Predicting changes in the distribution and abundance of species under environmental change

Environmental changes are expected to alter both the distribution and the abundance of organisms. A disproportionate amount of past work has focused on distribution only, either documenting historical range shifts or predicting future occurrence patterns. However, simultaneous predictions of abundance and distribution across landscapes would be far more useful. To critically assess which approaches represent advances towards the goal of joint predictions of abundance and distribution, we review recent work on changing distributions and on effects of environmental drivers on single populations. Several methods have been used to predict changing distributions. Some of these can be easily modified to also predict abundance, but others cannot. In parallel, demographers have developed a much better understanding of how changing abiotic and biotic drivers will influence growth rate and abundance in single populations. However, this demographic work has rarely taken a landscape perspective and has largely ignored the effects of intraspecific density. We advocate a synthetic approach in which population models accounting for both density dependence and effects of environmental drivers are used to make integrated predictions of equilibrium abundance and distribution across entire landscapes. Such predictions would constitute an important step forward in assessing the ecological consequences of environmental changes.     

Net ecosystem productivity of boreal jack pine stands regenerating from clearcutting under current and future climates

Life cycle analysis of climate and disturbance effects on forest net ecosystem productivity (NEP) is necessary to assess changes in forest carbon (C) stocks under current or future climates. Ecosystem models used in such assessments need to undergo well-constrained tests of their hypotheses for climate and disturbance effects on the processes that determine CO₂ exchange between forests and the atmosphere. We tested the ability of the model ecosys to simulate diurnal changes in CO₂ fluxes under changing air temperatures (T_a) and soil water contents during forest regeneration with eddy covariance measurements over boreal jack pine (Pinus banksiana) stands along a postclearcut chronosequence. Model hypotheses for hydraulic and nutrient constraints on CO₂ fixation allowed ecosys to simulate the recovery of C cycling during the transition of boreal jack pine stands from C sources following clearcutting (NEP from −150 to −200 g C m⁻² yr⁻¹) to C sinks at maturity (NEP from 20 to 80 g C m⁻² yr⁻¹) with large interannual variability. Over a 126-year logging cycle, annualized NEP, C harvest, and net biome productivity (NBP=NEP–harvest removals) of boreal jack pine averaged 47, 33 and 14 g C m⁻² yr⁻¹. Under an IPCC SRES climate change scenario, rising T_a exacerbated hydraulic constraints that adversely affected NEP of boreal jack pine after 75 years. These adverse effects were avoided in the model by replacing the boreal jack pine ecotype with one adapted to warmer T_a. This replacement raised annualized NEP, C harvest, and NBP to 81, 56 and 25 g C m⁻² yr⁻¹ during a 126-year logging cycle under the same climate change scenario.     

An ecosystem model-based estimate of changes in water availability differs from water proxies that are commonly used in species distribution models

Aim  To assess whether the water availability measures commonly used in species distribution models might be misleading because they do not account for the hydrological effects of changes in vegetation structure and functioning.
Location  Europe.
Methods  We compared different methods for estimating water availability in species distribution models with the soil water content predicted by a process-based ecosystem model. The latter also accounted for the hydrological effects of dynamic changes in vegetation structure and functioning, including potential physiological effects of increasing CO₂.
Results  All proxies showed similar patterns of water availability across Europe for current climate, but when projected into the future, the changes in the simpler water availability measures showed no correlation with those projected by the more complex ecosystem model, even if CO₂ effects were switched off.
Main conclusions  Results from species distribution modelling studies concerning future changes in species ranges and biodiversity should be interpreted with caution, and more process-based representations of the water balance of terrestrial ecosystems should be considered within these models.     

Choice of predictor variables as a source of uncertainty in continental‐scale species distribution modelling under climate change

Aim Species distribution modelling is commonly used to guide future conservation policies in the light of potential climate change. However, arbitrary decisions during the model‐building process can affect predictions and contribute to uncertainty about where suitable climate space will exist. For many species, the key climatic factors limiting distributions are unknown. This paper assesses the uncertainty generated by using different climate predictor variable sets for modelling the impacts of climate change. Location Europe, 10° W to 50° E and 30° N to 60° N. Methods Using 1453 presence pixels at 30 arcsec resolution for the great bustard (Otis tarda), predictions of future distribution were made based on two emissions scenarios, three general climate models and 26 sets of predictor variables. Twenty‐six current models were created, and 156 for both 2050 and 2080. Map comparison techniques were used to compare predictions in terms of the quantity and the location of presences (map comparison kappa, MCK) and using a range change index (RCI). Generalized linear models (GLMs) were used to partition explained deviance in MCK and RCI among sources of uncertainty. Results The 26 different variable sets achieved high values of AUC (area under the receiver operating characteristic curve) and yet introduced substantial variation into maps of current distribution. Differences between maps were even greater when distributions were projected into the future. Some 64–78% of the variation between future maps was attributable to choice of predictor variable set alone. Choice of general climate model and emissions scenario contributed a maximum of 15% variation and their order of importance differed for MCK and RCI. Main conclusions Generalized variable sets produce an unmanageable level of uncertainty in species distribution models which cannot be ignored. The use of sound ecological theory and statistical methods to check predictor variables can reduce this uncertainty, but our knowledge of species may be too limited to make more than arbitrary choices. When all sources of modelling uncertainty are considered together, it is doubtful whether ensemble methods offer an adequate solution. Future studies should explicitly acknowledge uncertainty due to arbitrary choices in the model‐building process and develop ways to convey the results to decision‐makers.     

Current and future distribution of a parasite with complex life cycle under global change scenarios: Echinococcus multilocularis in Europe

Global change is expected to have complex effects on the distribution and transmission patterns of zoonotic parasites. Modelling habitat suitability for parasites with complex life cycles is essential to further our understanding of how disease systems respond to environmental changes, and to make spatial predictions of their future distributions. However, the limited availability of high quality occurrence data with high spatial resolution often constrains these investigations. Using 449 reliable occurrence records for Echinococcus multilocularis from across Europe published over the last 35 years, we modelled habitat suitability for this parasite, the aetiological agent of alveolar echinococcosis, in order to describe its environmental niche, predict its current and future distribution under three global change scenarios, and quantify the probability of occurrence for each European country. Using a machine learning approach, we developed large-scale (25 × 25 km) species distribution models based on seven sets of predictors, each set representing a distinct biological hypothesis supported by current knowledge of the autecology of the parasite. The best-supported hypothesis included climatic, orographic and land-use/land-cover variables such as the temperature of the coldest quarter, forest cover, urban cover and the precipitation seasonality. Future projections suggested the appearance of highly suitable areas for E. multilocularis towards northern latitudes and in the whole Alpine region under all scenarios, while decreases in habitat suitability were predicted for central Europe. Our spatially explicit predictions of habitat suitability shed light on the complex responses of parasites to ongoing global changes.     

Predicting invasions in Australia by a Neotropical shrub under climate change: the challenge of novel climates and parameter estimation

Aim  To test how well species distributions and abundance can be predicted following invasion and climate change when using only species distribution and abundance data to estimate parameters.
Location  Models were developed for the species' native range in the Americas and applied to Australia.
Methods  We developed a predictive model for an invasive neotropical shrub ( Parkinsonia aculeata) using a popular ecophysiological bioclimatic modelling technique (CLIMEX) fitted against distribution and abundance data in the Americas. The effect of uncertainty in model parameter estimates on predictions in Australia was tested. Alternative data sources were used when model predictions were sensitive to uncertainty in parameter estimates. The resulting best-fit model was run under two climate change scenarios.
Results  Of the 19 parameters used, 9 could not be fitted using data from the native range. However, only parameters that lowered temperature or increased moisture requirements for growth noticeably altered the model prediction in Australia. Differences in predictions were dramatic, and reflect climates in Australia that were not represented in the Americas (novel climates). However, these poorly fitted parameters could be fitted post hoc using alternative data sources prior to predicting responses to climate change.
Conclusions  Novel climates prevented the development of a predictive model which relied only on native-range distribution and abundance data because certain parameters could not be fitted. In fact, predictions were more sensitive to parameter uncertainty than to climate change scenarios. Where uncertainty in parameter estimates affected predictions, it could be addressed through the inclusion of alternative data sources. However, this may not always be possible, for example in the absence of post-invasion data.     

Climate controls the distribution of a widespread invasive species: implications for future range expansion

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Conservation of Chinese Theaceae species under future climate and land use changes

Aim

Climate and land use changes are two major pervasive and growing global causes of rapid changes in the distribution patterns of biodiversity, challenging the future effectiveness of protected areas (PAs), which were mainly designed based on a static view of biodiversity. Therefore, evaluating the effectiveness of protected areas for protecting the species threatened by climate and land use change is critical for future biodiversity conservation.

Location

China.

Methods

Here, using distributions of 200 Chinese Theaceae species and ensemble species distribution models, we identified species threatened by future climate and land use change (i.e. species with predicted loss of suitable habitat ≥30%) under scenarios incorporating climate change, land use change and dispersal. We then estimate the richness distribution patterns of threatened species and identify priority conservation areas and conservation gaps of the current PA network.

Results

Our results suggest that 36.30%–51.85% of Theaceae species will be threatened by future climate and land use conditions and that although the threatened species are mainly distributed at low latitudes in China under both current and future periods, the mean richness of the threatened species per grid cell will decline by 0.826–3.188 species by the 2070s. Moreover, we found that these priority conservation areas are highly fragmented and that the current PA network only covers 14.21%–20.87% of the ‘areas worth exploring’ and 6.91%–7.91% of the ‘areas worth attention’.

Main Conclusions

Our findings highlight the necessity of establishing new protected areas and ecological corridors in priority conservation areas to protect the threatened species. Moreover, our findings also highlight the importance of taking into consideration the potential threatened species under future climate and land use conditions when designating priority areas for biodiversity conservation.     

Predicting the potential global distribution of Ageratina adenophora under current and future climate change scenarios

AimInvasive alien species (IAS) threaten ecosystems and humans worldwide, and future climate change may accelerate the expansion of IAS. Predicting the suitable areas of IAS can prevent their further expansion. Ageratina adenophora is an invasive weed over 30 countries in tropical and subtropical regions. However, the potential suitable areas of A. adenophora remain unclear along with its response to climate change. This study explored and mapped the current and future potential suitable areas of Ageratina adenophora.LocationGlobal.TaxaAsteraceae A. adenophora (Spreng.) R.M.King & H.Rob. Commonly known as Crofton weed.MethodsBased on A. adenophora occurrence data and climate data, we predicted its suitable areas of this weed under current and future (four RCPs in 2050 and 2070) by MaxEnt model. We used ArcGIS 10.4 to explore the potential suitable area distribution characteristics of this weed and the “ecospat” package in R to analyze its altitudinal distribution changes.ResultsThe area under the curve (AUC) value (>0.9) and true skill statistics (TSS) value (>0.8) indicated excelled model performance. Among environment factors, mean temperature of coldest quarter contributed most to the model. Globally, the suitable areas for A. adenophora invasion decreased under climate change scenarios, although regional increases were observed, including in six biodiversity hotspot regions. The potential suitable areas of A. adenophora under climate change would expand in regions with higher elevation (3,000–3,500 m).Main conclusionsMean temperature of coldest quarter was the most important variable influencing the potential suitable area of A. Adenophora. Under the background of a warming climate, the potential suitable area of A. adenophora will shrink globally but increase in six biodiversity hotspot regions. The potential suitable area of A. adenophora would expand at higher elevation (3,000–3,500 m) under climate change. Mountain ecosystems are of special concern as they are rich in biodiversity and sensitive to climate change, and increasing human activities provide more opportunities for IAS invasion.     

Indiana bat summer maternity distribution: effects of current and future climates

Temperate zone bats may be more sensitive to climate change than other groups of mammals because many aspects of their ecology are closely linked to temperature. However, few studies have tried to predict the responses of bats to climate change. The Indiana bat (Myotis sodalis) is a federally listed endangered species that is found in the eastern United States. The northerly distribution of Indiana bat summer maternity colonies relative to their winter distributions suggests that warmer climates may result in a shift in their summer distribution. Our objectives were to determine the climatic factors associated with Indiana bat maternity range and forecast changes in the amount and distribution of the range under future climates. We used Maxent to model the suitable climatic habitat of Indiana bats under current conditions and four future climate forecasts for 2021–30, 2031–40, 2041–50, and 2051–60. Average maximum temperature across the maternity season (May–August) was the most important variable in the model of current distribution of Indiana bat maternity colonies with suitability decreasing considerably above 28ºC. The areal extent of the summer maternity distribution of Indiana bats was forecasted to decline and be concentrated in the northeastern United States and Appalachian Mountains; the western part of the current maternity range (Missouri, Iowa, Illinois, Kentucky, Indiana, and Ohio) was forecasted to become climatically unsuitable under most future climates. Our models suggest that high temperatures may be a factor in roost‐site selection at the regional scale and in the future, may also be an important variable at the microhabitat scale. When behavioral changes fail to mitigate the effects of high temperature, range shifts are likely to occur. Thus, habitat management for Indiana bat maternity colonies in the northeastern United States and Appalachian Mountains of the Southeast is critical as these areas will most likely serve as climatic refugia.     

不同气候条件下沙冬青属植物在我国的潜在分布——基于生态位模型预测

沙冬青属（Ammopiptanthus）植物是古地中海第三纪孑遗濒危物种,包括沙冬青（Ammopiptanthus mgolicus）和矮沙冬青（Ammopiptanthus nanus）,主要分布在我国西北干旱、半干旱地区,其不仅具有较高的研究价值,同时对我国西北干旱地区生态环境具有十分重要的作用。近年来由于全球气候变化及人为干扰等因素,沙冬青属植物天然分布面积骤缩,濒临灭绝。本研究利用MaxEnt模型、Bioclim模型和Domain模型对沙冬青属植物在我国末次间冰期（Last Interglacial）、末次冰盛期（Last Glacial Maximum）、当代和2050年（RCP4.5和RCP8.5）4个时期气候情景下的潜在适生区进行预测。结果表明：MaxEnt模型对沙冬青属植物潜在分布区的预测具有极高的准确度,所有模型的平均受试者工作特征曲线下面积（AUC测试值）均高于0.80。当代沙冬青最佳及高适生区占全国总面积的2.78%,主要集中在内蒙古中部、宁夏北部和甘肃北部等地;未来沙冬青最佳及高适生区在现有分布范围呈现向外扩张的趋势,主要分布在内蒙古鄂托克旗、鄂尔多斯、阿拉善左旗、宁夏吴忠和甘肃民勒县等地。当代矮沙冬青最佳及高适生区占全国总面积的2.23%,主要集中在新疆南部;未来矮沙冬青最佳及高适生区向新疆乌恰县南部、乌鲁木齐北部移动和扩大,主要分布在新疆乌恰县、乌苏市、吐鲁番市和乌鲁木齐市。未来2050年（RCP4.5和RCP8.5）两种气候情景下沙冬青和矮沙冬青的潜在分布总面积均有所增加,与当代相比变化不明显,但不同适生等级的潜在分布面积变化较大,在更高的CO₂排放量（RCP8.5）情景下沙冬青和矮冬青的最佳及高适生区范围的预测结果都将减少。从气候因素角度考虑,研究表明未来气候情景下沙冬青属植物的适生区变化过程中,年均温（Bio1）、最湿月降水量（Bio13）和温度季节性变化（Bio4）是影响沙冬青属植物分布的关键因子,并为我国西北干旱半干旱地区具有重要的经济价值并将持续其生态服务功能。     

Modelling both dominance and species distribution provides a more complete picture of changes to mangrove ecosystems under climate change

Dominant species influence the composition and abundance of other species present in ecosystems. However, forecasts of distributional change under future climates have predominantly focused on changes in species distribution and ignored possible changes in spatial and temporal patterns of dominance. We develop forecasts of spatial changes for the distribution of species dominance, defined in terms of basal area, and for species occurrence, in response to sea level rise for three tree taxa within an extensive mangrove ecosystem in northern Australia. Three new metrics are provided, indicating the area expected to be suitable under future conditions (E_occupied), the instability of suitable area (E_instability) and the overlap between the current and future spatial distribution (E_overlap). The current dominance and occurrence were modelled in relation to a set of environmental variables using boosted regression tree (BRT) models, under two scenarios of seedling establishment: unrestricted and highly restricted. While forecasts of spatial change were qualitatively similar for species occurrence and dominance, the models of species dominance exhibited higher metrics of model fit and predictive performance, and the spatial pattern of future dominance was less similar to the current pattern than was the case for the distributions of species occurrence. This highlights the possibility of greater changes in the spatial patterning of mangrove tree species dominance under future sea level rise. Under the restricted seedling establishment scenario, the area occupied by or dominated by a species declined between 42.1% and 93.8%, while for unrestricted seedling establishment, the area suitable for dominance or occurrence of each species varied from a decline of 68.4% to an expansion of 99.5%. As changes in the spatial patterning of dominance are likely to cause a cascade of effects throughout the ecosystem, forecasting spatial changes in dominance provides new and complementary information in addition to that provided by forecasts of species occurrence.     

Identifying historical and future global change drivers that place species recovery at risk

Ecosystem management in the face of global change requires understanding how co-occurring threats affect species and communities. Such an understanding allows for effective management strategies to be identified and implemented. An important component of this is differentiating between factors that are within (e.g. invasive predators) or outside (e.g. drought, large wildfires) of a local manager's control. In the global biodiversity hotspot of south-western Australia, small- and medium-sized mammal species are severely affected by anthropogenic threats and environmental disturbances, including invasive predators, fire, and declining rainfall. However, the relative importance of different drivers has not been quantified. We used data from a long-term monitoring program to fit Bayesian state-space models that estimated spatial and temporal changes in the relative abundance of four threatened mammal species: the woylie (Bettongia penicillata), chuditch (Dasyurus geoffroii), koomal (Trichosurus vulpecula) and quenda (Isoodon fusciventor). We then use Bayesian structural equation modelling to identify the direct and indirect drivers of population changes, and scenario analysis to forecast population responses to future environmental change. We found that habitat loss or conversion and reduced primary productivity (caused by rainfall declines) had greater effects on species' spatial and temporal population change than the range of fire and invasive predator (the red fox Vulpes vulpes) management actions observed in the study area. Scenario analysis revealed that a greater extent of severe fire and further rainfall declines predicted under climate change, operating in concert are likely to further reduce the abundance of these species, but may be mitigated partially by invasive predator control. Considering both historical and future drivers of population change is necessary to identify the factors that risk species recovery. Given that both anthropogenic pressures and environmental disturbances can undermine conservation efforts, managers must consider how the relative benefit of conservation actions will be shaped by ongoing global change.     

Predicting range shifts of Davidia involucrata Ball. under future climate change

Understanding and predicting how species will respond to climate change is crucial for biodiversity conservation. Here, we assessed future climate change impacts on the distribution of a rare and endangered plant species, Davidia involucrate in China, using the most recent global circulation models developed in the sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC6). We assessed the potential range shifts in this species by using an ensemble of species distribution models (SDMs). The ensemble SDMs exhibited high predictive ability and suggested that the temperature annual range, annual mean temperature, and precipitation of the driest month are the most influential predictors in shaping distribution patterns of this species. The projections of the ensemble SDMs also suggested that D. involucrate is very vulnerable to future climate change, with at least one‐third of its suitable range expected to be lost in all future climate change scenarios and will shift to the northward of high‐latitude regions. Similarly, at least one‐fifth of the overlap area of the current nature reserve networks and projected suitable habitat is also expected to be lost. These findings suggest that it is of great importance to ensure that adaptive conservation management strategies are in place to mitigate the impacts of climate change on D. involucrate.     

Predicting the distributions of Pouteria adolfi-friederici and Prunus africana tree species under current and future climate change scenarios in Ethiopia

Distributions of potential ranges of plant species are not yet fully known in Ethiopia where high climatic variability and vegetation types are found. This study was undertaken to predict distributions of suitable habitats of Pouteria adolfi-friederici and Prunus africana under current and two future climate scenarios (RCP 4.5 and RCP 8.5 in 2050 and 2070) in Ethiopia. Eleven environmental variables with less correlation coefficients (r < 0.7) were used to make the prediction. Shifting in extents of habitat suitability and effects of elevation, solar radiation and topographic position in relation to the current and future climatic scenarios were statistically analysed using independent t-test and linear model. We found decreasing area of highly suitable habitat from 0.51% to 0.46%, 0.36% and 0.33%, 0.24% for Prunus africana and 1.13% to 1.02%, 0.77% and 0.76%, 0.60% for Pouteria adolfi-friederici, under RCP 4.5 and RCP 8.5 by 2050 and 2070 respectively. Moist and dry afromontane forests are identified as the most suitable habitat for both species. Overall, our results suggest that climate change can promote dynamic suitable habitat niches under different future climate scenarios. Therefore, biodiversity conservation strategies should take into account habitat suitability dynamics issues and identify where to conserve species before implementing conservation practices.     

A tool for simulating and communicating uncertainty when modelling species distributions under future climates

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气候变化情景下祁连圆柏在青海省的适宜分布区预测

祁连圆柏具有良好的水土保持功能,是青海省高寒干旱地区造林绿化的优良乡土树种之一,预测未来气候变化情景下祁连圆柏在青海省的潜在地理分布将为祁连圆柏的经营管理和引种栽培提供理论指导.本研究基于实地调查和资料搜集获得88个有效地理分布样点,利用Maxent模型和ArcGIS空间分析技术对当前气候条件下祁连圆柏在青海省的潜在地...