A Current Perspective on the Historical Geographic Distribution of the Endangered Muriquis (Brachyteles spp.): Implications for Conservation

The muriqui (Brachyteles spp.), endemic to the Atlantic Forest of Brazil, is the largest primate in South America and is endangered, mainly due to habitat loss. Its distribution limits are still uncertain and need to be resolved in order to determine their true conservation status. Species distribution modeling (SDM) has been used to estimate potential species distributions, even when information is incomplete. Here, we developed an environmental suitability model for the two endangered species of muriqui (Brachyteles hypoxanthus and B. arachnoides) using Maxent software. Due to historical absence of muriquis, areas with predicted high habitat suitability yet historically never occupied, were excluded from the predicted historical distribution. Combining that information with the model, it is evident that rivers are potential dispersal barriers for the muriquis. Moreover, although the two species are environmentally separated in a large part of its distribution, there is a potential contact zone where the species apparently do not overlap. This separation might be due to either a physical (i.e., Serra da Mantiqueira mountains) or a biotic barrier (the species exclude one another). Therefore, in addition to environmental characteristics, physical and biotic barriers potentially shaped the limits of the muriqui historical range. Based on these considerations, we proposed the adjustment of their historical distributional limits. Currently only 7.6% of the predicted historical distribution of B. hypoxanthus and 12.9% of B. arachnoides remains forested and able to sustain viable muriqui populations. In addition to measurement of habitat loss we also identified areas for conservation concern where new muriqui populations might be found.     

Genetic Pool Information Reflects Highly Suitable Areas: The Case of Two Parapatric Endangered Species of Tuco-tucos (Rodentia: Ctenomiydae)

Conservation of small mammals requires knowledge of the genetically and ecologically meaningful spatial scales at which species respond to habitat modifications. Conservation strategies can be improved through the use of ecological niche models and genetic data to classify areas of high environmental suitability. In this study, we applied a Maxent model integrated with genetic information (nucleotide diversity, haplotype diversity and Fu''s Fs neutrality tests) to evaluate potential genetic pool populations with highly suitable areas for two parapatric endangered species of tuco-tucos (Ctenomys minutus and C. lami). Our results demonstrated that both species were largely influenced by vegetation and soil variables at a landscape scale and inhabit a highly specific niche. Ctenomys minutus was also influenced by the variable altitude; the species was associated with low altitudes (sea level). Our model of genetic data associated with environmental suitability indicate that the genetic pool data were associated with highly suitable areas for C. minutus. This pattern was not evident for C. lami, but this outcome could be a consequence of the restricted range of the species. The preservation of species requires not only detailed knowledge of their natural history and genetic structure but also information on the availability of suitable areas where species can survive, and such knowledge can aid significantly in conservation planning. This finding reinforces the use of these two techniques for planning conservation actions.     

Confronting species distribution model predictions with species functional traits

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day⁻¹. Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = −0.26, 95% CI (−0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.     

Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.     

A species distribution model for pine marten (<Emphasis Type="Italic">Martes martes</Emphasis>) in the least forested region of Europe

Species distribution modelling is a useful technique that provides data on factors that can influence a species range, identify high suitability areas and model future scenarios. The pine marten (Martes martes) has undergone major historical declines in abundance and distribution in Northern Ireland, similar to that which has occurred throughout its range. Currently, the species is in a phase of range expansion in Northern Ireland, in what is the least forested landscape in Europe. To assess the suitability of this environment for pine marten re-establishment, presence only distribution data combined with landcover data at a 10-km scale were used in a species distribution modelling study using Maxent. The results indicated that approximately 32% (4500 km²) of the land area of Northern Ireland had a high probability of pine marten occurrence. Pine marten distribution was positively associated with the extent of conifer forest landcover types, which also had the highest single attribute contribution to the model. Landcover types that were negatively associated with pine marten distribution included the extent of open, dwarf and urban areas.     

Population dynamics in canopy gaps: nonlinear response to variable light regimes by an understory plant

Gap-dependent species are typically understood to have higher population growth rates (λs) when they are exposed to higher light transmittance. I investigated the relationship between both diffuse light and direct light transmittance and λ for the gap-dependent plant Trollius laxus using 5 years of data from 20 subpopulations (11 in created, experimental canopy gaps; 9 in intact canopy control areas). There was a nonlinear (unimodal) relationship between diffuse light and λ for T. laxus under the wide range of light levels encountered at the gap and control subpopulations [4–58 % diffuse photosynthetic photon flux density (PPFD)]. There was no relationship between direct light and λ. However, in the gaps, where light levels were generally greater than 20 % PPFD, both diffuse light and direct light had strong negative linear relationships with λ. Therefore, under wide-ranging light regimes, plant populations may show complicated, nonlinear responses to gap formation. Furthermore, gap-dependent plant populations may even decline in the brightest gaps. These results demonstrate that future studies on forest plant population dynamics should strive to include populations from a wide variety of light regimes, and avoid broadly categorizing light regimes as simply “gap” or “non-gap.”     

The Distributional Ecology of the Maned Sloth: Environmental Influences on Its Distribution and Gaps in Knowledge

The maned sloth Bradypus torquatus (Pilosa, Bradypodidae) is endemic to a small area in the Atlantic Forest of coastal Brazil. It has been listed as a threatened species because of its restricted geographic range, habitat loss and fragmentation, and declining populations. The major objectives of this study were to estimate its potential geographic distribution, the climatic conditions across its distributional range, and to identify suitable areas and potential species strongholds. We developed a model of habitat suitability for the maned sloth using two methods, Maxent and Mahalanobis Distance, based on 42 occurrence points. We evaluated environmental variable importance and the predictive ability of the generated distribution models. Our results suggest that the species distribution could be strongly influenced by environmental factors, mainly temperature seasonality. The modeled distribution of the maned sloth included known areas of occurrence in the Atlantic Forest (Sergipe, Bahia, Espírito Santo, and Rio de Janeiro), but did not match the observed distributional gaps in northern Rio de Janeiro, northern Espírito Santo or southern Bahia. Rather, the model showed that these areas are climatically suitable for the maned sloth, and thus suggests that factors other than climate might be responsible for the absence of species. Suitable areas for maned sloth were located mainly in the mountainous region of central Rio de Janeiro throughout Espírito Santo and to the coastal region of southern Bahia. We indicate 17 stronghold areas and recommended survey areas for the maned sloth. In addition, we highlight specific areas for conservation, including the current network protected areas. Our results can be applied for novel surveys and discovery of unknown populations, and help the selection of priority areas for management and conservation planning, especially of rare and relatively cryptic species directed associated with forested habitats.     

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time‐consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence‐only data. We evaluate our method on independently collected high‐quality presence‐absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce “hinge features” that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore “background sampling” strategies that cope with sample selection bias and decrease model‐building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence‐only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) “target‐group” background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.     

New insights into the distribution and conservation status of the Golden-White Tassel-Ear Marmoset <Emphasis Type="Italic">Mico chrysoleucos</Emphasis> (Primates,Callitrichidae)

Among the 13 Mico species recognized by the IUCN Red List of Threatened Species, six are listed as “Data Deficient”. The geographic range of most of the Mico species has been estimated from only a few records. We report new localities and the geographic extension of Mico chrysoleucos. In addition, we confirmed the presence of the species in two distinct protected areas. We modeled the habitat suitability of M. chrysoleucos using the maximum entropy method and including new records obtained by the authors in the state of Amazonas, Brazil. From the total area of occurrence calculated for the species, 22.8% is covered by protected areas and indigenous lands. The annual mean deforestation rate estimated between 2000 and 2015 was 2.95%, and the total area deforested by 2015 was 3354 km² or 8.6% of the total distribution limits of the species. The habitat lost between 2000 and 2015 was 3.2% (1131 km²) of the total potential distribution, while the habitat loss area legally protected was 31 km², and the habitat loss in settlements was equal to 691 km². Our results extend the geographic distribution of the species about 100 km farther south, with the Maracanã River being a possible geographic barrier for the species. The significantly low rate of habitat loss inside protected areas and indigenous land, when compared to unprotected areas, points out the importance of these areas to M. chrysoleucos conservation. The species is relatively wide-ranging, legally protected, and resilient to regional anthropic threats. However, the hydroelectric schemes and the improvement of the road system in southern Amazonia pose an imminent threat to the species.     

气候变化对黄土高原及邻近地区稀有种枯蝉分布的潜在影响(英文)

【目的】未来数十年的气候变化预计会是造成很多物种生境丧失的一个重要因素。对适应能力相对脆弱的地方性物种,预测气候变化对其生境的影响将对生物多样性保护具有重要意义。【方法】本文基于最大熵模型,对珍稀蝉科中国特有种枯蝉Subpsaltria yangi在当前和未来气候条件下的生境适宜度进行了评估。【结果】结果表明,枯蝉主要局限分布于黄土高原及邻近地区。预计至2050年,即使在温和的气候变化情景下,枯蝉的生境面积也会明显减少。影响枯蝉栖息地分布的关键因素为年平均气温、最冷月的最低气温、最冷季的平均气温和最潮湿月份的降水量。枯蝉现存种群栖息地应当受到保护,甘肃天水和陕西延安地区应作为枯蝉分布的核心区予以保护,以应对气候变化对其生境带来的影响。【结论】本研究获得的枯蝉适宜生境分布图可以为该稀有物种的新种群发现、现生种群分布地土地规划管理以及有效的自然保护区设立提供重要信息。     

Prospective sampling based on model ensembles improves the detection of rare species

Identifying the geographic distribution of populations is a basic, yet crucial step in many fundamental and applied ecological projects, as it provides key information on which many subsequent analyses depend. However, this task is often costly and time consuming, especially where rare species are concerned and where most sampling designs generally prove inefficient. At the same time, rare species are those for which distribution data are most needed for their conservation to be effective. To enhance fieldwork sampling, model‐based sampling (MBS) uses predictions from species distribution models: when looking for the species in areas of high habitat suitability, chances should be higher to find them. We thoroughly tested the efficiency of MBS by conducting an important survey in the Swiss Alps, assessing the detection rate of three rare and five common plant species. For each species, habitat suitability maps were produced following an ensemble modeling framework combining two spatial resolutions and two modeling techniques. We tested the efficiency of MBS and the accuracy of our models by sampling 240 sites in the field (30 sites×8 species). Across all species, the MBS approach proved to be effective. In particular, the MBS design strictly led to the discovery of six sites of presence of one rare plant, increasing chances to find this species from 0 to 50%. For common species, MBS doubled the new population discovery rates as compared to random sampling. Habitat suitability maps coming from the combination of four individual modeling methods predicted well the species' distribution and more accurately than the individual models. As a conclusion, using MBS for fieldwork could efficiently help in increasing our knowledge of rare species distribution. More generally, we recommend using habitat suitability models to support conservation plans.     

Rapid estimation of potential yield for data-poor Tapes philippinarum fisheries in North Adriatic coastal lagoons

We show how a simple species distribution model can be used for the rapid estimation of potential yield and for the identification of suitable sites for farming of Tapes philippinarum in two North Adriatic lagoons (Caleri and Marinetta-Vallona, Italy) in the face of limited data. We used a two-part species distribution model with sediment type, hydrodynamism, dissolved oxygen, and salinity as predictors of T. philippinarum potential yield. The first model component uses logistic regression to identify the areas in which clams occur, while the second component uses a weighted geometric mean of suitability values to estimate the potential annual yield (kg m^?2 year^?1) for the sites where T. philippinarum is predicted to be present. We used site-specific yield data from Caleri and Marinetta-Vallona to estimate the weights of the geometric mean by constrained linear regression. We validated the two-part model on an independent set of yield data (R _adj ²  = 0.82), and we then estimated the spatial distribution of potential yield in the two lagoons. The calibration and application of a simple species distribution model are useful tools for objectively identifying the most suitable sites for farming of T. philippinarum in North Adriatic lagoons.     

Population structure and genetic diversity of the only extant Baroninae swallowtail butterfly,Baronia brevicornis,revealed by ISSR markers

Due to its relict nature, the unique Baroninae swallowtail, Baronia brevicornis, is considered a “living fossil”. It is also one of the most enigmatic butterfly species with contentious origins and peculiar ecological characteristics. The aim of this study is to evaluate the genetic diversity and population structure of this endemic species of butterfly in Mexico. We sampled populations in two areas within its restricted geographical range in central Mexico and the isolated subspecies population in the state of Chiapas. Three ISSR primers produced 66 loci, indicating a high genetic diversity (P = 100 %, H _e  = 0.22) and variation range in these populations (62 % < P < 85 %, 0.18 < H _e  < 0.25). The Chiapas population presented the lowest values. The observed high values can be explained by the population dynamic of this species characterized by a very high density of individuals over very limited areas. Variation between populations appears to reflect both the age of colonization and locality perturbation level. Two methods of genetic structure analysis (Self-Organizing Map and Structure analysis) match to define three clusters. Natural and anthropogenic barriers may explain the separation between two clusters (cluster 1 and 2) of central Mexico but an unexpected result revealed that the Chiapas population is not genetically distinguishable from the central Mexico populations (cluster 3) leading us to hypothesize a possible “recent” separation or anthropogenic introduction. Habitat and host plant specificity probably limits the exchange of individuals between populations thus increasing fragmentation and leading to a complex genetic structure. We should put in place population monitoring schemes at different spatial scales, combining field occurrences and genetic tools, in order to reduce extinction susceptibility and keep track of recolonization events for this enigmatic species.     

Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

Integrating genetics and suitability modelling to bolster climate change adaptation planning in Patagonian <Emphasis Type="Italic">Nothofagus</Emphasis> forests

We investigated the impact of past changes in habitat suitability on the current patterns of genetic diversity of two southern beeches (Nothofagus nervosa and Nothofagus obliqua) in their eastern fragmented range in Patagonian Argentina, and model likely future threats to their population genetic structure. Our goal was to develop a spatially-explicit strategy for guiding conservation and management interventions in light of climate change. We combined suitability modelling under current, past (Last Glacial Maximum ~ 21,000 bp), and future (2050s) climatic conditions with genetic characterization data based on chloroplast DNA, isozymes, and microsatellites. We show the complementary usefulness of the distribution of chloroplast haplotypes and locally common allelic richness calculated from microsatellite data for identifying the locations of putative glacial refugia. Our findings suggest that contemporary hotspots of genetic diversity correspond to convergence zones of different expansion routes, most likely as a consequence of admixture processes. Future suitability predictions suggest that climate change might differentially affect both species. All genetically most diverse populations of N. nervosa and several of N. obliqua are located in areas that may be most severely impacted by climate change, calling for forward-looking conservation interventions. We propose a practical spatially- explicit strategy to target conservation interventions distinguishing priority populations for (1) in situ conservation (hotspots of genetic diversity likely to remain suitable under climate change), (2) ex situ conservation in areas where high genetic diversity overlaps with high likelihood of drastic climate change, (3) vulnerable populations (areas expected to be negatively affected by climate change), and (4) potential expansion areas under climate change.     

A climate change context for the decline of a foundation tree species in south-western Australia: insights from phylogeography and species distribution modelling

Background and Aims A worldwide increase in tree decline and mortality has been linked to climate change and, where these represent foundation species, this can have important implications for ecosystem functions. This study tests a combined approach of phylogeographic analysis and species distribution modelling to provide a climate change context for an observed decline in crown health and an increase in mortality in Eucalyptus wandoo, an endemic tree of south-western Australia.Methods Phylogeographic analyses were undertaken using restriction fragment length polymorphism analysis of chloroplast DNA in 26 populations across the species distribution. Parsimony analysis of haplotype relationships was conducted, a haplotype network was prepared, and haplotype and nucleotide diversity were calculated. Species distribution modelling was undertaken using Maxent models based on extant species occurrences and projected to climate models of the last glacial maximum (LGM).Key Results A structured pattern of diversity was identified, with the presence of two groups that followed a climatic gradient from mesic to semi-arid regions. Most populations were represented by a single haplotype, but many haplotypes were shared among populations, with some having widespread distributions. A putative refugial area with high haplotype diversity was identified at the centre of the species distribution. Species distribution modelling showed high climatic suitability at the LGM and high climatic stability in the central region where higher genetic diversity was found, and low suitability elsewhere, consistent with a pattern of range contraction.Conclusions Combination of phylogeography and paleo-distribution modelling can provide an evolutionary context for climate-driven tree decline, as both can be used to cross-validate evidence for refugia and contraction under harsh climatic conditions. This approach identified a central refugial area in the test species E. wandoo, with more recent expansion into peripheral areas from where it had contracted at the LGM. This signature of contraction from lower rainfall areas is consistent with current observations of decline on the semi-arid margin of the range, and indicates low capacity to tolerate forecast climatic change. Identification of a paleo-historical context for current tree decline enables conservation interventions to focus on maintaining genetic diversity, which provides the evolutionary potential for adaptation to climate change.     

Habitat and occurrence of ixodid ticks in the Liguria region,northwest Italy

Questing ticks were collected during monthly dragging sessions (March–August 2011) in three provinces of the Liguria region, north-western Italy, to evaluate the species occurrence, spatial distribution and relative abundance. A total of 1,464 specimens were collected in 94 dragging sites. Ixodes ricinus was the most abundant species (81.3 % of collected ticks), followed by Haemaphysalis punctata (10.9 %), Dermacentor marginatus (5.5 %), Ixodes frontalis (1.3 %), and Rhipicephalus spp. (0.9 %). Ixodes frontalis is reported for the first time in Liguria. An aggregation of I. ricinus positive sites was observed in inland areas characterized by dense forests dominated by deciduous trees (Castanetum and Fagetum phytoclimatic zones), especially in the west of the region where the differences in the Normalized Difference Vegetation Index (NDVI) were higher between inland and coastal sites. Negative binomial regression for repeated measures was used to model the associations of NDVI and season with counts of host-seeking I. ricinus nymphs. The NDVI was a good predictor of I. ricinus nymphs abundance, and confirmed its utility in discriminating habitat suitability for this vector in north-western coastal Italy, where dry habitat conditions may limit the distribution of this species.     

Is the Climate Right for Pleistocene Rewilding? Using Species Distribution Models to Extrapolate Climatic Suitability for Mammals across Continents

Species distribution models (SDMs) are increasingly used for extrapolation, or predicting suitable regions for species under new geographic or temporal scenarios. However, SDM predictions may be prone to errors if species are not at equilibrium with climatic conditions in the current range and if training samples are not representative. Here the controversial “Pleistocene rewilding” proposal was used as a novel example to address some of the challenges of extrapolating modeled species-climate relationships outside of current ranges. Climatic suitability for three proposed proxy species (Asian elephant, African cheetah and African lion) was extrapolated to the American southwest and Great Plains using Maxent, a machine-learning species distribution model. Similar models were fit for Oryx gazella, a species native to Africa that has naturalized in North America, to test model predictions. To overcome biases introduced by contracted modern ranges and limited occurrence data, random pseudo-presence points generated from modern and historical ranges were used for model training. For all species except the oryx, models of climatic suitability fit to training data from historical ranges produced larger areas of predicted suitability in North America than models fit to training data from modern ranges. Four naturalized oryx populations in the American southwest were correctly predicted with a generous model threshold, but none of these locations were predicted with a more stringent threshold. In general, the northern Great Plains had low climatic suitability for all focal species and scenarios considered, while portions of the southern Great Plains and American southwest had low to intermediate suitability for some species in some scenarios. The results suggest that the use of historical, in addition to modern, range information and randomly sampled pseudo-presence points may improve model accuracy. This has implications for modeling range shifts of organisms in response to climate change.