Species Distribution Model MaxEnt Research Articles

A quantitative synthesis of the importance of variables used in MaxEnt species distribution models

AbstractAimTo synthesize the species distribution modelling (SDM) literature to inform which variables have been used in MaxEnt models for different taxa and to quantify how frequently they have been important for species’ distributions.LocationGlobal.MethodsWe conducted a quantitative synthesis analysing the contribution of over 400 distinct environmental variables to 2040 MaxEntSDMs for nearly 1900 species representing over 300 families. Environmental variables were grouped into 24 related factors and results were analysed by examining the frequency with which variables were found to be most important, the mean contribution of each variable (at various taxonomic levels), and using TrueSkill™, a Bayesian skill rating system.ResultsPrecipitation, temperature, bathymetry, distance to water and habitat patch characteristics were the most important variables overall. Precipitation and temperature were analysed most frequently and one of these variables was often the most important predictor in the model (nearly 80% of models, when tested). Notably, distance to water was the most important variable in the highest proportion of models in which it was tested (42% of 225 models). For terrestrial species, precipitation, temperature and distance to water had the highest overall contributions, whereas for aquatic species, bathymetry, precipitation and temperature were most important.Main conclusionsOver all MaxEnt models published, the ability to discriminate occurrence from reference sites was high (averageAUC = 0.92). Much of this discriminatory ability was due to temperature and precipitation variables. Further, variability (temperature) and extremes (minimum precipitation) were the most predictive. More generally, the most commonly tested variables were not always the most predictive, with, for instance, ‘distance to water’ infrequently tested, but found to be very important when it was. Thus, the results from this study summarize the MaxEntSDMliterature, and can aid in variable selection by identifying underutilized, but potentially important variables, which could be incorporated in future modelling efforts.

Out of the weeds? Reduced plant invasion risk with climate change in the continental United States

Identifying invasion risk is critical for regional prioritization of management and monitoring, however, we currently lack a comprehensive assessment of the invasion risk posed by plants for the United States. We aim to quantify geographic invasion risk for currently established terrestrial invasive plants in the continental U.S. under current and future climate. We assembled a comprehensive occurrence database for 896 terrestrial invasive plant species from 33 regional collections of field and museum data and projected species ranges using MaxEnt species distribution models based on current (1950–2000 average) and future (2040–2060 average) climate. We quantified geographic invasion risk as differences in species richness, invasion debt, range infilling, and identification of hotspots. Potential invasive plant richness was higher than observed richness, particularly in eastern temperate forests, where as many as 83% of species with suitable climate have not yet established. A small percentage (median=0.22%) of species' potential ranges are currently occupied by them. With climate change, potential invasive plant richness declined by a median of 7.3% by 2050. About 80% of invasive plant hotspots were geographically stable with climate change, with the remaining 20% shifting northward. Invasion hotspots and current invasion debt reveal extensive, ongoing risk from existing invasive plants across the U.S., particularly in the Southeast. Climate change alters the spatial distributions of focal species for monitoring and is likely to reduce overall invasion risk in many areas. Early detection and rapid response programs could be most effective in stemming the spread of invasive plant species in areas with increased risk under climate change, while areas with persistent high risk are candidates for containment and control. The areas with reduced risk are prime locations for invasion of new imports from tropical and subtropical climates, highlighting the simultaneous need for prevention strategies.

Present and Future Potential Habitat Distribution of Carcharhinus falciformis and Canthidermis maculata By-Catch Species in the Tropical Tuna Purse-Seine Fishery under Climate Change

By-catch species from tropical tuna purse seine fishery have been affected by fishery pressures since the last century; however, the habitat distribution and the climate change impacts on these species are poorly known. With the objective of predicting the potential suitable habitat for a shark (Carcharhinus falciformis) and a teleost (Canthidermis maculata) in the Indian, Atlantic and Eastern Pacific Oceans, a MaxEnt species distribution model (SDM) was developed using data collected by observers in tuna purse seiners. The relative percentage of contribution of some environmental variables (depth, sea surface temperature, salinity and primary production) and the potential impact of climate change on species habitat by the end of the century under the A2 scenario (scenario with average concentrations of carbon dioxide of 856 ppm by 2100) were also evaluated. Results showed that by-catch species can be correctly modelled using observed occurrence records and few environmental variables with SDM. Results from projected maps showed that the equatorial band and some coastal upwelling regions were the most suitable areas for both by-catch species in the three oceans in concordance with the main fishing grounds. Sea surface temperature was the most important environmental variable which contributed to explain the habitat distribution of the two species in the three oceans in general. Under climate change scenarios, the largest change in present habitat suitability is observed in the Atlantic Ocean (around 16% of the present habitat suitability area of Carcharhinus falciformis and Canthidermis maculata, respectively) whereas the change is less in the Pacific (around 10% and 8%) and Indian Oceans (around 3% and 2 %). In some regions such as Somalia, the Atlantic equatorial band or Peru’s coastal upwelling areas, these species could lose potential habitat whereas in the south of the equator in the Indian Ocean, the Benguela System and in the Pacific coast of Central America, they could gain suitable habitat as consequence of global warming. This work presents new information about the present and future habitat distribution under climate change of both by-catch species which can contributes to the development of ecosystem-based fishery management and spatially driven management measures.

Climate change may alter breeding ground distributions of eastern migratory monarchs (Danaus plexippus) via range expansion of Asclepias host plants.

Climate change can profoundly alter species’ distributions due to changes in temperature, precipitation, or seasonality. Migratory monarch butterflies (Danaus plexippus) may be particularly susceptible to climate-driven changes in host plant abundance or reduced overwintering habitat. For example, climate change may significantly reduce the availability of overwintering habitat by restricting the amount of area with suitable microclimate conditions. However, potential effects of climate change on monarch northward migrations remain largely unknown, particularly with respect to their milkweed (Asclepias spp.) host plants. Given that monarchs largely depend on the genus Asclepias as larval host plants, the effects of climate change on monarch northward migrations will most likely be mediated by climate change effects on Asclepias. Here, I used MaxEnt species distribution modeling to assess potential changes in Asclepias and monarch distributions under moderate and severe climate change scenarios. First, Asclepias distributions were projected to extend northward throughout much of Canada despite considerable variability in the environmental drivers of each individual species. Second, Asclepias distributions were an important predictor of current monarch distributions, indicating that monarchs may be constrained as much by the availability of Asclepias host plants as environmental variables per se. Accordingly, modeling future distributions of monarchs, and indeed any tightly coupled plant-insect system, should incorporate the effects of climate change on host plant distributions. Finally, MaxEnt predictions of Asclepias and monarch distributions were remarkably consistent among general circulation models. Nearly all models predicted that the current monarch summer breeding range will become slightly less suitable for Asclepias and monarchs in the future. Asclepias, and consequently monarchs, should therefore undergo expanded northern range limits in summer months while encountering reduced habitat suitability throughout the northern migration.

Predicting the impacts of climate change on the potential distribution of major native non-food bioenergy plants in China.

Planting non-food bioenergy crops on marginal lands is an alternative bioenergy development solution in China. Native non-food bioenergy plants are also considered to be a wise choice to reduce the threat of invasive plants. In this study, the impacts of climate change (a consensus of IPCC scenarios A2a for 2080) on the potential distribution of nine non-food bioenergy plants native to China (viz., Pistacia chinensis, Cornus wilsoniana, Xanthoceras sorbifolia, Vernicia fordii, Sapium sebiferum, Miscanthus sinensis, M. floridulus, M. sacchariflorus and Arundo donax) were analyzed using a MaxEnt species distribution model. The suitable habitats of the nine non-food plants were distributed in the regions east of the Mongolian Plateau and the Tibetan Plateau, where the arable land is primarily used for food production. Thus, the large-scale cultivation of those plants for energy production will have to rely on the marginal lands. The variables of “precipitation of the warmest quarter” and “annual mean temperature” were the most important bioclimatic variables for most of the nine plants according to the MaxEnt modeling results. Global warming in coming decades may result in a decrease in the extent of suitable habitat in the tropics but will have little effect on the total distribution area of each plant. The results indicated that it will be possible to grow these plants on marginal lands within these areas in the future. This work should be beneficial for the domestication and cultivation of those bioenergy plants and should facilitate land-use planning for bioenergy crops in China.

What story does geographic separation of insular bats tell? A case study on Sardinian rhinolophids.

Competition may lead to changes in a species’ environmental niche in areas of sympatry and shifts in the niche of weaker competitors to occupy areas where stronger ones are rarer. Although mainland Mediterranean (Rhinolophus euryale) and Mehely’s (R. mehelyi) horseshoe bats mitigate competition by habitat partitioning, this may not be true on resource-limited systems such as islands. We hypothesize that Sardinian R. euryale (SAR) have a distinct ecological niche suited to persist in the south of Sardinia where R. mehelyi is rarer. Assuming that SAR originated from other Italian populations (PES) – mostly allopatric with R. mehelyi – once on Sardinia the former may have undergone niche displacement driven by R. mehelyi. Alternatively, its niche could have been inherited from a Maghrebian source population. We: a) generated Maxent Species Distribution Models (SDM) for Sardinian populations; b) calibrated a model with PES occurrences and projected it to Sardinia to see whether PES niche would increase R. euryale’s sympatry with R. mehelyi; and c) tested for niche similarity between R. mehelyi and PES, PES and SAR, and R. mehelyi and SAR. Finally we predicted R. euryale’s range in Northern Africa both in the present and during the Last Glacial Maximum (LGM) by calibrating SDMs respectively with SAR and PES occurrences and projecting them to the Maghreb. R. mehelyi and PES showed niche similarity potentially leading to competition. According to PES’ niche, R. euryale would show a larger sympatry with R. mehelyi on Sardinia than according to SAR niche. Such niches have null similarity. The current and LGM Maghrebian ranges of R. euryale were predicted to be wide according to SAR’s niche, negligible according to PES’ niche. SAR’s niche allows R. euryale to persist where R. mehelyi is rarer and competition probably mild. Possible explanations may be competition-driven niche displacement or Maghrebian origin.

Robustness and accuracy of Maxent niche modelling for Lactuca species distributions in light of collecting expeditions

Niche modelling software can be used to assess the probability of detecting a population of a plant species at a certain location. In this study, we used the distribution of the wild relatives of lettuce (Lactuca spp.) to investigate the applicability of Maxent species distribution models for collecting missions. Geographic origin data of genebank and herbarium specimens and climatic data of the origin locations were used as input. For Lactuca saligna, we varied the input data by omitting the specimens from different parts of the known distribution area to assess the robustness of the predicted distributions. Furthermore, we examined the accuracy of the modelling by comparing the predicted probabilities of population presence against recent expedition data for the endemic Lactuca georgica and the cosmopolitan Lactuca serriola. We found Maxent to be quite robust in its predictions, although its usefulness was higher for endemic taxa than for more widespread species. The exclusion of occurrence data from the perceived range margins of the species can result in important information about local adaptation to distinct climatic conditions. We discuss the potential for enhanced use of Maxent in germplasm collecting planning.

Shifts in the extent and location of rice cropping areas match the climate change pattern in China during 1980–2010

Knowledge of cropping areas and climate change is crucial to understanding the causes and consequences of global land use change, and the response of rice areas to climate change is a hot topic to global food security. This study investigates the impacts of climate change on suitable areas for rice cultivation and how the actual cultivated area of rice has been altered in response to climate change during the past three decades. To understand whether the shifts in the extent and location of rice cropping areas match the pattern of climate change, the yearly climate data from 726 weather stations and the rice census data from 2,343 counties were employed to simulate the climatically suitable region for rice using the MaxEnt species distribution model, as well as to model the actual geographical distribution of rice using the spatial allocation production model in each decade. The results show that approximately 3.9 % of all Chinese land area (roughly 3.7 × 107 ha) has become suitable for rice due to climate change over the past three decades, representing new potential areas for rice cultivation. Meanwhile, the actual rice cropping area has increased by approximately 18.2 %, indicating that the extent and location of the rice expansion match the pattern of climate change. However, some spatial inconsistencies did exist between the actual rice area’s expansion and the climatically suitable region after 1990. Nevertheless, climate change was a possible factor impacting the geospatial and temporal changes of the actual rice cropping area in China.

Implications of climatic heterogeneity for conservation of the Lesser Prairie‐Chicken (Tympanuchus pallidicinctus)

A geographic range is a heterogeneous matrix where the natural capability to support resident species varies from detrimental to optimal. Given this, the first priority for species conservation should be to determine where optimal environments exist. We used MaxEnt species distribution modeling to distinguish climatic characteristics associated with persistent leks from those at random locations in order to characterize the niche and potential distribution of the imperiled Lesser Prairie‐Chicken (Tympanuchus pallidicinctus). Annual, brood period, and winter precipitation were the most important characteristics of the niche across the entire range, but maximum temperature during nesting was a key characteristic in the southern part of the range. Habitat suitability across the geographic range was multimodal but also consistent with a center‐periphery pattern. Western Kansas and Oklahoma and east‐central New Mexico contributed more than expected, whereas all other areas contributed less than expected, to habitat suitability of the geographic range. Lesser Prairie‐Chickens appear constrained on the western edge of their range by abiotic factors, namely aridity, unfavorable temperatures and sandy soils. Conversely, they appear constrained on the eastern edge by biotic factors, namely transition from mid‐grass to tallgrass prairie that results in dominance by the Greater Prairie‐Chicken (Tympanuchus cupido). Conserving Lesser Prairie‐Chickens will depend on maintaining persistent populations in western Kansas and Oklahoma and east‐central New Mexico, as well as expanding its distribution across Texas. Aridity, unfavorable temperatures, and a paucity of habitat make eastern Colorado and southeastern New Mexico challenging areas for persistence of this species. Climate change may make it more difficult for populations in New Mexico and adjacent parts of Texas to persist, but more favorable for populations in eastern Colorado. The potential difficulties for populations in the southern part of the range will make it increasingly important to conserve populations in western Kansas and Oklahoma.

A Potential Distribution Model and Conservation Plan for the Critically Endangered Ecuadorian Capuchin, Cebus albifrons aequatorialis

Conservation actions that effectively and efficiently target single, highly threatened species require current data on the species’ geographic distribution and environmental associations. The Ecuadorian capuchin (Cebus albifrons aequatorialis) is a critically endangered primate found only in the fragmented forests of western Ecuador and northern Peru, which are among the world’s most severely threatened ecosystems. We use the MAXENT species distribution modeling method to model the potential distribution and environmental associations of Cebus albifrons aequatorialis, using all known presence localities recorded within the last 2 decades as well as 13 climate, topography, vegetation, and land-use data sets covering the entire geographic range of the subspecies. The environmental conditions that our model predicted to be ideal for supporting Cebus albifrons aequatorialis included ≥20% tree cover, mild temperature seasonality, annual precipitation <2000 mm, and low human population density. Our model identified 5028 km2 of suitable habitat remaining, although many of these forest fragments are unprotected and are unlikely to support extant populations. Using the median population density across all sites for which data are available, we estimate the total carrying capacity of the remaining habitat to be 12,500 total individuals. The true number of remaining individuals is likely to be considerably lower due to anthropogenic factors. We highlight four critical regions of high predicted suitability in western Ecuador and northern Peru on which immediate conservation actions should focus, and we lay out clear priorities to guide conservation actions for ensuring the long-term survival of this gravely threatened and little known primate.

Correction: The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

Correction: The Effects of Sampling Bias and Model Complexity on the Predictive Performance of MaxEnt Species Distribution Models

The importance of correcting for sampling bias in MaxEnt species distribution models

AbstractAimAdvancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better‐surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.LocationBorneo, Southeast Asia.MethodsWe collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range‐restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north‐eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.ResultsSpatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.Main ConclusionsWe conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.

The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as “feature types” in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.

Improving our understanding of environmental controls on the distribution of C3 and C4 grasses

A number of studies have demonstrated the ecological sorting of C3 and C4 grasses along temperature and moisture gradients. However, previous studies of C3 and C4 grass biogeography have often inadvertently compared species in different and relatively unrelated lineages, which are associated with different environmental settings and distinct adaptive traits. Such confounded comparisons of C3 and C4 grasses may bias our understanding of ecological sorting imposed strictly by photosynthetic pathway. Here, we used MaxEnt species distribution modeling in combination with satellite data to understand the functional diversity of C3 and C4 grasses by comparing both large clades and closely related sister taxa. Similar to previous work, we found that C4 grasses showed a preference for regions with higher temperatures and lower precipitation compared with grasses using the C3 pathway. However, air temperature differences were smaller (2°C vs. 4°C) and precipitation and % tree cover differences were larger (1783mm vs. 755mm, 21.3% vs. 7.7%, respectively) when comparing C3 and C4 grasses within the same clade vs. comparing all C4 and all C3 grasses (i.e., ignoring phylogenetic structure). These results were due to important differences in the environmental preferences of C3 BEP and PACMAD clades (the two main grass clades). Winter precipitation was found to be more important for understanding the distribution and environmental niche of C3 PACMADs in comparison with both C3 BEPs and C4 taxa, for which temperature was much more important. Results comparing closely related C3 -C4 sister taxa supported the patterns derived from our modeling of the larger clade groupings. Our findings, which are novel in comparing the distribution and niches of clades, demonstrate that the evolutionary history of taxa is important for understanding the functional diversity of C3 and C4 grasses, and should have implications for how grasslands will respond to global change.

Three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return lidar

Accurate measurement of forest canopy structure is critical for understanding forest-wildlife habitat relationships. Although most theory and application have been based on in situ measurements, imaging technologies such as Light Detection and Ranging (lidar) provide measurements that are both vertically accurate and horizontally extensive. We use small-footprint, multiple-return lidar from a state-wide dataset (1-m footprint, 0.11point/m2) to characterize the vertical and horizontal structure of successional loblolly pine (Pinus taeda) and mature, fire-maintained longleaf pine (Pinus palustris) forests on the coastal plain of North Carolina, USA. The relationship between these characteristics and the federally-endangered red-cockaded woodpecker’s (Picoides borealis, Vieillot) habitat preferences were assessed; as this species has a strong affinity for mature longleaf pine forests. Vertical structure was characterized by lidar-derived metrics (e.g., average and standard deviation of canopy height) and horizontal patterns of vertical structure were quantified by semivariograms and lacunarity analysis. Lidar metrics were compared with field measurements of stand structure and with woodpecker habitat use. We predicted woodpecker distribution using the Maxent species distribution modeling algorithm with elevation, landcover, and hydrography geospatial variables, with and without lidar-derived structural variables. Lidar successfully quantified canopy variation and differentiated between the structural characteristics of these two similar coniferous evergreen forest types (e.g. significant differences in maximum height, canopy cover, and size classes). Loblolly stands were found to have the tallest trees on average with a higher canopy cover. Both semivariograms and lacunarity analyses clearly differentiated between evergreen forest structural types (e.g. semivariogram range was 18.7m for longleaf, 32.3m for loblolly). By examining the immediate area around cavity nesting sites we found taller trees than those found across broader foraging sites. The species distribution model accurately predicted woodpecker distribution (tested with woodpecker presence, AUC>.85). The addition of lidar-derived variables improved the model’s predictive power by 8% compared to the model based only on elevation, landcover, and hydrography environmental variables. We show that relatively low density lidar data are valuable for wildlife studies by characterizing and separating similar canopy types, describing different use zones (foraging vs. nesting), and for use in species distribution models.

Climate Change and the Cost of Conserving Species in Madagascar

We examined the cost of conserving species as climate changes. We used a Maxent species distribution model to predict the ranges from 2000 to 2080 of 74 plant species endemic to the forests of Madagascar under 3 climate scenarios. We set a conservation target of achieving 10,000 ha of forest cover for each species and calculated the cost of achieving this target under each scenario. We interviewed managers of projects to restore native forests and conducted a literature review to obtain the net present cost per hectare of management actions to maintain or establish forest cover. For each species, we added hectares of land from lowest to highest cost per additional year of forest cover until the conservation target was achieved throughout the time period. Climate change was predicted to reduce the size of species' ranges, the overlap between species' ranges and existing or planned protected areas, and the overlap between species' ranges and existing forest. As a result, climate change increased the cost of achieving the conservation target by necessitating successively more costly management actions: additional management within existing protected areas (US$0-60/ha); avoidance of forest degradation (i.e., loss of biomass) in community-managed areas ($160-576/ha); avoidance of deforestation in unprotected areas ($252-1069/ha); and establishment of forest on nonforested land within protected areas ($802-2710/ha), in community-managed areas ($962-3226/ha), and in unprotected areas ($1054-3719/ha). Our results suggest that although forest restoration may be required for the conservation of some species as climate changes, it is more cost-effective to maintain existing forest wherever possible.

The performance of range maps and species distribution models representing the geographic variation of species richness at different resolutions

ABSTRACTAim The method used to generate hypotheses about species distributions, in addition to spatial scale, may affect the biodiversity patterns that are then observed. We compared the performance of range maps and MaxEnt species distribution models at different spatial resolutions by examining the degree of similarity between predicted species richness and composition against observed values from well‐surveyed cells (WSCs).Location Mexico.Methods We estimated amphibian richness distributions at five spatial resolutions (from 0.083° to 2°) by overlaying 370 individual range maps or MaxEnt predictions, comparing the similarity of the spatial patterns and correlating predicted values with the observed values for WSCs. Additionally, we looked at species composition and assessed commission and omission errors associated with each method.Results MaxEnt predictions reveal greater geographic differences in richness between species rich and species poor regions than the range maps did at the five resolutions assessed. Correlations between species richness values estimated by either of the two procedures and the observed values from the WSCs increased with decreasing resolution. The slopes of the regressions between the predicted and observed values indicate that MaxEnt overpredicts observed species richness at all of the resolutions used, while range maps underpredict them, except at the finest resolution. Prediction errors did not vary significantly between methods at any resolution and tended to decrease with decreasing resolution. The accuracy of both procedures was clearly different when commission and omission errors were examined separately.Main conclusions Despite the congruent increase in the geographic richness patterns obtained from both procedures as resolution decreases, the maps created with these methods cannot be used interchangeably because of notable differences in the species compositions they report.

Near-present and future distribution of Anopheles albimanus in Mesoamerica and the Caribbean Basin modeled with climate and topographic data

BackgroundAnopheles albimanus is among the most important vectors of human malaria in Mesoamerica and the Caribbean Basin (M-C). Here, we use topographic data and 1950–2000 climate (near present), and future climate (2080) layers obtained from general circulation models (GCMs) to project the probability of the species’ presence, p(s), using the species distribution model MaxEnt.ResultsThe projected near-present distribution parameterized with 314 presence points related well to the known geographic distribution in the study region. Different model experiments suggest that the range of An. albimanus based on near-present climate surfaces covered at least 1.27 million km2 in the M-C, although 2080 range was projected to decrease to 1.19 million km2. Modeled p(s) was generally highest in Mesoamerica where many of the original specimens were collected. MaxEnt projected near-present maximum elevation at 1,937 m whereas 2080 maximum elevation was projected at 2,118 m. 2080 climate scenarios generally showed increased p(s) in Mesoamerica, although results varied for northern South America and no major range expansion into the mid-latitudes was projected by 2080.ConclusionsMaxEnt experiments with near present and future climate data suggest that An. albimanus is likely to invade high-altitude (>2,000 m) areas by 2080 and therefore place many more people at risk of malaria in the M-C region even though latitudinal range expansion may be limited.

Invasion hotspots for non‐native plants in Australia under current and future climates

AbstractWe apply the concept of biodiversity hotspot analysis (the identification of biogeographical regions of high species diversity) to identify invasion hotspots – areas of potentially suitable climate for multiple non‐native plant species – in Australia under current and future climates. We used the species distribution model Maxent to model climate suitability surfaces for 72 taxa, recognized as ‘Weeds of National Significance’ (WoNS) in Australia, under current and projected climate for 2020 and 2050. Current climate suitability layers were summed across all 72 species, and we observed two regions of high climatic suitability corresponding to the top 25th percentile of combined climatic suitability values across Australia. We defined these as potential invasion hotspots. Areas of climatic suitability equivalent to the hotspot regions were identified in the composite maps for 2020 and 2050, to track spatial changes in the hotspots over the two time steps. Two potential invasion hotspot regions were identified under current and projected climates: the south west corner of Western Australia (SW), and south eastern Australia (SE). Herbarium data confirmed the presence of 73% and 99% of those species predicted to be in each hotspot respectively, suggesting that the SE has greater invasion potential. The area of both hotspots was predicted to retract southward and towards the coast under future climate scenarios, reducing in size by 81% (SW) and 71% (SE) by 2050. This reduction was driven by the dominance of southern temperate invasive plant species in the WoNS list (47 of the 72), of which 44 were predicted to experience reductions in their bioclimatic range by 2050. While climate is likely to become less suitable for the majority of WoNS in the future, potential invasion hotspots based on climate suitability are likely to remain in the far south of eastern Australia, and in the far south west of Western Australia by 2050.

Potential range of the invasive fish rotan (Perccottus glenii) in the Holarctic

Rotan Perccottus glenii is one of the most widespread alien invasive freshwater fish in Eurasia. We reviewed the mechanisms of its dispersion, identified the potential range and regarded these results in the light of possible prevention of further expansion. Our analysis was based on 970 presence records and 198 absence records from both invaded and native parts of the range. Since 1916 invasion dynamics of rotan have been driven by episodic anthropogenic translocations, followed by spontaneous expansion within river networks, and secondary translocations. Maxent species distribution models with independent validations showed that rotan distribution in Eurasia has climatic limitations; rotan have already invaded most areas with high climatic suitability in the Palearctic, but some regions of North Eurasia and North America, where rotan is currently absent, have high climatic suitability and may be vulnerable to invasion in the future. Rotan’s high invasiveness, lack of geographical barriers and absence of reliable methods to prevent spread present a very high risk of expansion within appropriate climate limits in Europe. Our analysis shows that the long-term invasion dynamics of an invasive freshwater species may depend on climate variables rather than on river basin borders. Species distribution models, based on large scale environmental layers, can be useful to understand the invasion risk for other freshwater species restricted to shallow aquatic habitats.