Use Of Species Distribution Models Research Articles

Limitations to the Use of Species-Distribution Models for Environmental-Impact Assessments in the Amazon.

Species-distribution models (SDM) are tools with potential to inform environmental-impact studies (EIA). However, they are not always appropriate and may result in improper and expensive mitigation and compensation if their limitations are not understood by decision makers. Here, we examine the use of SDM for frogs that were used in impact assessment using data obtained from the EIA of a hydroelectric project located in the Amazon Basin in Brazil. The results show that lack of knowledge of species distributions limits the appropriate use of SDM in the Amazon region for most target species. Because most of these targets are newly described and their distributions poorly known, data about their distributions are insufficient to be effectively used in SDM. Surveys that are mandatory for the EIA are often conducted only near the area under assessment, and so models must extrapolate well beyond the sampled area to inform decisions made at much larger spatial scales, such as defining areas to be used to offset the negative effects of the projects. Using distributions of better-known species in simulations, we show that geographical-extrapolations based on limited information of species ranges often lead to spurious results. We conclude that the use of SDM as evidence to support project-licensing decisions in the Amazon requires much greater area sampling for impact studies, or, alternatively, integrated and comparative survey strategies, to improve biodiversity sampling. When more detailed distribution information is unavailable, SDM will produce results that generate uncertain and untestable decisions regarding impact assessment. In many cases, SDM is unlikely to be better than the use of expert opinion.

Assessing suitable area for Acacia dealbata Mill. in the Ceira River Basin (Central Portugal) based on maximum entropy modelling approach

Biological invasion by exotic organisms became a key issue, a concern associated to the deep impacts on several domains described as resultant from such processes. A better understanding of the processes, the identification of more susceptible areas, and the definition of preventive or mitigation measures are identified as critical for the purpose of reducing associated impacts. The use of species distribution modeling might help on the purpose of identifying areas that are more susceptible to invasion. This paper aims to present preliminary results on assessing the susceptibility to invasion by the exotic species Acacia dealbata Mill. in the Ceira river basin. The results are based on the maximum entropy modeling approach, considered one of the correlative modelling techniques with better predictive performance. Models which validation is based on independent data sets present better performance, an evaluation based on the AUC of ROC accuracy measure. Keywords : susceptibility to invasion, maximum entropy, species distribution modelling, GIS. http://dx.doi.org/10.17127/got/2015.8.009 Data de submissão: 2015-07-31 Data de aprovação: 2015-12-04 Data de publicação: 2015-12-30

Integrated models that unite local and regional data reveal larger-scale environmental relationships and improve predictions of species distributions

The scale of environmental relationships is often inferred through the use of species distribution models. Yet such models are frequently developed at two distinct scales. Coarse-scale models typically use information-poor (e.g., presence-only) data to predict relative distributions across geographic ranges, whereas fine-scale models often use richer information (e.g., presence–absence data) to predict distributions at local to landscape scales. We unite presence–absence and presence-only data to predict occurrence of species, what we refer to as integrated distribution models. We determine if integrated models improve predictions of species distributions and identification of characteristic spatial scales of environmental relationships relative to presence–absence modeling and ensemble modeling that averages predictions from separate presence-only and presence–absence models. We apply recent advances in integrated distribution models to predict Sherman’s fox squirrel (Sciurus niger shermani) distribution in north-central Florida. Presence-only data were collected through a citizen-science program across its geographic range, while presence–absence data were collected using camera trapping surveys across 40 landscapes. Integrated models estimated environmental relationships with greater precision and identified larger characteristic scales for environmental relationships than using presence–absence data alone. In addition, integrated models tended to have greater predictive performance, which was more robust to the amount of presence–absence and presence-only data used in modeling, than presence–absence and ensemble models. Integrated distribution models hold much potential for improving our understanding of environmental relationships, the scales at which environmental relationships operate, and providing more accurate predictions of species distributions. Many avenues exist for further advancement of these modeling approaches.

Geographical isolation and invasion ecology

Isolation is a fundamental geographical characteristic that plays an important role in invasion ecology. However, risk mapping for established non-native species currently tends to ignore isolation, and instead focuses on the risk of population growth through the use of species distribution models. By focusing on how geographical isolation can be used to improve invasive species risk assessments this paper aims to: (i) provide a brief review of how methods to quantify isolation have developed, (ii) describe least-cost modelling as one approach to quantify isolation, (iii) promote catchment area mapping as a method of continuous isolation mapping that can improve invasive species risk assessments that may currently only consider risk from population growth, and (iv) discuss ways in which geographical isolation could be modified to control invasive species.

Spatial conservation priorities are highly sensitive to choice of biodiversity surrogates and species distribution model type

Pressure to conserve biodiversity with limited resources has led to increasing use of species distribution models (SDMs) for spatial conservation prioritization. Published spatial prioritization exercises often focus on well‐studied groups, with data compiled from on‐line databases of ad‐hoc collections. Conservation plans generally aim to protect all components of biodiversity, and it is implied that the species used in prioritization act as surrogates. Here, we assess the sensitivity of spatial priorities to model and surrogate choice using a case study from a fragmented agricultural area of south eastern Australia that is poorly represented in the national reserve system. We model the distributions of 30 species of bird, microbat and bee using two types of SDM; generalised linear models based on systematic surveys that yield presence and absence observations, and MaxEnt models based on biodiversity database records. Eight prioritization scenarios were tested using Zonation software, and were based on either the presence–background or presence–absence SDMs and combinations of surrogacy among the three taxa. We found low correlations between SDMs generated for the same species using different modelling frameworks (μ = 0.18, n = 26). Area under the receiver operating characteristic curve (AUC) estimates generated by MaxEnt were optimistic; on average 1.36 times higher than when tested against the systematic survey data. Conservation priorities were sensitive to the choice of surrogate and type of data used to fit SDMs, and though bats and birds formed moderately good surrogates for each other, there was less compelling evidence of surrogacy for bees. Because valid surrogacy is unlikely with most existing data sets, investment in high quality data for less‐surveyed groups prior to planning should still be a priority. If this is not possible, then it is advisable to analyse the sensitivity of conservation plans to the assumed surrogacy and quality of data available.

Evidence of a link between demographic rates and species habitat suitability from post release movements in a reinforced bird population

Despite the increasing use of species distribution models for predicting current or future animal distribution, only a few studies have linked the gradient of habitat suitability (HS) to demographic parameters. While such approaches can improve the reliability of models, they can help to better predict the response of species to changes in HS over space and time, as induced by ongoing global change. Here, we tested whether the spatial variation in HS along the individual movement path is related to survival, using extensive tracking data collected from captive‐bred individuals translocated to reinforce the wild populations of houbara bustard. We first modelled and mapped the HS from presence data of wild individuals using niche models in a consensus framework. We further analysed survival of released individuals using capture–recapture modelling and its links to HS, as the trend in suitability from the release sites along movements. We found that the survival of released individuals was related to changes in HS along their movements. For instance, individuals which moved to sites of lower HS than their release sites have lower survival probabilities than the others, independently of the HS of the release sites and daily movement rate. Our results provide an empirical support of the relationship between HS and survival, a major fitness component.

Improving the use of species distribution models in conservation planning and management under climate change.

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.

Expansion of Protected Areas under Climate Change: An Example of Mountainous Tree Species in Taiwan

Tree species in mountainous areas are expected to shift their distribution upward in elevation in response to climate change, calling for a potential redesign of existing protected areas. This study aims to predict whether or not the distributions of two high-mountain tree species, Abies (Abies kawakamii) and Tsuga (Tsuga chinensis var. formosana), will significantly shift upward due to temperature change, and whether current protected areas will be suitable for conserving these species. Future temperature change was projected for 15 different future scenarios produced from five global climate models. Shifts in Abies and Tsuga distributions were then predicted through the use of species distribution models (SDMs) which included occurrence data of Abies and Tsuga, as well as seasonal temperature, and elevation. The 25 km × 25 km downscaled General Circulation Model (GCMs) data for 2020–2039 produced by the Taiwan Climate Change Projection and Information Platform was adopted in this study. Habitat suitability in the study area was calculated using maximum entropy model under different climatic scenarios. A bootstrap method was applied to assess the parameter uncertainty of the maximum entropy model. In comparison to the baseline projection, we found that there are significant differences in suitable habitat distributions for Abies and Tsuga under seven of the 15 scenarios. The results suggest that mountainous ecosystems will be substantially impacted by climate change. We also found that the uncertainty originating from GCMs and the parameters of the SDM contribute most to the overall level of variability in species distributions. Finally, based on the uncertainty analysis and the shift in habitat suitability, we applied systematic conservation planning approaches to identify suitable areas to add to Taiwan’s protected area network.

The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon

The prevention of deforestation in rainforests requires the identification of where facilitating and mitigating factors will combine and increase the likelihood of deforestation. This approach, which relates a geographic space with an environmental space of factors to predict where new deforestation will occur, is very similar to the approaches used to predict species distributions. Thus, we believe that deforestation can be treated as a “species” and that its future occurrence can be determined using species distribution models.The objective of this work is to test the efficiency of species distribution models in predicting the potential areas of deforestation in a region of the western Amazon. We analyzed five different areas in the arc of deforestation. For each area, we ran the MaxEnt model in six different experiments to determine the boundaries of the probability distributions.Potential areas identified using the different models of MaxEnt were very effective in predicting deforestation areas. The models that used only previous deforestation density were less effective than the models that included functional variables.In four of the five areas, 80% of the new deforestation occurred in the area predicted by the models. These models were more effective than the business-as-usual (BAU) and governance (GOV) model scenarios described using the DINAMICA-EGO platform by Soares-Filho et al. (2006).Species distribution models are a valuable tool for determining potential areas of future Amazon deforestation. The use of these models arises as support to efforts to conserve tropical forests and identify critical locations where command and control actions against deforestation can be most efficient.

Diversity and distribution of deep-sea shrimps in the Ross Sea region of Antarctica.

Although decapod crustaceans are widespread in the oceans, only Natantia (shrimps) are common in the Antarctic. Because remoteness, depth and ice cover restrict sampling in the South Ocean, species distribution modelling is a useful tool for evaluating distributions. We used physical specimen and towed camera data to describe the diversity and distribution of shrimps in the Ross Sea region of Antarctica. Eight shrimp species were recorded: Chorismus antarcticus; Notocrangon antarcticus; Nematocarcinus lanceopes; Dendrobranchiata; Pasiphaea scotiae; Pasiphaea cf. ledoyeri; Petalidium sp., and a new species of Lebbeus. For the two most common species, N. antarcticus and N. lanceopes, we used maximum entropy modelling, based on records of 60 specimens and over 1130 observations across 23 sites in depths from 269 m to 3433 m, to predict distributions in relation to environmental variables. Two independent sets of environmental data layers at 0.05° and 0.5° resolution respectively, showed how spatial resolution affected the model. Chorismus antarcticus and N. antarcticus were found only on the continental shelf and upper slopes, while N. lanceopes, Lebbeus n. sp., Dendrobranchiata, Petalidium sp., Pasiphaea cf. ledoyeri, and Pasiphaea scotiae were found on the slopes, seamounts and abyssal plain. The environmental variables that contributed most to models for N. antarcticus were depth, chlorophyll-a concentration, temperature, and salinity, and for N. lanceopes were depth, ice concentration, seabed slope/rugosity, and temperature. The relative ranking, but not the composition of these variables changed in models using different spatial resolutions, and the predicted extent of suitable habitat was smaller in models using the finer-scale environmental layers. Our modelling indicated that shrimps were widespread throughout the Ross Sea region and were thus likely to play important functional role in the ecosystem, and that the spatial resolution of data needs to be considered both in the use of species distribution models.

Prevalence, thresholds and the performance of presence–absence models

SummaryThe use of species distribution models to understand and predict species' distributions necessitates tests of fit to empirical data. Numerous performance metrics have been proposed, many of which require continuous occurrence probabilities to be converted to binary ‘present or absent’ predictions using threshold transformations. It is widely accepted that both continuous and binary performance metrics should be independent of prevalence (the proportion of locations that are occupied). However, because these metrics have been mostly assessed on a case‐specific basis, there are few general guidelines for measuring performance.Here, we develop a conceptual framework for classifying performance metrics, based on whether they are sensitive to prevalence, and whether they require binary predictions. We use this framework to investigate how these performance metric properties influence the predictions made by the models they select.A literature survey reveals that binary metrics are widely employed and that prevalence‐independent metrics are used more frequently than prevalence‐dependent metrics. However, we show that prevalence‐dependent metrics are essential to assess the numerical accuracy of model predictions and are more useful in applications that require occupancy estimates. Furthermore, we demonstrate that in comparison with continuous metrics, binary metrics often select models that have reduced ability to separate presences from absences, make predictions which over‐ or underestimate occupancy and give misleading estimates of uncertainty. Importantly, models selected using binary metrics will often be of reduced practical use even when applied to ecological problems that require binary decision‐making.We suggest thatSDMperformance should be assessed using prevalence‐dependent performance metrics whenever the absolute values of occurrence predictions are important and that continuous metrics should be used instead of binary metrics whenever possible. We thus recommend the wider application of prevalence‐dependent continuous metrics, particularly likelihood‐based metrics such as Akaike's Information Criterion (AIC), to assess the performance of presence–absence models.

Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation

Range maps provide important information in species conservation management, specially in the case of rare species of conservation interest. For the vast majority of cases, this information can only be estimated by means of species distribution modelling. When absence data is unavailable, modelled distribution maps represent the spatial variation of the degree of suitability for the species rather than their realised distribution. Although discerning potentially suitable areas for a given species is an important asset in conservation, it is necessary to estimate current distributions in order to preserve current populations. This work explores the use of species distribution modelling (Maxent) for species of conservation interest when their Extent of Occurrence (EOO) is well-known and there is quality occurrence data. In this case, derived binary maps of potentially suitable areas can be obtained and used to assess the conservation and protection status of a given species in combination with the EOO and existing protected area networks. Seven species, which are rare and endemic to the Western Mediterranean, have been used as an example. Valuable information for conservation assessment such as potentially suitable areas, EOO, Areas of Occupancy (AOO) and degree of protection is provided for this set of species. In addition, the existing informal view that among experts these species have range sizes much smaller than their potentially suitable area is confirmed. This could probably be attributed to important but currently unknown predictor variables and to historical phylogeographic factors.

Can habitat modelling for the octopus Eledone cirrhosa help identify key areas for Risso’s dolphin in Scottish waters?

Marine protected areas (MPAs) are becoming increasingly important tools in the conservation of cetaceans. This has led to an interest in the use of species distribution models to predict where cetacean species are likely to occur based on the distribution of environmental variables. However, relationships between cetacean distribution and environmental variables are generally assumed to reflect the environmental preferences of their prey. Thus, understanding the distribution of prey may increase our ability to identify important areas for cetaceans. Here, we describe the diet of Risso’s dolphin (Grampus griseus) by analysing stomach contents of individuals stranded in Scotland over the last twenty years. Next, we use habitat modelling to test whether Rissos’s dolphin distribution in Scottish waters, as inferred from sightings, is related to the distribution of its main prey, the octopus Eledone cirrhosa. While good models of the relationship between the distribution of E. cirrhosa and environmental variables were obtained, there was no evidence of a relationship between modelled octopus distribution and the occurrence of Risso’s dolphins. These results suggest that identifying key areas for its main prey species is unlikely to help identify potential MPAs for Risso’s dolphin, at least at the spatial resolution used in this study.

Identifying optimal feeding habitat and proposed Marine Protected Areas (pMPAs) for the black-legged kittiwake (Rissa tridactyla) suggests a need for complementary management approaches

Marine Protected Areas (MPAs) are an important conservation tool. For marine predators, recent research has focused on the use of Species Distribution Models (SDMs) to identify proposed sites. We used a maximum entropy modelling approach based on static and dynamic oceanographic parameters to determine optimal feeding habitat for black-legged kittiwakes (Rissa tridactyla) at two colonies during two consecutive breeding seasons (2009 and 2010). A combination of Geographic Positioning System (GPS) loggers and Time-Depth Recorders (TDRs) attributed feeding activity to specific locations. Feeding areas were<30km from the colony, <40km from land, in productive waters, 25–175m deep. The predicted extent of optimal habitat declined at both colonies between 2009 and 2010 coincident with declines in reproductive success. Whilst the area of predicted optimal habitat changed, its location was spatially stable between years. There was a close match between observed feeding locations and habitat predicted as optimal at one colony (Lambay Island, Republic of Ireland), but a notable mismatch at the other (Rathlin Island, Northern Ireland). Designation of an MPA at Rathlin may, therefore, be less effective than a similar designation at Lambay perhaps due to the inherent variability in currents and sea state in the North Channel compared to the comparatively stable conditions in the central Irish Sea. Current strategies for designating MPAs do not accommodate likely future redistribution of resources due to climate change. We advocate the development of new approaches including dynamic MPAs that track changes in optimal habitat and non-colony specific ecosystem management.

How fine is fine-scale? Questioning the use of fine-scale bioclimatic data in species distribution models used for forecasting abundance patterns in butterflies

The use of species distribution models (SDMs) to predict the spatial occurrence and abundance of species in relation to environmental predictors has been debated in terms of species' ecology and biogeography. The predictive power of these models is well recognized for vertebrates, but has not yet been tested for invertebrates. In this study, we aim to assess the use of SDMs for predicting local abundances of invertebrates at a macroscale level. A maximum entropy algorithm was used to build SDMs based on occurrence records of 61 species of butterflies and bioclimatic information with a 30 arc second resolution. Predictions of habitat suitability were correlated with butterfly abundance data derived from independently conducted field surveys in order to check for a relationship between the predictions of the model and local abundances. Even though the model accurately described the current distributions of the species in the study area at a macroscale, the observed occurrences of the species (i.e. presence/absence) recorded by the field surveys differed significantly from the model's predictions for the corresponding grid cells. Moreover, there was no correlation between observed abundance and the model's predictions for most species of butterflies. We conclude that the spatial abundance of butterflies cannot be predicted from environmental suitability modelled at a resolution as large as in this study. Using the finest scale bioclimatic information currently available (i.e. 30 arc seconds) it is not adequate to predict species abundances as structural and ecological factors as well as climatic patterns acting at a smaller scale are key determinants of the occurrence and abundance of invertebrates. Therefore, future studies have to account for the role of the resolution in environmental predictors when assessments of spatial abundances via SDMs will be conducted.

Tracing glacial refugia of Triturus newts based on mitochondrial DNA phylogeography and species distribution modeling

IntroductionThe major climatic oscillations during the Quaternary Ice Age heavily influenced the distribution of species and left their mark on intraspecific genetic diversity. Past range shifts can be reconstructed with the aid of species distribution modeling and phylogeographical analyses. We test the responses of the different members of the genus Triturus (i.e. the marbled and crested newts) as the climate shifted from the previous glacial period (the Last Glacial Maximum, ~21 Ka) to the current interglacial.ResultsWe present the results of a dense mitochondrial DNA phylogeography (visualizing genetic diversity within and divergence among populations) and species distribution modeling (using two different climate simulations) for the nine Triturus species on composite maps.ConclusionsThe combined use of species distribution modeling and mitochondrial phylogeography provides insight in the glacial contraction and postglacial expansion of Triturus. The combined use of the two independent techniques yields a more complete understanding of the historical biogeography of Triturus than both approaches would on their own. Triturus newts generally conform to the ‘southern richness and northern purity’ paradigm, but we also find more intricate patterns, such as the absence of genetic variation and suitable area at the Last Glacial Maximum (T. dobrogicus), an ‘extra-Mediterranean’ refugium in the Carpathian Basin (T. cristatus), and areas where species displaced one another postglacially (e.g. T. macedonicus and western T. karelinii). We provide a biogeographical scenario for Triturus, showing the positions of glacial refugia, the regions that were postglacially colonized and the areas where species displaced one another as they shifted their ranges.

Discovery of Two New Localities for Todd's ParakeetPyrrhura picta caeruleicepsUsing Distribution Models: Enhancing Knowledge of a Little Known Neotropical Bird

Todd's parakeet Pyrrhura picta caeruleiceps is a geographically isolated race of painted parakeet P. picta, a widespread species. Several authors highlight the need to recognize Todd's parakeet at the species level, and despite past efforts to find new populations, no new locations have been found. We applied a Species Distribution Model (SDM) to guide a 30-day survey during 2011 to increase information on its geographic distribution. Surveys resulted in the discovery of two new populations in Los Motilones Mountains, on the north-eastern boundary of Colombia with Venezuela. Our field surveys support previous arguments of a continuous geographic range from the northern part of the Eastern Cordillera to the Perijá Mountains. However, we did not find birds at the base of the Colombian slope of the Perijá Mountains, and confirming its absence there requires further study. Habitat loss at the three localities visited was high, with most remaining habitat represented by continuous gallery forests located on steep and humid slopes at higher altitudes. If conditions for population maintenance are only present in those patches, it will be necessary to identify forest fragments that help maintain connectivity between populations on the northernmost part of the Eastern Cordillera and the Perijá Mountains. More surveys in the Perijá Mountains and in Venezuela are also recommended. This study provides further evidence of the usefulness of species distribution models to increase available information on rare or little known taxa.

Conservation planning under climate change: accounting for adaptive potential and migration capacity in species distribution models

AbstractAimA number of assumptions underpinning the use of species distribution models to predict biological responses to climate change are violated for temperate and boreal tree species that are widespread, long‐lived and genetically adapted to local climate conditions. To address this situation, we propose a methodology to account for the potential effects of genetic structure, adaptive potential and limited migration capacity.LocationBritish Columbia, Canada.MethodsSimilar to the widely used ‘no migration’ and ‘unlimited migration’ scenarios, we employ more refined biological response scenarios to evaluate the potential effects of genetic adaptation to local environments and the capacity of species to adapt and migrate. These scenarios are realized by two sets of geographic delineations that partition the species range into multiple populations and that subdivide the study area into smaller landscape units.ResultsIn a case study for British Columbia, we demonstrate how the approach can be used to evaluate the adequacy of a reserve system of 906 protected areas to ensure long‐term maintenance of forest genetic resources for 48 tree species. We find that between 35% and 85% of locally adapted populations in protected areas are maintained under a median climate change scenario until the end of the century. A sensitivity analysis shows that assumptions about migration and adaptation capacity of species have a major effect on the projected conservation status.Main conclusionsWe propose that the results of species distribution models have practical value for conservation planning if the focus is on maintenance rather than loss of suitable habitat. Accounting for genetic structure, adaptive potential and migration capacity through best‐case and worst‐case scenarios provide important information to effectively allocate limited resources available for conservation action.

Polyploidy associated with altered and broader ecological niches in the Claytonia perfoliata (Portulacaceae) species complex

Polyploids are often hypothesized to have distinct and broader niches than their diploid progenitors. Differences in geographic distributions between diploid and polyploids are frequently used to infer niche differentiation and increased breadth, but they are seldom used to test these hypotheses explicitly. Niche overlap and breadth were compared for diploids, tetraploids, and hexaploids of three taxa in the Claytonia perfoliata complex (C. parviflora, C. perfoliata, and C. rubra) with the use of species distribution models. Resampling and randomization approaches were used to test hypotheses of niche differentiation, breadth, and conservatism. Niche differentiation was detected between polyploid and diploid cytotypes assigned to the same taxon (e.g., C. parviflora 2× vs. 4×) but not between hexaploids and tetraploids within a taxon (e.g., C. parviflora 4× vs. 6×). Individual hexaploid cytotypes had broader ecological niches than individual diploid cytotypes. However, as a group the three hexaploid taxa did not exceed the combined niche breadth of the three diploids, suggesting that polyploidy does not result in transgressive niche breadth for this group. Niche overlap was lowest among diploids and was highest among the three hexaploid cytotypes, consistent with introgression associated with polyploidy resulting in greater ecological similarity. Although cytotypes possessed nonidentical niches, after accounting for environmental differences among ranges, cytotypes were more similar than expected, suggesting niche conservatism and similar responses to environmental characteristics. These results suggest that polyploids occupy distinct and broader niches relative to diploids but that cytotypes also share fundamentally similar responses to environmental variation across ploidy levels.

Effects of Climate Change on the Potential Species Richness of Mesoamerican Forests

ABSTRACTThe realized species richness of tropical forests cannot yet be reliably mapped at a regional scale due to lack of systematically collected data. An estimate of the potential species richness (PSR), however, can be produced through the use of species distribution modelling. PSR is interpretable as a climatically determined upper limit to observed species richness. We mapped current PSR and future PSR under climate change scenarios for Mesoamerica by combining the spatial distributions of 2000 tree species as predicted by generalized additive models built from herbaria records and climate layers. An explanatory regression tree was used to extract conditional rules describing the relationship between PSR and climate. The results were summarized by country, ecoregion and protected area status in order to investigate current and possible future variability in PSR in the context of regional biodiversity conservation. Length of the dry season was found to be the key determinant of PSR. Protected areas were found to have higher median PSR values than unprotected areas in most of the countries within the study area. Areas with exceptionally high PSR, however, remain unprotected throughout the region. Neither changes in realized species richness nor extinctions will necessarily follow changes in modelled PSR under climate change. However model output suggests that an increase in temperature of around 3°C, combined with a 20 percent decrease in rainfall could lead to a widespread reduction of around 15 percent of current PSR, with values of up to 40 percent in some moist lower montane tropical forests. The modelled PSR of dry forest ecoregions was found to be relatively stable. Some cooler upper montane forests in northern Mesoamerica, where few species of tropical origin are currently found, may gain PSR if species are free to migrate.