Scaling down distribution maps from atlas data: a test of different approaches with virtual species

Aim Conservation managers are increasingly looking for modelled projections of species distributions to inform management strategies; however, the coarse resolution of available data usually compromises their helpfulness. The aim of this paper is to delineate and test different approaches for converting coarse‐grain occurrence data into high‐resolution predictions, and to clarify the conceptual circumstances affecting the accuracy of downscaled models. Location We used environmental data from a real landscape, southern Africa, and simulated species distributions within this landscape. Methods We built 10 virtual species at a resolution of 5 arcmin, and for each species we simulated atlas range maps at four decreasing resolutions (15, 30, 60, 120 arcmin). We tested the ability of three downscaling strategies to produce high‐resolution predictions using two modelling techniques: generalized linear models and generalized boosted models. We calibrated reference models with high‐resolution data and we compared the relative reduction of predictive performance in the downscaled models by using a null model approach. We also estimated the applicability of downscaling procedures to different situations by using distribution data for Mediterranean reptiles. Results All reference models achieved high performance measures. For all strategies, we observed a reduction of predictive performance proportional to the degree of downscaling. The differences in evaluation indices between reference models and downscaled projections obtained from atlases at 15 and 30 arcmin were never statistically significant. The accuracy of projections scaled down from 60 arcmin largely depended on the combination of approach and algorithm adopted. Projections scaled down from 120 arcmin gave misleading results in all cases. Main conclusions Moderate levels of downscaling allow for reasonably accurate results, regardless of the technique used. The most general effect of scaling down coarse‐grain data is the reduction of model specificity. The models can successfully delineate a species’ environmental association up until a 12‐fold downscaling, although with an increasing approximation that causes the overestimation of true distributions. We suggest appropriate procedures to mitigate the commission error introduced by downscaling at intermediate levels (approximately 12‐fold). Reductions of grain size > 12‐fold are discouraged.     

Testing population-level mechanisms of invasion by a mobile vertebrate: a simple conceptual framework for salmonids in streams

Invasion biology suffers from a lack of the ability to predict the outcome of particular invasions because of reliance on verbal models and lack of rigorous experimental data at the appropriate scale. More progress is likely to be made by considering invasions as population-level phenomena and initially focusing on specific taxa or particular categories of invasions. To this end, we propose a simple conceptual framework to motivate studies of invasion by salmonids (salmon, trout, grayling, and whitefish) in streams that emphasizes population-level mechanisms affecting native species and promoting spread by the invader. Specifically, the only direct mechanisms by which the abundance of the native species can decline are through biotic interactions which cause decreased reproductive rates or survival at specific life stages, net emigration, debilitating or fatal diseases introduced by the invader, or a combination of these factors. Conversely, abundance of the invader must increase by local reproduction, high survival, net immigration, or a combination of these factors. Review of existing salmonid invasion literature suggests that future studies could be improved by using manipulative field experiments at a spatial and temporal scale appropriate to address population-level processes, characterizing how movement affects the establishment and spread of an invader, and including abiotic context in experimental designs. Using the example of brook trout (Salvelinus fontinalis) invasion into streams containing native Colorado River cutthroat trout (Oncorhynchus clarki pleuriticus) in the central Rocky Mountains (USA), we demonstrate how the framework can be used to design a manipulative field experiment to test for population-level mechanisms causing ecological effects and promoting invasion success. Experiments of this type will give invasion ecologists a useful example of how a taxon-specific invasion framework can improve the ability to predict ecological effects, and provide fishery biologists with the quantitative foundation necessary to better manage stream salmonid invasions.     

Species Invasiveness in Biological Invasions: A Modelling Approach

The study of invasiveness, the traits that enable a species to invade a habitat, and invasibility, the habitat characteristics that determine its susceptibility to the establishment and spread of an invasive species, provide a useful conceptual framework to formulate the biological invasion problem in a modelling context. Another important aspect is the complex interaction emerging among the invader species, the noninvader species already present in the habitat, and the habitat itself. Following a modelling approach to the biological invasion problem, we present a spatially explicit cellular automaton model (Interacting Multiple Cellular Automata (IMCA)). We use field parameters from the invader Gleditsia triacanthos and the native Lithraea ternifolia in montane forests of central Argentina as a case study to compare outputs and performance of different models. We use field parameters from another invader, Ligustrum lucidum, and the native Fagara coco from the same system to run the cellular automaton model. We compare model predictions with invasion values from aerial photographs. We discuss in detail the importance of factors affecting species invasiveness, and give some insights into habitat invasibility and the role of interactions between them. Finally, we discuss the relevance of mathematical modelling for studying and predicting biological invasions. The IMCA model provided a suitable context for integrating invasiveness, invasibility, and the interactions. In the invasion system studied, the presence of an invader's juvenile bank not only accelerated the rate of invasion but was essential to ensure invasion. Using the IMCA model, we were able to determine that not only adult survival but particularly longevity of the native species influenced the spread velocity of the invader, at least when a juvenile bank is present. Other factors determining velocity of invasion detected by the IMCA model were seed dispersal distance and age of reproductive maturity. We derived relationships between species' adult survival, fecundity and longevity of both theoretical and applied relevance for biological invasions. Invasion velocities calculated from the aerial photographs agreed well with predictions of the IMCA model.     

Patterns of alien plant distribution at multiple spatial scales in a large national park: implications for ecology, management and monitoring

Aim Spatial scale is critical for understanding and managing biological invasions. In providing direction to managing alien plant invasions, much emphasis is placed on collecting spatially explicit data. However, insufficient thought is often given to how the data are to be used, frequently resulting in the incompatibility of the data for different uses. This paper explores the role of spatial scale in interpreting, managing and monitoring alien plant invasions in a large protected area. Location Kruger National Park, South Africa. Methods Using 27,000 spatially‐explicit records of invasive alien plants for the Kruger National Park (> 20,000 km²) we assessed alien plant species richness per cell at nine different scales of resolution. Results When assessing the patterns of alien plants at the various scales of resolution, almost identical results are obtained when working at scales of quarter‐degree grids and quaternary watersheds (the fourth level category in South Africa's river basin classification system). Likewise, insights gained from working at resolutions of 0.1–0.5 km and 1–5 km are similar. At a scale of 0.1 × 0.1 km cells, only 0.4% of the Kruger National Park is invaded, whereas > 90% of the park is invaded when mapped at the quarter‐degree cell resolution. Main conclusions Selecting the appropriate scale of resolution is crucial when evaluating the distribution and abundance of alien plant invasions, understanding ecological processes, and operationalizing management applications and monitoring strategies. Quarter‐degree grids and quaternary watersheds are most useful at a regional or national scale. Grid cells of 1 to 25 km² are generally useful for establishing priorities for and planning management interventions. Fine‐scale data are useful for informing management in areas which are small in extent; they also provide the detail appropriate for assessing patterns and rates of invasion.     

Redox and temperature-sensitive changes in microbial communities and soil chemistry dictate greenhouse gas loss from thawed permafrost

Shelf seas and their associated benthic habitats represent key systems in the global carbon cycle. However, the quantification of the related stocks and flows of carbon are often poorly constrained. To address benthic carbon storage in the North–West European continental shelf, we have spatially predicted the mass of particulate organic carbon (POC) stored in the top 10 cm of shelf sediments in parts of the North Sea, English Channel and Celtic Sea using a Random Forest model, POC measurements on surface sediments from those seas and relevant predictor variables. The presented model explains 78% of the variance in the data and we estimate that approximately 250 Mt of POC are stored in surficial sediments of the study area (633,000 km²). Upscaling to the North–West European continental shelf area (1,111,812 km²) yielded a range of 230–882 Mt of POC with the most likely estimate being on the order of 476 Mt. We demonstrate that the largest POC stocks are associated with coarse-grained sediments due to their wide-spread occurrence and high dry bulk densities. Our results also highlight the importance of coastal sediments for carbon storage and sequestration. Important predictors for POC include mud content in surficial sediments, annual average bottom temperature and distance to shoreline, with the latter possibly a proxy for terrestrial inputs. Now that key variables in determining the spatial distribution of POC have been identified, it is possible to predict future changes to the POC stock, with the presented maps providing an accurate baseline against which to assess predicted changes.     

Species distribution models support the need of international cooperation towards successful management of plant invasions

To protect native biodiversity and habitats from the negative impacts of biological invasions, comprehensive studies and measures to anticipate invasions are required, especially across countries in a transfrontier context. Species distribution models (SDMs) can be particularly useful to integrate different types of data and predict the distribution of invasive species across borders, both for current conditions and under scenarios of future environmental changes. We used SDMs to test whether predicting invasions and potential spatial conflicts with protected areas in a transfrontier context, under current and future climatic conditions, would provide additional insights on the patterns and drivers of invasion when compared to models obtained from predictions for individual regions/countries (different modelling strategies). The framework was tested with the invasive alien plant Acacia dealbata in North of Portugal/NW Spain Euro-region, where the species is predicted to increase its distribution under future climatic conditions. While SDMs fitted in a transfrontier context and using “the national strategy (with Portugal calibration data) presented similar patterns, the distribution of the invasive species was higher in the former. The transfrontier strategy expectedly allowed to capture a more complete and accurate representation of the species’ niche. Predictions obtained in a transfrontier context are therefore more suitable to support resource prioritisation for anticipation and monitoring impacts of biological invasions, while also providing additional support for international cooperation when tackling issues of global change. Our proposed framework provided useful information on the potential patterns of invasion by A. dealbata in a transfrontier context, with an emphasis on protected areas. This information is crucial for decision-makers focusing on the prevention of invasions by alien species inside protected areas in a transfrontier context, opening a new way for collaborative management of invasions.     

Competition between similar invasive species: modeling invasional interference across a landscape

As the number of biological invasions increases, interactions between different invasive species will become increasingly important. Several studies have examined facilitative invader–invader interactions, potentially leading to invasional meltdown. However, if invader interactions are negative, invasional interference may lead to lower invader abundance and spread. To explore this possibility, we develop models of two competing invaders. A landscape simulation model examines the patterns created by two such species invading into the same region. We then apply the model to a case study of Carduus nutans L. and C. acanthoides L., two economically important invasive weeds that exhibit a spatially segregated distribution in central Pennsylvania, USA. The results of these spatially-explicit models are generally consistent with the results of classic Lotka–Volterra competition models, with widespread coexistence predicted if interspecific effects are weaker than intraspecific effects for both species. However, spatial segregation of the two species (with lower net densities and no further spread) may arise, particularly when interspecific competition is stronger than intraspecific competition. A moving area of overlap may result when one species is a superior competitor. In the Carduus system, our model suggests that invasional interference will lead to lower levels of each species when together, but a similar net level of thistle invasion due to the similarity of intra- and interspecific competition. Thus, invasional interference may have important implications for the distribution and management of invasive species.     

Spatial Scaling of Non-Native Fish Richness across the United States

A major goal and challenge of invasion ecology is to describe and interpret spatial and temporal patterns of species invasions. Here, we examined fish invasion patterns at four spatially structured and hierarchically nested scales across the contiguous United States (i.e., from large to small: region, basin, watershed, and sub-watershed). All spatial relationships in both richness and fraction between species groups (e.g., natives vs. exotics) were positive at large scales. However, contrary to predictions using null/neutral models, the patterns at small scales were hump-shaped (unimodal), not simply negative. The fractions of both domestic (introduced among watersheds within the USA) and foreign (introduced from abroad) exotics increased with area across scales but decreased within each scale. The foreign exotics exhibited the highest dominance (lowest evenness) and spatial variation in distribution, followed by domestic exotics and natives, although on average natives still occupy larger areas than domestic and foreign exotics. The results provide new insight into patterns and mechanisms of fish species invasions at multiple spatial scales in the United States.     

Hotspots of plant invasion predicted by propagule pressure and ecosystem characteristics

Aim Biological invasions pose a major conservation threat and are occurring at an unprecedented rate. Disproportionate levels of invasion across the landscape indicate that propagule pressure and ecosystem characteristics can mediate invasion success. However, most invasion predictions relate to species’ characteristics (invasiveness) and habitat requirements. Given myriad invaders and the inability to generalize from single‐species studies, more general predictions about invasion are required. We present a simple new method for characterizing and predicting landscape susceptibility to invasion that is not species‐specific. Location Corangamite catchment (13,340 km²), south‐east Australia. Methods Using spatially referenced data on the locations of non‐native plant species, we modelled their expected proportional cover as a function of a site’s environmental conditions and geographic location. Models were built as boosted regression trees (BRTs). Results On average, the BRTs explained 38% of variation in occupancy and abundance of all exotic species and exotic forbs. Variables indicating propagule pressure, human impacts, abiotic and community characteristics were rated as the top four most influential variables in each model. Presumably reflecting higher propagule pressure and resource availability, invasion was highest near edges of vegetation fragments and areas of human activity. Sites with high vegetation cover had higher probability of occupancy but lower proportional cover of invaders, the latter trend suggesting a form of biotic resistance. Invasion patterns varied little in time despite the data spanning 34 years. Main conclusions To our knowledge, this is the first multispecies model based on occupancy and abundance data used to predict invasion risk at the landscape scale. Our approach is flexible and can be applied in different biomes, at multiple scales and for different taxonomic groups. Quantifying general patterns and processes of plant invasion will increase understanding of invasion and community ecology. Predicting invasion risk enables spatial prioritization of weed surveillance and control.     

Spatial autocorrelation and the analysis of invasion processes from distribution data: a study with the crayfish <Emphasis Type="Italic">Procambarus clarkii</Emphasis>

Complex spatial dynamics are frequent in invasive species; analyzing distribution patterns can help to understand the mechanisms driving invasions. We used different spatial regression techniques to evaluate processes determining the invasion of the red swamp crayfish Procambarus clarkii. We evaluated four a priori hypotheses on processes that may determine crayfish invasion: landscape alteration, connectivity, wetland suitability for abiotic and biotic features. We assessed the distribution of P. clarkii in 119 waterbodies in a recently invaded area. We used spatially explicit statistical techniques (spatial eigenvector mapping, generalized additive models, Bayesian intrinsic conditional autoregressive models) within an information-theoretic framework to assess the support of hypotheses; we also analyzed the pattern of spatial autocorrelation of data, model residuals, and eigenvectors. We found strong agreement between the results of spatial eigenvector mapping and Bayesian autoregressive models. Procambarus clarkii was significantly associated with the largest, permanent wetlands. Additive models suggested also association with human-dominated landscapes, but tended to overfit data. The results indicate that abiotic wetlands features and landscape alteration are major drivers of the species’ distribution. Species distribution data, residuals of ordinary least squares regression, and spatial eigenvectors all showed positive and significant spatial autocorrelation at distances up to 2,500 m; this may be caused by the dispersal ability of the species. Our analyses help to understand the processes determining the invasion and to identify the areas most at risk where screening and early management efforts can be focused. The comparison of multiple spatial techniques allows a robust assessment of factors determining complex distribution patterns.     

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km²). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.     

Effects of environmental and anthropogenic drivers on Amur tiger distribution in northeastern China

We examined environmental and anthropogenic factors drive range loss in large mammals, using presence data of Amur tigers opportunistically collected between 2000 and 2012, and anthropogenic and environmental variables to model the distribution of the Amur tiger in northeastern China. Our results suggested that population distribution models of different subregions showed different habitat factors determining tiger population distribution patterns. Where farmland cover was over 50 km² per pixel (196 km²), distance was within 15 km to the railway in Changbaishan and road density (length per pixel) increased in Wandashan, the relative probability of Amur tiger occurrence exhibited monotonic avoidance responses; however, where distance was within 150 km of the Sino-Russia border, the occurrence probability of Amur tiger was relatively high. We analyzed the avoidance or preference responses of Amur tiger distribution to elevation, snow depth and Viewshed. Furthermore, different subregional models detected a variety of spatial autocorrelation distances due to different population clustering patterns. We found that spatial models significantly improved model fits for non-spatial models and made more robust habitat suitability predications than that of non-spatial models. Consequently, these findings provide useful guidance for habitat conservation and management.     

Challenges and opportunities in population monitoring of cheetahs

Population monitoring is key to wildlife conservation and management but is challenging at the spatial and temporal extents necessary for understanding changes. Noninvasive survey methods and spatial capture–recapture (SCR) models have revolutionized wildlife monitoring by providing the means to acquire data at large scales and the framework to generate spatially explicit predictions, respectively. Despite opportunities for improved monitoring, challenges can remain in the study design and model fitting phases of an SCR approach. Here, we used a search-encounter design with multi-session SCR models to collect spatially indexed photographs and estimate changes in density of cheetahs between 2005 and 2013–2016 in the Masai Mara National Reserve (MMNR) in Kenya. Our SCR models of cheetah encounters suggested little change in cheetah density from 2005 to 2013–2016, with some evidence that density fluctuated annually in the MMNR. The sampling period length (5 vs. 10 months) and timing (early, late, full year) over which spatial encounters were modeled did not alter inferences about density when sample sizes were adequate (>20 spatially distinct encounters). Our average density estimate of ~1.2 cheetahs/100 km² is consistent with the impression that the MMNR provides important cheetah habitat in Africa. During most years, spatial distribution of vegetation greenness (proxy for ungulate habitat quality) accounted for important variation in encounter rates. The search-encounter design here could be applied to other regions for cheetah monitoring. While snapshot estimates of population size across time are useful for wildlife monitoring, open population models may better identify the mechanisms behind temporal changes.     

The influence of coarse‐scale environmental features on current and predicted future distributions of narrow‐range endemic crayfish populations

1. A major limitation to effective management of narrow‐range crayfish populations is the paucity of information on the spatial distribution of crayfish species and a general understanding of the interacting environmental variables that drive current and future potential distributional patterns. 2. Maximum Entropy Species Distribution Modeling Software (MaxEnt) was used to predict the current and future potential distributions of four endemic crayfish species in the Ouachita Mountains. Current distributions were modelled using climate, geology, soils, land use, landform and flow variables thought to be important to lotic crayfish. Potential changes in the distribution were forecast by using models trained on current conditions and projecting onto the landscape predicted under climate‐change scenarios. 3. The modelled distribution of the four species closely resembled the perceived distribution of each species but also predicted populations in streams and catchments where they had not previously been collected. Soils, elevation and winter precipitation and temperature most strongly related to current distributions and represented 65–87% of the predictive power of the models. Model accuracy was high for all models, and model predictions of new populations were verified through additional field sampling. 4. Current models created using two spatial resolutions (1 and 4.5 km²) showed that fine‐resolution data more accurately represented current distributions. For three of the four species, the 1‐km² resolution models resulted in more conservative predictions. However, the modelled distributional extent of Orconectes leptogonopodus was similar regardless of data resolution. Field validations indicated 1‐km² resolution models were more accurate than 4.5‐km² resolution models. 5. Future projected (4.5‐km² resolution models) model distributions indicated three of the four endemic species would have truncated ranges with low occurrence probabilities under the low‐emission scenario, whereas two of four species would be severely restricted in range under moderate–high emissions. Discrepancies in the two emission scenarios probably relate to the exclusion of behavioural adaptations from species‐distribution models. 6. These model predictions illustrate possible impacts of climate change on narrow‐range endemic crayfish populations. The predictions do not account for biotic interactions, migration, local habitat conditions or species adaptation. However, we identified the constraining landscape features acting on these populations that provide a framework for addressing habitat needs at a fine scale and developing targeted and systematic monitoring programmes.     

Gypsy moth response to landscape structure differs from neutral model predictions: implications for invasion monitoring

Simulations of dispersal across computer-generated neutral landscapes have generated testable predictions about the relationship between dispersal success and landscape structure. Models predict a threshold response in dispersal success with increasing habitat fragmentation. A threshold is defined as an abrupt, disproportionate decline in dispersal success at a certain proportion of habitat in the landscape. To identify potential empirical threshold responses in invasion success to landscape structure, we quantified the relationship between progression of the gypsy moth (Lymantria dispar) invasion wavefront across Michigan (1985–1996) and the structure of the Michigan landscape using two indices of invasion success and six landscape metrics. We also examined the effect of scale of analysis and choice of land cover characterization on our results by repeating our analysis at three scales using two different land cover maps. Contrary to simulation model predictions, thresholds in invasion success did not correspond closely with thresholds in landscape structure metrics. Increased variation in invasion success indices at smaller scales of analysis also suggested that invasion success should be studied at larger spatial extents (≥75 km²) than would be appropriate for characterizing individual dispersal events. The predictions of individual dispersal models across neutral landscapes may have limited applications for the monitoring and management of vagile species with excellent dispersal capabilities such as the gypsy moth.     

Inclusion of local environmental conditions alters high-latitude vegetation change predictions based on bioclimatic models

Current predictions of how species will respond to climate change are typically based on coarse-grained climate surfaces utilizing bioclimate envelope modelling. However, the suitability of environmental conditions for a given species might result from a variety of factors including some unrelated to climate. To address this issue, we investigated whether the inclusion of topographical and soil information in bioclimatic envelope models would significantly alter predictions of climate change—induced fine-scale tree and shrub species range size changes at the tree-limit in subarctic Europe. Using generalized additive models and data on current climate and species distributions and three different climate scenarios for the period 2040–2069, we developed predictions of the currently suitable area and potential range size changes of seven tree and shrub species in an area of 1,100 km² at a resolution of 1-ha. The inclusion of topography and soil information increased the predictive accuracy of climate-only models for all studied species. The predicted changes in species distribution volumes were contradictory, and the predicted occurrences varied greatly depending on the model used. Our results therefore support the arguments that vegetation responses to climate change can be influenced by local environmental conditions and that attention should be paid to the combined effects of these factors. We conclude that disregarding local topography and soil conditions in bioclimatic models may result in biased projections of range expansions and the associated colonization, extinction and turnover assessments.     

Cross-scale management strategies for optimal control of trees invading from source plantations

Biological invasion by non-native tree species can transform landscapes, and as a consequence, has received growing attention from researchers and managers alike. This problem is driven primarily by the naturalisation and invasion of tree species escaping from cultivation or forestry plantations. Furthermore, these invasions can be strongly influenced by the land-use matrix of the surrounding region, specific management of the source populations, and environmental conditions that influence seed dispersal or habitat quality for the invader. A major unresolved challenge for managing tree invasions in landscapes is how management should be deployed to contain or slow the spread of invading populations from one or more sources (e.g. plantations). We develop a spatial simulation model to test: (1) how to best prioritise the control of invasive tree populations spatially to slow or contain the biological invader when habitat quality varies in the landscape, and (2) how to allocate control effort among different management units when trees spread from many source populations. We first show that to slow down spread effectively, management strategy is less important than management effort. We then identify the conditions affecting the relative performance of different management strategies. At the landscape scale, targeting peripheral stands consistently yielded the best results whereas at the regional scale, management strategies needed to account for both habitat quality and tree life-history. Overall, our findings demonstrate that knowledge of how habitat affects tree life-history stages can improve management to contain or slow tree invasions by improving the spatial match between management effort and efficacy.     

A Tale of Four “Carp”: Invasion Potential and Ecological Niche Modeling

Background

Invasive species are a serious problem in ecosystems, but are difficult to eradicate once established. Predictive methods can be key in determining which areas are of concern regarding invasion by such species to prevent establishment [1]. We assessed the geographic potential of four Eurasian cyprinid fishes (common carp, tench, grass carp, black carp) as invaders in North America via ecological niche modeling (ENM). These “carp” represent four stages of invasion of the continent (a long-established invader with a wide distribution, a long-established invader with a limited distribution, a spreading invader whose distribution is expanding, and a newly introduced potential invader that is not yet established), and as such illustrate the progressive reduction of distributional disequilibrium over the history of species'' invasions.

Methodology/Principal Findings

We used ENM to estimate the potential distributional area for each species in North America using models based on native range distribution data. Environmental data layers for native and introduced ranges were imported from state, national, and international climate and environmental databases. Models were evaluated using independent validation data on native and invaded areas. We calculated omission error for the independent validation data for each species: all native range tests were highly successful (all omission values <7%); invaded-range predictions were predictive for common and grass carp (omission values 8.8 and 19.8%, respectively). Model omission was high for introduced tench populations (54.7%), but the model correctly identified some areas where the species has been successful; distributional predictions for black carp show that large portions of eastern North America are at risk.

Conclusions/Significance

ENMs predicted potential ranges of carp species accurately even in regions where the species have not been present until recently. ENM can forecast species'' potential geographic ranges with reasonable precision and within the short screening time required by proposed U.S. invasive species legislation.     

Scale dependent effects of native prey diversity,prey biomass and natural disturbance on the invasion success of an exotic predator

There is growing support for the general notion that the drivers of invasion success often shift from biotic to abiotic factors with increasing spatial scale. Most of this research, however, has been conducted on a single trophic level; i.e. it has primarily looked at how the diversity of native competitors may influence invasion success. Less attention has been paid to understanding how native prey diversity may influence the invasion success of exotic predators and whether such biotic factors are scale-dependent. We used a hierarchical spatial survey of 17 stream communities to test whether native prey diversity, along with native prey biomass, algal resource abundance and annual stream discharge, influenced the abundance of an exotic crayfish predator, and whether the importance of these factors were scale-dependent. We used a hierarchical generalized linear model to evaluate the influence of these community and stream characteristics on exotic crayfish abundance at both the transect scale (1 m²) and the stream scale (400 m²). Our results indicated that at the stream scale, high stream discharge significantly limited invader abundance. However, at the smaller transect scale, native prey biomass was a significant driver of invasion success and positively correlated with invader abundance. We suggest that our results add to the emerging pattern that abiotic processes are stronger determinants of invasion success at large spatial scales, whereas biotic processes become more important with decreasing spatial scale. However, for predator invasions, prey biomass, not prey diversity may be a more important for driver of invasion success at small spatial scales.     

Where will conflicts between alien and rare species occur after climate and land-use change? A test with a novel combined modelling approach

Protecting native biodiversity against alien invasive species requires powerful methods to anticipate these invasions and to protect native species assumed to be at risk. Here, we describe how species distribution models (SDMs) can be used to identify areas predicted as both suitable for rare native species and highly susceptible to invasion by alien species, at present and under future climate and land-use scenarios. To assess the condition and dynamics of such conflicts, we developed a combined predictive modelling (CPM) approach, which predicts species distributions by combining two SDMs fitted using subsets of predictors classified as acting at either regional or local scales. We illustrate the CPM approach for an alien invader and a rare species associated with similar habitats in northwest Portugal. Combined models predict a wider variety of potential species responses, providing more informative projections of species distributions and future dynamics than traditional, non-combined models. They also provide more informative insight regarding current and future rare-invasive conflict areas. For our studied species, conflict areas of highest conservation relevance are predicted to decrease over the next decade, supporting previous reports that some invasive species may contract their geographic range and impact due to climate change. More generally, our results highlight the more informative character of the combined approach to address practical issues in conservation and management programs, especially those aimed at mitigating the impact of invasive plants, land-use and climate changes in sensitive regions.