Integrating Species Distribution Research Articles

Integrating Maxent model and landscape ecology theory for studying spatiotemporal dynamics of habitat: Suggestions for conservation of endangered Red-crowned crane

Abstract Numerous studies have been conducted on current distribution and future changes of habitats by researchers from a range of disciplines such as ecology, biology, geography, environmental science, and agricultural science, among others. However, there is a lack of detailed studies on historical spatiotemporal dynamics of habitat, which can provide useful information for the conservation and management of endangered species habitats. Thus, we proposed an integrated framework to assess historical changes of habitat across space and time, as well as to analyze the driving mechanism based on Maxent model and landscape theory. Here, we collected Red-crowned crane records and environmental variables including climate, elevation, wind farm disturbance, land use and land cover (LULC) type and disturbance, and road disturbance at regional scale from 1984 to 2017. Our results suggested that there was a dramatic decline in habitat area, habitat suitability and habitat connectivity. LULC disturbance was the major driving factor leading to the decline, followed by LULC type and wind farm disturbance. Importantly, our results quantified LULC disturbance threshold, LULC type suitability, and range of wind farm impact, which served to propose specific recommendations for habitat restoration. Our approach highlighted the importance of integrating species distribution models and landscape ecology theory for conservation, hence was widely applicable to endangered species in the context of increasing human activities.

Use of historical data to improve conservation of the black grouse (Lyrurus tetrix) in Northeast China

AbstractBiological conservation requires sufficient basic data, like knowledge of the biogeographic characteristics and spatiotemporal dynamics of endangered species. Reconstructing range dynamics through time and detecting their underlying causes can be helpful for present and future conservation actions. Black grouse (Lyrurus tetrix) populations have declined during the late 20th century in Northeast China, but the mechanisms are still unclear. We integrated species distribution models with long‐term historical ecological records to reconstruct historical distributions of black grouse in Northeast China (1950s–2000s). Then, we prioritized the landscape for species protection based on the long‐term historical potential distribution to improve the conservation management of this species. The area of potential distribution was 27.01% of the total study area in the 1950s, but the potential distribution in the 2000s was 16.39% of the study area. The range loss from the 1950s to the 2000s was 160,567 km2, representing a 39.30% reduction. Within the study area, the species’ potential distribution in the Changbai Mountains has gradually disappeared since the 1980s. Based on conservation prioritization analysis, the priority conservation area is 151,276 km2, which is mainly distributed in the Greater and Lesser Khingan Mountains. National nature reserves only included 6.35% of the potential distribution of the black grouse in the 2000s. Several conservation actions should be implemented to rejuvenate black grouse populations in Northeast China.

A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir

SummarySpecies distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.

Multispecies conservation of freshwater fish assemblages in response to climate change in the southeastern United States

AbstractAimStreamflow and water temperature are primary variables influencing the distribution of freshwater taxa. Climate‐induced changes in these variables are already causing shifts in species distributions, with continued changes projected in the coming decades. The Mobile River Basin (MRB), located in the southeastern United States, contains some of the highest levels of temperate freshwater biodiversity in North America. We integrated species distribution data with contemporary and future streamflow and water temperature data as well as other physical habitat data to characterize occurrence probabilities of fish species in the MRB with the goal of identifying current and future areas of high conservation value.LocationMobile River Basin, southeastern United States.MethodsWe used a maximum entropy approach to estimate baseline and future occurrence probability distributions for 88 fish species in the MRB based on model‐generated streamflow and water temperature as well as geologic, topographic and land cover data. Areas of conservation prioritization were identified based on regions that contain suitable habitat for high levels of biodiversity according to baseline and future conditions while accounting for uncertainty associated with multiple future climate projections.ResultsOn average, flow (28%), water temperature (28%) and geology (30%) contribute evenly to determining suitable habitat for fish species in the MRB. Based on baseline and future species distribution model estimates, high priority streams (best 10%) are largely concentrated in the eastern portion of the MRB, with a majority (51%) located within the Coosa and Tallapoosa River systems.Main conclusionWe provide a framework that uses relevant hydrologic and environmental data in the context of future climatic uncertainty to estimate areas of freshwater conservation opportunity in the coming decades. While streamflow and water temperature represent important habitat for freshwater fishes in the MRB, distributions are also constrained by other aspects of the physical environment.

Assessment of the extinction risks of medicinal plants in Egypt under climate change by integrating species distribution models and IUCN Red List criteria

The IUCN Red List of Threatened Species is one of the most important of all conservation indicators, but most developing countries do not have enough information with which to make assessments. The use of species distribution models (SDMs) to predict habitat suitability, both currently and in the future under the effects of climate change, offers a potential solution for estimating extinction risk. With a set of validated observations, we used SDMs to make preliminary evaluations of the risk of extinction for 114 Egyptian medicinal plants based on IUCN Red-List Criteria and Categories. Using MaxEnt and eleven environmental variables, distributions were projected for 2020, 2050, and 2080 under two emission scenarios (A2a and B2a) assuming unlimited and no dispersal. The IUCN assessments used the predicted distributions as well as the actual records to measure both extent of occurrence (EOO) and area of occupancy (AOO). There was a positive correlation between EOO estimates based on actual records and those based on SDMs, demonstrating the ability of SDMs to compensate for a lack of data. Most species could be classified as Least Concern (LC) at the current time, whilst in the future under climate change, many species face some risk of extinction, depending on assumptions. We conclude that it is possible to make regional risk assessments even in data-sparse countries, in order to plan conservation management in the future. Using species distribution modelling together with IUCN Red-List assessment is a good method for countries where data are sparse in order to conserve and protect threatened species.

Resolving misaligned spatial data with integrated species distribution models.

Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change‐of‐support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser‐quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.

A practical guide for combining data to model species distributions.

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.

Effects of climatically shifting species distributions on biocultural relationships

Abstract Local and Indigenous Peoples play critical roles in safeguarding global biological and cultural diversity. However, species distribution modelling has yet to incorporate perspectives that assess threats to the linked biological and cultural systems of local and Indigenous Peoples. Here, we provide the first example of integrating species distribution modelling with benefit‐relevant indicators. This novel approach assesses how human access to culturally important species may change over time. Focusing on two culturally significant species used by the Indigenous Māori people of New Zealand, we first identified predictor variables relevant to the habitat of each species. We used species distribution models (SDMs) to estimate the recent (1961–1990) potential distribution for each species based on occurrence records and predictor variables, then generated future climate suitability maps. Our models show that future suitability for one species shifts to the south, in line with changes in temperature and precipitation, while the second species range expands into higher latitudes, driven primarily by increased temperature. When we combined these models with knowledge of tribal boundaries and cultural practices, results indicated that these distributions might decrease access to culturally important plants. Future suitability for one species shifted substantially from where it is most valued for weaving, while the second species range expanded to include more of its primary medicinal users. Climate change‐mediated shifts in the ranges of these species are likely to affect intergenerational human–environment relationships, sense of place, cultural identity and knowledge on a regional scale, as well as cultural identity and social cohesion on a national scale. By interpreting SDMs within a socioecological framework, this research illustrates a new approach to assessment of vulnerabilities to climate change and identifies strategies for adaptation. A plain language summary is available for this article.

Genetic diversity and demographic analysis of an endangered tree species Diplopanax stachyanthus in subtropical China: implications for conservation and management

With climatic change and anthropogenic activities, endangered species in subtropical China face high risk of extinction. However, many of them have received little attention in the past. In this study, three chloroplast DNA fragments (trnL–trnF, trnH–psbA, rps16) and integrated species distribution models were employed to evaluate the genetic diversity, genetic structure and distribution area changes of the endangered tree species Diplopanax stachyanthus. We identified three lineages (i.e., the eastern, central and western lineages) and high levels of genetic diversity in this species. The genetic differentiation is extremely high among the three lineages and among populations; most of the ten populations fixed private haplotype(s). Our time estimation elucidated that the splits of these lineages occurred during Pliocene to Pleistocene. Bayesian skyline plots analyses showed the change of effective population size of D. stachyanthus at ca. 0.1 Ma. Multiple interglacial refugia were inferred for D. stachyanthus in subtropical China. Climatic fluctuation and geographic changes during the Quaternary as well as habitat losses may have played important roles in driving intraspecific divergence and shaped current genetic structure of D. stachyanthus. Furthermore, there may be a major decrease in the suitable habitats for this species. Our findings would shed light on the evolutionary history of D. stachyanthus in subtropical China, and facilitate the conservation and management of this species.

MapBio：Mapping Biodiversity of China

MapBio is a project initiated by the Chinese Academy of Sciences, which aims at integrating species distribution data from different sources and mapping the biodiversity of China to support biodiversity research and biodiversity conservation decisions. Species distribution data may be found in journal articles, books and different databases in various formats, and most species distributions are described in free text. MapBio is trying to build up a workflow for collecting this free text, parsing it into standardized data and projecting distributions onto a map for each species in China. A map module of MapBio is designed and implemented based on Web GIS to visualize species distributions on a map at different levels, e.g., occurrence points, county, province, distribution range, protected area, waterbody, biogeographic realm. Since the completeness of distribution data is very important for assessing biodiversity, we developed a tool in MapBio for analysis of the gaps in distribution data. Based on the species distribution data, especially the occurrence data, MapBio provides an integrated modeling tool for helping users to build species niche models. MapBio is an open access project. Users can get data and services from it easily for biodiversity research and conservation, and also can contribute their own biodiversity data to MapBio.

Towards an Integrated Species Distribution Modelling Environment

The Australian Department of the Environment and Energy (DoEE) is working with the Atlas of Living Australia (ALA), Biodiversity Climate Change Virtual Laboratory (BCCVL) together with 2 state environment departments (New South Wales and Queensland) to develop a standard framework for modelling threatened species distributions for use in policy / environmental decision making. In addition, DoEE is working with 7 state and territory environment departments to implement a common assessment method (CAM) for the assessment and listing of nationally threatened species. The method is based on the IUCN Red List criteria. Each Australian jurisdiction has traditionally used different assessment method, including categories, criteria, thresholds, definitions and scales of assessment to list threatened species within their jurisdiction. The CAM is a standardised method for species assessed for listing at the national level. Through cross-jurisdictional collaboration, this will improve the efficiency of the assessment process and facilitate consistency across jurisdictional lists. The BCCVL includes linkages to species observations on the ALA and users are able to add their own data including contextual and species data. The project aims to create a secure environment where cross-jurisdictional collaboration can occur both on the standardisation of methodologies for creating species distributions and the integration of data. The project also aims to provide a secure platform for jurisdictions to contribute sensitive observations not available through the ALA and take into consideration expert feedback on the distribution of species. The project will provide a public-facing platform whereby SDM’s can be published. This will be searchable by area, species or contributor. All outputs will be scientifically robust, repeatable, maintainable, open and transparent. The increased validity and robustness of models lead to better informed decisions relating to impacts of development and conservation of species.

Assessing environmental attributes and effects of climate change on Sphagnum peatland distributions in North America using single- and multi-species models.

The fate of Northern peatlands under climate change is important because of their contribution to global carbon (C) storage. Peatlands are maintained via greater plant productivity (especially of Sphagnum species) than decomposition, and the processes involved are strongly mediated by climate. Although some studies predict that warming will relax constraints on decomposition, leading to decreased C sequestration, others predict increases in productivity and thus increases in C sequestration. We explored the lack of congruence between these predictions using single-species and integrated species distribution models as proxies for understanding the environmental correlates of North American Sphagnum peatland occurrence and how projected changes to the environment might influence these peatlands under climate change. Using Maximum entropy and BIOMOD modelling platforms, we generated single and integrated species distribution models for four common Sphagnum species in North America under current climate and a 2050 climate scenario projected by three general circulation models. We evaluated the environmental correlates of the models and explored the disparities in niche breadth, niche overlap, and climate suitability among current and future models. The models consistently show that Sphagnum peatland distribution is influenced by the balance between soil moisture deficit and temperature of the driest quarter-year. The models identify the east and west coasts of North America as the core climate space for Sphagnum peatland distribution. The models show that, at least in the immediate future, the area of suitable climate for Sphagnum peatland could expand. This result suggests that projected warming would be balanced effectively by the anticipated increase in precipitation, which would increase Sphagnum productivity.

Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection

Summary Two main sources of data for species distribution models (SDMs) are site‐occupancy (SO) data from planned surveys, and presence‐background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types. We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorporates detection probability. The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow‐bellied glider (Petaurus australis) in south‐eastern Australia, with the predictive performance of the Integrated Model again found to be superior. PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out‐of‐sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone. Unlike conventional SDMs which have restrictive scale‐dependence in their predictions, our Integrated Model is based on a point process model and has no such scale‐dependency. It may be used for predictions of abundance at any spatial‐scale while still maintaining the underlying relationship between abundance and area.

Integrating species distribution modelling into decision-making to inform conservation actions

Species distribution models (SDMs) have been widely tagged as valuable tools in a variety of conservation assessments to address pressing conservation problems. However, these solutions could be hampered by difficulties to overcome the knowledge-action boundary between conservation and modelling practice. These difficulties have been well typified in the ecological modelling sphere, but a specific conceptual framework on how to bridge this gap is still lacking. This work reports successful examples on how to use SDMs to identify the most favourable habitats for implementing conservation management actions. We use these examples to discuss about the three main topics that deserve special attention to help enhance information flow between practitioners and modellers: the decision context, the modelling framework and the spatial products. Finally, we suggest some practical solutions to improve applications of effective conservation action on the ground. We emphasize the importance of matching modelling goals and decision targets by a close collaboration of modellers with decision makers and species experts. Moreover, we highlight the key role of clear and useful spatial products to provide relevant and timely feedback to increase understanding and promote utilisation by conservation practitioners, and to inform and involve targeted audiences.

Climate change and landscape fragmentation jeopardize the population viability of the Siberian tiger (Panthera tigris altaica)

The Amur tiger, a flagship species of the boreal forest ecosystem in Russian Far East and northeastern China, has declined dramatically in population and geographic distribution due to human caused habitat fragmentation and poaching over the past century. The fate of this largest feline species will also be influenced by the worsening impacts of climate change. In this paper we assess the possible effects of climate change (three scenarios from the 2007 IPCC Report) on the Amur tiger by integrating species distribution modeling (SDM) and population viability analysis (PVA). We projected the potential and realized suitable habitat distributions to examine the impacts from anthropogenic factors, and evaluated the changes of suitable habitat and extinction risk for 100 years under climate change. The realized suitable habitat was projected to be more severely fragmented than the potential suitable habitat because of human-related factors. The potential suitable habitat would expand northward under all climate change scenarios considered. However, the tiger population would suffer the largest decline and highest extinction risk in the next 100 years under the worst climate change scenario (A1B) even though the size of potential habitat would be greatest. Under climate change, the tiger population could persist for the next century only if the size and quality of current habitat patches would remain intact. In addition, our study demonstrated that using SDM alone could grossly overestimate the geographic distribution of the Amur tiger, and that coupling SDM and PVA could provide important insights into conservation planning to mitigate the effects of climate change.

Bridging the gap between ecosystem modeling tools and geographic information systems: Driving a food web model with external spatial–temporal data

Research toward the impacts of climate change and human activities on marine ecosystems is challenged by the limitations of present-day ecosystem models to address the interrelated spatial dynamics between climate, ocean chemistry, marine food webs, and human systems. The work presented here, the spatial–temporal data framework, is part of a larger study, the NF-UBC Nereus Program, to develop a new approach to model interoperability for closing the gap between marine ecosystem modeling tools via geographic information systems (GIS) technology. The approach we present simplifies interdisciplinary model interoperability by separating technical and scientific challenges into a flexible and modular software approach. To illustrate capabilities of the new framework, we use a remote-sensing derived spatial and temporal time series to drive the primary production dynamics in an existing food web model of the North-Central Adriatic using the Ecospace module of the Ecopath with Ecosim approach. In general, the predictive capabilities of the food web model to hind-cast ecosystem dynamics are enhanced when applying the new framework by better reflecting observed species population trends and distributions. Results show that changes at the phytoplankton level due to changes in primary production are realistically reproduced and cascade up the pelagic food web. The dynamics of zooplankton and small and large pelagic fish are impacted. Highly exploited demersal species such as European hake do, however, not show clear signs of cascading. This may be due to the high fishing pressure on this species and the resulting strong historical decline in the area. In general, the development of the new framework offers ecosystem modelers with unprecedented capabilities to include spatial–temporal time series into food web analysis with a minimal set of required steps. It is a promising step toward integrating species distribution models and food web dynamics, and future implementations of interdisciplinary model interoperability.

Integrating species distribution models (SDMs) and phylogeography for two species of Alpine Primula

The major intention of the present study was to investigate whether an approach combining the use of niche-based palaeodistribution modeling and phylo-geography would support or modify hypotheses about the Quaternary distributional history derived from phylogeographic methods alone. Our study system comprised two closely related species of Alpine Primula. We used species distribution models based on the extant distribution of the species and last glacial maximum (LGM) climate models to predict the distribution of the two species during the LGM. Phylogeographic data were generated using amplified fragment length polymorphisms (AFLPs). In Primula hirsuta, models of past distribution and phylogeographic data are partly congruent and support the hypothesis of widespread nunatak survival in the Central Alps. Species distribution models (SDMs) allowed us to differentiate between alpine regions that harbor potential nunatak areas and regions that have been colonized from other areas. SDMs revealed that diversity is a good indicator for nunataks, while rarity is a good indicator for peripheral relict populations that were not source for the recolonization of the inner Alps. In P. daonensis, palaeo-distribution models and phylogeographic data are incongruent. Besides the uncertainty inherent to this type of modeling approach (e.g., relatively coarse 1-km grain size), disagreement of models and data may partly be caused by shifts of ecological niche in both species. Nevertheless, we demonstrate that the combination of palaeo-distribution modeling with phylogeographical approaches provides a more differentiated picture of the distributional history of species and partly supports (P. hirsuta) and partly modifies (P. daonensis and P. hirsuta) hypotheses of Quaternary distributional history. Some of the refugial area indicated by palaeodistribution models could not have been identified with phylogeographic data.

Integrating species distribution models and interacting particle systems to predict the spread of an invasive alien plant

AbstractAim We demonstrate how to integrate two widely used tools for modelling the spread of invasive plants, and compare the performance of the combined model with that of its individual components using the recent range dynamics of the invasive annual weed Ambrosia artemisiifolia L.Location Austria.Methods Species distribution models, which deliver habitat‐based information on potential distributions, and interacting particle systems, which simulate spatio‐temporal range dynamics as dependent on neighbourhood configurations, were combined into a common framework. We then used the combined model to simulate the invasion of A. artemisiifolia in Austria between 1990 and 2005. For comparison, simulations were also performed with models that accounted only for habitat suitability or neighbourhood configurations. The fit of the three models to the data was assessed by likelihood ratio tests, and simulated invasion patterns were evaluated against observed ones in terms of predictive discrimination ability (area under the receiver operating characteristic curve, AUC) and spatial autocorrelation (Moran’s I).Results The combined model fitted the data significantly better than the single‐component alternatives. Simulations relying solely on parameterized spread kernels performed worst in terms of both AUC and spatial pattern formation. Simulations based only on habitat information correctly predicted infestation of susceptible areas but reproduced the autocorrelated patterns of A. artemisiifolia expansion less adequately than did the integrated model.Main conclusions Our integrated modelling approach offers a flexible tool for forecasts of spatio‐temporal invasion patterns from landscape to regional scales. As a further advantage, scenarios of environmental change can be incorporated consistently by appropriately updating habitat suitability layers. Given the susceptibility of many alien plants, including A. artemisiifolia, to both land use and climate changes, taking such scenarios into account will increasingly become relevant for the design of proactive management strategies.

Identification of potential conflict areas between land transformation and biodiversity conservation in north-eastern South Africa

Transformation of natural vegetation to other land-uses, such as crop cultivation and urban development, presents the most important threat to biodiversity. Plant and animal species distribution data were employed to identify areas of high biodiversity value in the major summer crop production region in north-eastern South Africa. These areas of biodiversity conservation importance were then evaluated in terms of their (1) potential overlap with areas currently transformed by land-uses in the region and (2) potential co-occurrence with areas of natural vegetation cover likely to become cultivated. Integrating species distribution, land-cover and land capability data allowed for potential conflict areas, i.e. areas with a high biodiversity value facing large current or future land transformation threats to be identified. Areas of potential conflict appear to be central Gauteng, the KwaZulu-Natal coastline, Maputuland and the escarpment of Mpumalanga. Most of the arable areas, that are not currently under some form of land-use, are marginal lands where the physical land characteristics demand high input costs, give rise to low yields and are thus not suitable for full scale commercial cultivation. As the results indicate some of these areas have a high biodiversity value, land reform programs should therefore refrain from promoting cultivation on marginal lands in these conflict areas, as they provide the last safe havens for many species. The proportion of bird, butterfly, mammal and plant species’ ranges remaining in an untransformed state was quantified. Animal species with less than 60% of their natural range remaining, referred to as impacted species, comprised 63 bird, 207 butterfly and 15 mammal species. The grid cells containing these impacted species were identified as additional potential conflict areas. This study presents evidence that there is significant overlap between areas of biodiversity conservation interest and transformed or arable land in this region of South Africa and that there is an urgent need for the formulation of appropriate policies to promote biodiversity conservation on private farmland.