Incorporating bioclimatic and biogeographic data in the construction of species distribution models in order to prioritize searches for new populations of threatened flora

The aim of this study was to analyse the usefulness of incorporating bioclimatic and biogeographic data into digital species prediction and modelling tools in order to identify potential habitats of rare or endangered flora taxa. Species distribution models (SDMs) were obtained using the Maximum entropy algorithm. Habitat suitability maps were based on sites of known occurrence of studied species. The study showed that highly reliable habitat prediction models can be obtained through the inclusion of bioclimatic and biogeographic maps when modelling these species. The resultant SDMs are able to fit the search area more closely to the characteristics of the species, excluding the percentage of highly suitable areas that are located far from the known distribution of the taxon, where the probability of finding the plant is low. Therefore, it is possible to overcome one of the most commonly encountered problems in the construction of rare or threatened flora taxa SDMs, derived from the low number of initial citations. The resulting SDMs and the vegetation map enable prioritization of the search for new populations and optimization of the economic and human resources used in the collection of field data.     

Predicting distributions,habitat preferences and associated conservation implications for a genus of rare fishes,seahorses (Hippocampus spp.)

Aim

To identify useful sources of species data and appropriate habitat variables for species distribution modelling on rare species, with seahorses as an example, deriving ecological knowledge and spatially explicit maps to advance global seahorse conservation.

Location

The shallow seas.

Methods

We applied a typical species distribution model (SDM), maximum entropy, to examine the utility of (1) two versions of habitat variables (habitat occurrences vs. proximity to habitats) and (2) three sources of species data: quality research‐grade (RG) data, quality‐unknown citizen science (CS) and museum‐collection (MC) data. We used the best combinations of species data and habitat variables to predict distributions and estimate species–habitat relations and threatened status for seahorse species.

Results

We demonstrated that using “proximity to habitats” and integrating all species datasets (RG, CS and MC) derived models with the highest accuracies among all dataset variations. Based on this finding, we derived reliable models for 33 species. Our models suggested that only 0.4% of potential seahorse range was suitable to more than three species together; seahorse biogeographic epicentres were mainly in the Philippines; and proximity to sponges was an important habitat variable. We found that 12 “Data Deficient” species might be threatened based on our predictions according to IUCN criteria.

Main conclusions

We highlight that using proper habitat variables (e.g., proximity to habitats) is critical to determine distributions and key habitats for low‐mobility animals; collating and integrating quality‐unknown occurrences (e.g., CS and MC) with quality research data are meaningful for building SDMs for rare species. We encourage the application of SDMs to estimate area of occupancy for rare organisms to facilitate their conservation status assessment.

     

滇黄精的潜在分布与气候适宜性分析

为了解滇黄精(Polygonatum kingianum)的适宜生长区,运用Maxent模型模拟其潜在分布区,探讨其引种栽培的适宜气候条件。结果表明,预测模型的AUC值为0.974~0.980,表明模型具有良好的预测能力。滇黄精主要适生区位于我国西南地区,适生面积约81.34×10~4 km~2,占全国适生区面积的88.24%。云南的高度适生区面积最大(19.96×10~4 km~2);四川次之(5.49×10~4 km~2)。75%的高度适生区分布于海拔2 492 m以下的地区,3 400 m以上的地区不适宜于滇黄精生长。最冷月最低温度、7月最低温度、5-8月太阳辐射、最干月降水量、4月和9-11月平均降水量是限制滇黄精分布的主要气候变量。因此,海拔1 400~2 100 m的亚热带地区是滇黄精最适宜的生长区。     

Changes in distribution of waterbirds following prolonged drought reflect habitat availability in coastal and inland regions

Provision of suitable habitat for waterbirds is a major challenge for environmental managers in arid and semiarid regions with high spatial and temporal variability in rainfall. It is understood in broad terms that to survive waterbirds must move according to phases of wet–dry cycles, with coastal habitats providing drought refugia and inland wetlands used during the wet phase. However, both inland and coastal wetlands are subject to major anthropogenic pressures, and the various species of waterbird may have particular habitat requirements and respond individualistically to spatiotemporal variations in resource distribution. A better understanding of the relationships between occurrence of waterbirds and habitat condition under changing climatic conditions and anthropogenic pressures will help clarify patterns of habitat use and the targeting of investments in conservation. We provide the first predictive models of habitat availability between wet and dry phases for six widely distributed waterbird species at a large spatial scale. We first test the broad hypothesis that waterbirds are largely confined to coastal regions during a dry phase. We then examine the contrasting results among the six species, which support other hypotheses erected on the basis of their ecological characteristics. There were large increases in area of suitable habitat in inland regions in the wet year compared with the dry year for all species, ranging from 4.14% for Australian White Ibis to 31.73% for Eurasian Coot. With over half of the suitable habitat for three of the six species was located in coastal zones during drought, our study highlights the need to identify and conserve coastal drought refuges. Monitoring of changes in extent and condition of wetlands, combined with distribution modeling of waterbirds, will help support improvements in the conservation and management of waterbirds into the future.     

未来气候变化对孑遗植物鹅掌楸地理分布的影响

为了解未来气候波动对鹅掌楸(Liriodendron chinense)潜在适生区的影响,利用最大熵模型(Maxent)和地理信息系统(ArcGIS)软件,结合物种地理分布点信息,对鹅掌楸当前适生区分布进行了模拟和划分,同时预测了2061-2080年间气候变化条件下鹅掌楸的潜在适生分布区变化,进而分析影响鹅掌楸地理分布...     

Projected changes in distributions of Australian tropical savanna birds under climate change using three dispersal scenarios

Identifying the species most vulnerable to extinction as a result of climate change is a necessary first step in mitigating biodiversity decline. Species distribution modeling (SDM) is a commonly used tool to assess potential climate change impacts on distributions of species. We use SDMs to predict geographic ranges for 243 birds of Australian tropical savannas, and to project changes in species richness and ranges under a future climate scenario between 1990 and 2080. Realistic predictions require recognition of the variability in species capacity to track climatically suitable environments. Here we assess the effect of dispersal on model results by using three approaches: full dispersal, no dispersal and a partial-dispersal scenario permitting species to track climate change at a rate of 30 km per decade. As expected, the projected distributions and richness patterns are highly sensitive to the dispersal scenario. Projected future range sizes decreased for 66% of species if full dispersal was assumed, but for 89% of species when no dispersal was assumed. However, realistic future predictions should not assume a single dispersal scenario for all species and as such, we assigned each species to the most appropriate dispersal category based on individual mobility and habitat specificity; this permitted the best estimates of where species will be in the future. Under this "realistic" dispersal scenario, projected ranges sizes decreased for 67% of species but showed that migratory and tropical-endemic birds are predicted to benefit from climate change with increasing distributional area. Richness hotspots of tropical savanna birds are expected to move, increasing in southern savannas and southward along the east coast of Australia, but decreasing in the arid zone. Understanding the complexity of effects of climate change on species' range sizes by incorporating dispersal capacities is a crucial step toward developing adaptation policies for the conservation of vulnerable species.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

Population status and distribution modelling of the critically endangered riverine rabbit (Bunolagus monticularis)

The riverine rabbit (Bunolagus monticularis) is one of the most endangered mammals in the world due to fragmentation of its habitat in the semi‐arid Karoo region of South Africa, to which it is endemic. It is an umbrella conservation species for the riparian shrubland associated with the seasonal drainage system of the Karoo, where its presence is an indicator of ecosystem health. In this study, we analysed historical survey data to derive an improved assessment of the current B. monticularis population status and distribution. Geospatial analysis was conducted using geographical information systems, and distribution modelling was performed using Maxent. Extent of occurrence for the species is 54,227 km², and area of occupancy is 2943 km². Estimates of 157–207 mature individuals confirm an alarmingly small species population size, and it appears that no subpopulation has >50 mature individuals. Our findings thus support the continued classification of this species as ‘critically endangered’ under IUCN Red List criteria. However, with most remaining habitat occurring outside of protected areas, and with habitat loss being exacerbated by climate change, a viable conservation plan remains elusive.     

Using species distributions models for designing conservation strategies of Tropical Andean biodiversity under climate change

Biodiversity in the Tropical Andes is under continuous threat from anthropogenic activities. Projected changes in climate will likely exacerbate this situation. Using species distribution models, we assess possible future changes in the diversity and climatic niche size of an unprecedented number of species for the region. We modeled a broad range of taxa (11,012 species of birds and vascular plants), including both endemic and widespread species and provide a comprehensive estimation of climate change impacts on the Andes. We find that if no dispersal is assumed, by 2050s, more than 50% of the species studied are projected to undergo reductions of at least 45% in their climatic niche, whilst 10% of species could be extinct. Even assuming unlimited dispersal, most of the Andean endemics (comprising ∼5% of our dataset) would become severely threatened (>50% climatic niche loss). While some areas appear to be climatically stable (e.g. Pichincha and Imbabura in Ecuador; and Nariño, Cauca, Valle del Cauca and Putumayo in Colombia) and hence depict little diversity loss and/or potential species gains, major negative impacts were also observed. Tropical high Andean grasslands (páramos and punas) and evergreen montane forests, two key ecosystems for the provision of environmental services in the region, are projected to experience negative changes in species richness and high rates of species turnover. Adapting to these impacts would require a landscape-network based approach to conservation, including protected areas, their buffer zones and corridors. A central aspect of such network is the implementation of an integrated landscape management approach based on sustainable management and restoration practices covering wider areas than currently contemplated.