating the ecological realism of plant species distribution models with ecological indicator values

Species distribution models (SDMs) are routinely applied to assess current as well as future species distributions, for example to assess impacts of future environmental change on biodiversity or to underpin conservation planning. It has been repeatedly emphasized that SDMs should be evaluated based not only on their goodness of fit to the data, but also on the realism of the modeled ecological responses. However, possibilities for the latter are hampered by limited knowledge on the true responses as well as a lack of quantitative evaluation methods. Here we compared modeled niche optima obtained from European-scale SDMs of 1476 terrestrial vascular plant species with empirical ecological indicator values indicating the preferences of plant species for key environmental conditions. For each plant species we first fitted an ensemble SDM including three modeling techniques (GLM, GAM and BRT) and extracted niche optima for climate, soil, land use and nitrogen deposition variables with a large explanatory power for the occurrence of that species. We then compared these SDM-derived niche optima with the ecological indicator values by means of bivariate correlation analysis. We found weak to moderate correlations in the expected direction between the SDM-derived niche optima and ecological indicator values. The strongest correlation occurred between the modeled optima for growing degree days and the ecological indicator values for temperature. Correlations were weaker for SDM-derived niche optima with a more distal relationship to ecological indicator values (notably precipitation and soil moisture). Further, correlations were consistently highest for BRT, followed by GLM and GAM. Our method gives insight into the ecological realism of modeled niche optima and projected core habitats and can be used to improve SDMs by making a more informed selection of environmental variables and modeling techniques.     

The Predictive Performance and Stability of Six Species Distribution Models

Background

Predicting species’ potential geographical range by species distribution models (SDMs) is central to understand their ecological requirements. However, the effects of using different modeling techniques need further investigation. In order to improve the prediction effect, we need to assess the predictive performance and stability of different SDMs.

Methodology

We collected the distribution data of five common tree species (Pinus massoniana, Betula platyphylla, Quercus wutaishanica, Quercus mongolica and Quercus variabilis) and simulated their potential distribution area using 13 environmental variables and six widely used SDMs: BIOCLIM, DOMAIN, MAHAL, RF, MAXENT, and SVM. Each model run was repeated 100 times (trials). We compared the predictive performance by testing the consistency between observations and simulated distributions and assessed the stability by the standard deviation, coefficient of variation, and the 99% confidence interval of Kappa and AUC values.

Results

The mean values of AUC and Kappa from MAHAL, RF, MAXENT, and SVM trials were similar and significantly higher than those from BIOCLIM and DOMAIN trials (p<0.05), while the associated standard deviations and coefficients of variation were larger for BIOCLIM and DOMAIN trials (p<0.05), and the 99% confidence intervals for AUC and Kappa values were narrower for MAHAL, RF, MAXENT, and SVM. Compared to BIOCLIM and DOMAIN, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).

Conclusions

According to the prediction performance and stability of SDMs, we can divide these six SDMs into two categories: a high performance and stability group including MAHAL, RF, MAXENT, and SVM, and a low performance and stability group consisting of BIOCLIM, and DOMAIN. We highlight that choosing appropriate SDMs to address a specific problem is an important part of the modeling process.     

Effectiveness of land use and disturbance measures,compared to climate,soil, and topography,for modeling relative abundances of tree species in the eastern United States

Environmental factors control species distributions and abundances, but effectiveness of land use and disturbance variables for modeling species generally is unknown compared to climate, soil, and topography variables. Therefore, I used predictor variables from categories of 1) land use and disturbance, 2) climate, and 3) soil, topography, and wind speed to model the relative abundances (i.e., percentage of all trees) of 65 common tree species in the eastern United States, with a contrast to presence-absence models of species distributions. First, I modeled variables within each category to identify the five most important variables. Then, I combined variables from each category to isolate most important variables, based on five model combinations of input variables from each category, ranging from one (i.e., three total) to five (i.e., 15 total) variables. From the five models of combined categories for each tree species, I identified the model with the greatest R² value. Overall, climate variables were most important for tree species models with one and two input variables from each category, but land use and disturbance variables were most important for models with three to five input variables from each category. Although a range of R² values occurred by species and number of input model variables, 32 species had best models with greatest R² values of 0.50 to 0.81. For all best species models, the most important variables were temperature of the warmest quarter, historical fire return interval for all fires, agricultural area during years 1850 to 1997, and precipitation of the driest month. Current land cover classes, which are accessible and the most commonly modeled land use variables, were not important for modeling tree species abundances or distributions. Climate variables were most important for modeling species distributions. Results support the concept that while climate sets soft boundaries on distributions, relative abundances within distributions are affected by other filters. Future modeling may establish other important land use and disturbance variables, or refinements within the important variables of historical fire return interval and agricultural area over time, advancing integration of both land use and climate variables into studies.     

Influences of ecology and biogeography on shaping the distributions of cryptic species: three bat tales in Iberia

To determine what shapes the distributions of cryptic species, we aimed to unravel ecological niches and geographical distributions of three cryptic bat species complexes in Iberia, Plecotus auritus/begognae, Myotis mystacinus/alcathoe and Eptesicus serotinus/isabellinus (with 44, 69, 66, 27, 121 and 216 records, respectively), considering ecological interactions and biogeographical patterns. Species distribution models (SDMs) were built using a presence‐only technique (Maxent), incorporating genetically identified species records with environmental variables (climate, habitat, topography). The most relevant variables for each species’ distribution and respective response curves were then determined. SDMs for each species were overlapped to assess the contact zones within each complex. Niche analyses were performed using niche metrics and spatial principal component analyses to study niche overlap and breadth. The Plecotus complex showed a parapatric distribution, although having similar biogeographical affinities (Eurosiberian), possibly explained by competitive exclusion. The Myotis complex also showed Eurosiberian affinities, with high overlap between niches and distribution, suggesting resource partitioning between species. Finally, E. serotinus was associated with Eurosiberian areas, while E. isabellinus occurred in Mediterranean areas, suggesting possible competition in their restricted contact zone. This study highlights the relevance of considering potential ecological interactions between similarly ecological species when assessing species distributions. © 2014 The Linnean Society of London, Biological Journal of the Linnean Society, 2014, 112 ,150–162.     

Unraveling climate influences on the distribution of the parapatric newts <Emphasis Type="Italic">Lissotriton vulgaris meridionalis</Emphasis> and <Emphasis Type="Italic">L. italicus</Emphasis>

Background

Climate is often considered as a key ecological factor limiting the capability of expansion of most species and the extent of suitable habitats. In this contribution, we implement Species Distribution Models (SDMs) to study two parapatric amphibians, Lissotriton vulgaris meridionalis and L. italicus, investigating if and how climate has influenced their present and past (Last Glacial Maximum and Holocene) distributions. A database of 901 GPS presence records was generated for the two newts. SDMs were built through Boosted Regression Trees and Maxent, using the Worldclim bioclimatic variables as predictors.

Results

Precipitation-linked variables and the temperature annual range strongly influence the current occurrence patterns of the two Lissotriton species analyzed. The two newts show opposite responses to the most contributing variables, such as BIO7 (temperature annual range), BIO12 (annual precipitation), BIO17 (precipitation of the driest quarter) and BIO19 (precipitation of the coldest quarter). The hypothesis of climate influencing the distributions of these species is also supported by the fact that the co-occurrences within the sympatric area fall in localities characterized by intermediate values of these predictors. Projections to the Last Glacial Maximum and Holocene scenarios provided a coherent representation of climate influences on the past distributions of the target species. Computation of pairwise variables interactions and the discriminant analysis allowed a deeper interpretation of SDMs’ outputs. Further, we propose a multivariate environmental dissimilarity index (MEDI), derived through a transformation of the multivariate environmental similarity surface (MESS), to deal with extrapolation-linked uncertainties in model projections to past climate. Finally, the niche equivalency and niche similarity tests confirmed the link between SDMs outputs and actual differences in the ecological niches of the two species.

Conclusions

The different responses of the two species to climatic factors have significantly contributed to shape their current distribution, through contractions, expansions and shifts over time, allowing to maintain two wide allopatric areas with an area of sympatry in Central Italy. Moreover, our SDMs hindcasting shows many concordances with previous phylogeographic studies carried out on the same species, thus corroborating the scenarios of potential distribution during the Last Glacial Maximum and the Holocene emerging from the models obtained.

     

Incorporating spatial autocorrelation into species distribution models alters forecasts of climate‐mediated range shifts

Species distribution models (SDMs) are widely used to forecast changes in the spatial distributions of species and communities in response to climate change. However, spatial autocorrelation (SA) is rarely accounted for in these models, despite its ubiquity in broad‐scale ecological data. While spatial autocorrelation in model residuals is known to result in biased parameter estimates and the inflation of type I errors, the influence of unmodeled SA on species' range forecasts is poorly understood. Here we quantify how accounting for SA in SDMs influences the magnitude of range shift forecasts produced by SDMs for multiple climate change scenarios. SDMs were fitted to simulated data with a known autocorrelation structure, and to field observations of three mangrove communities from northern Australia displaying strong spatial autocorrelation. Three modeling approaches were implemented: environment‐only models (most frequently applied in species' range forecasts), and two approaches that incorporate SA; autologistic models and residuals autocovariate (RAC) models. Differences in forecasts among modeling approaches and climate scenarios were quantified. While all model predictions at the current time closely matched that of the actual current distribution of the mangrove communities, under the climate change scenarios environment‐only models forecast substantially greater range shifts than models incorporating SA. Furthermore, the magnitude of these differences intensified with increasing increments of climate change across the scenarios. When models do not account for SA, forecasts of species' range shifts indicate more extreme impacts of climate change, compared to models that explicitly account for SA. Therefore, where biological or population processes induce substantial autocorrelation in the distribution of organisms, and this is not modeled, model predictions will be inaccurate. These results have global importance for conservation efforts as inaccurate forecasts lead to ineffective prioritization of conservation activities and potentially to avoidable species extinctions.     

Host plant distributions and climate interact to affect the predicted geographic distribution of a Neotropical termite

Species distribution models (SDMs) have been widely used in the scientific literature. The majority of SDMs use climate data or other abiotic variables to forecast the potential distribution of a species in geographic space. Biotic interactions can affect the predicted spatial distribution of a species in many ways across multiple spatial scales, and incorporating these predictors in an SDM is a current topic in the scientific literature. Constrictotermes cyphergaster is a widely distributed termite in the Neotropics. This termite species nests in plants and more frequently nests in some arboreal species. Thus, this species is an excellent model to evaluate the influence of biotic interactions in SDMs. We evaluate the influences of climate and the geographic distribution of host plants on the potential distribution of C. cyphergaster. Three correlative models (MaxEnt) were built to predict the geographic distribution of the termite: (1) climate data, (2) biotic data (i.e., the geographic distribution of host plants), and (3) climate and biotic data. The models that were generated indicate that the potential geographic distribution of C. cyphergaster is concentrated in the Cerrado and Caatinga regions. In addition, path analysis and multiple regression revealed the importance of the direct effects of biological interactions in the geographic distribution of the termite, while climate affected the distribution of the termite mainly through indirect effects by influencing the geographic distributions of host plants. The current study endorses the importance of including biological interactions in SDMs. We recommend using biotic predictors in SDM studies of insect species, mainly because insects have important environmental services and biotic interaction data can improve the macroecological studies of this group.     

Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species

Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

Simulating climate change impacts on forests and associated vascular epiphytes in a subtropical island of East Asia

Aim This study aims to assess the impact of climate change on forests and vascular epiphytes, using species distribution models (SDMs). Location Island of Taiwan, subtropical East Asia. Methods A hierarchical modelling approach incorporating forest migration velocity and forest type–epiphyte interactions with classical SDMs was used to model the responses of eight forest types and 237 vascular epiphytes for the year 2100 under two climate change scenarios. Forest distributions were modelled and modified by dominant tree species’ dispersal limitations and hypothesized persistence under unfavourable climate conditions (20 years for broad‐leaved trees and 50 years for conifers). The modelled forest projections together with 16 environmental variables were used as predictors in models of epiphyte distributions. A null method was applied to validate the significance of epiphyte SDMs, and potential vulnerable species were identified by calculating range turnover rates. Results For the year 2100, the model predicted a reduction in the range of most forest types, especially for Picea and cypress forests, which shifted to altitudes c. 400 and 300 m higher, respectively. The models indicated that epiphyte distributions are highly correlated with forest types, and the majority (77–78%) of epiphyte species were also projected to lose 45–58% of their current range, shifting on average to altitudes c. 400 m higher than currently. Range turnover rates suggested that insensitive epiphytes were generally lowland or widespread species, whereas sensitive species were more geographically restricted, showing a higher correlation with temperature‐related factors in their distributions. Main conclusions The hierarchical modelling approach successfully produced interpretable results, suggesting the importance of considering biotic interactions and the inclusion of terrain‐related factors when developing SDMs for dependant species at a local scale. Long‐term monitoring of potentially vulnerable sites is advised, especially of those sites that fall outside current conservation reserves where additional human disturbance is likely to exacerbate the effect of climate change.     

Potential distributional shifts in North America of allelopathic invasive plant species under climate change models

Predictive studies play a crucial role in the study of biological invasions of terrestrial plants under possible climate change scenarios. Invasive species are recognized for their ability to modify soil microbial communities and influence ecosystem dynamics. Here, we focused on six species of allelopathic flowering plants—Ailanthus altissima, Casuarina equisetifolia, Centaurea stoebe ssp. micranthos, Dioscorea bulbifera, Lantana camara, and Schinus terebinthifolia—that are invasive in North America and examined their potential to spread further during projected climate change. We used Species Distribution Models (SDMs) to predict future suitable areas for these species in North America under several proposed future climate models. ENMEval and Maxent were used to develop SDMs, estimate current distributions, and predict future areas of suitable climate for each species. Areas with the greatest predicted suitable climate in the future include the northeastern and the coastal northwestern regions of North America. Range size estimations demonstrate the possibility of extreme range loss for these invasives in the southeastern United States, while new areas may become suitable in the northeastern United States and southeastern Canada. These findings show an overall northward shift of suitable climate during the next few decades, given projected changes in temperature and precipitation. Our results can be utilized to analyze potential shifts in the distribution of these invasive species and may aid in the development of conservation and management plans to target and control dissemination in areas at higher risk for potential future invasion by these allelopathic species.     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

     

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 potential range of <Emphasis Type="Italic">Ailanthus altissima</Emphasis> (tree of heaven) in South Africa: the roles of climate,land use and disturbance

Invasive trees are a major problem in South Africa. Many species are well established whereas others are still in the early stages of invasion. The management of invasive species is most cost effective at the early stages of invasion; it is thus essential to target and contain naturalizing invaders before they spread across the landscape. Multi-scale species distribution models (SDMs) provide useful insights to managers; they combine species-occurrence observations with climatic variables to predict potential distributions of alien species. Applying SDMs in human-dominated ecosystems is complicated because many factors associated with human actions interact in complex ways with climatic and edaphic factors to determine the potential suitability of sites for species. The aim of this study was to determine the degree to which a worldwide invader, A. altissima (Simaroubaceae) has occupied its potential range in South Africa, to identify areas at risk of future invasion. To do this we built a set of SDMs at both global and country scales using climatic, land use and human-footprint data. Climatic data best explained the distribution of A. altissima at the global scale whereas variables reflecting human-mediated disturbances were most influential at the national scale. Our analyses show the importance of human-mediated disturbances at a global scale and human occupancy at a country scale in determining the range limits of A. altissima. Populations of this tree species are already present in most parts of South Africa that are environmentally suitable for the species, and management actions need to focus on preventing increases in density in these areas.