Where might the western Svalbard tundra be vulnerable to pink‐footed goose (Anser brachyrhynchus) population expansion? Clues from species distribution models

Pink-footed geese ( Anser brachyrhynchus ) breed in the Arctic, where their populations have doubled since the 1980s. There is concern that nesting geese disturb the fragile tundra and lead to a trophic cascade with strong top-down effects on vegetation and soil processes. A better understanding of the distribution of geese and factors that influence nest site selection is needed to highlight potential problem areas and assess the potential for further population expansion. To help infer the importance of environmental variables on nest site selection, we built generalized additive models using nest observations collected in 2003 and 2004 from the Sassendalen valley, Svalbard, along with a suite of geographical information system explanatory predictors. The fit of the models was very high (explaining over 72% of the deviance), and predictive power to independent samples indicated useful predictions that could discriminate between presences and absence of nests very well (area under the receiver operating characteristic curves exceeded 0.88). Significant predictors of nest site selection included elevation, slope, aspect, percentage of snow cover, percentage of foraging habitat cover, and a spatial autocovariate. Spatial predictions were applied to the broader Nordenskiöldsland region of Svalbard and highlighted the importance of previously unsurveyed locations for nesting.     

Modelling pink-footed goose (Anser brachyrhynchus) wintering distributions for the year 2050: potential effects of land-use change in Europe

Feeding on farmland by overwintering populations of pink-footed geese ( Anser brachyrhynchus ) conflicts with agricultural interests in Northern Europe. In order to forecast the potential future of this conflict, we used generalized linear models to relate the presence and absence of pink-footed geese to variables describing the contemporary landscape, and predicted their future distributions in relation to two land-use scenarios for the year 2050. One future scenario represented a global, economically orientated world (A1) and the other represented a regional, environmentally concerned world (B2). The probability of goose occurrence increased within cropland and grassland, and could be explained by their proximity to coast, elevation, and the degree of habitat closure. Predictions to future scenarios revealed noticeable shifts in the suitability of goose habitat evident at the local and regional scale in response to future shifts in land use. In particular, as grasslands and croplands give way to unsuitable land-use types (e.g. woody biofuel crops, increased urbanization, and forest) under both future scenarios, our models predicted a decrease in habitat suitability for geese. If coupled with continued goose population expansion, we expect that the agricultural conflict will intensify under the A1 and particularly the B2 scenarios.     

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.     

气候速度影响下中国周边海域鲨鱼保护优先区识别

鲨鱼在气候变化和人类活动等因素的影响下面临着种群衰退的风险,开展鲨鱼保护优先区研究是鲨鱼保护行动的重要工作.将气候速度引入鲨鱼保护优先区的识别过程,旨在阐明中国周边海域鲨鱼现状保护成效和保护空缺,并预测气候速度影响下的鲨鱼保护优先区空间格局及其变化趋势.以集成物种分布模型模拟的146种鲨鱼栖息地作为保护对象,以2015年至2100年两种气候变化情景下的气候速度作为保护的机会成本,基于系统保护规划理论模拟现状和未来情景下的鲨鱼保护优先区选址方案.研究结果表明：（1）长江口以南至台湾海峡和北部湾近岸海域为鲨鱼多样性分布的主要区域,台湾海峡区域亦为珍稀濒危物种的重要分布区;（2）在两种气候情景下,南海中南部将面临较高的气候变化风险,而长江口以南至珠江口的近岸海域气候速度均相对较低,提示了这些区域或能成为气候变化影响下的生物避难所;（3）现有保护区仅保护了1.33%的海域和不到4%的鲨鱼物种,尚存在较大的保护空缺.当保护海域比例提升至10%时,可覆盖绝大多数鲨鱼物种.而当比例提升至30%时,珍稀濒危物种的栖息地将得到有效保护;（4）气候变化影响下保护优先区选址将发生不同程度的变化,尤其是在中国南海区域,如在保护规划时兼顾气候速度,可在满足相似保护目标的前提下减少保护优先区内25%以上的气候压力,以使其具有较强的应对气候变化潜力。     

Survival of Svalbard pink-footed geese Anser brachyrhynchus in relation to winter climate, density and land-use

1. Global change may strongly affect population dynamics, but mechanisms remain elusive. Several Arctic goose species have increased considerably during the last decades. Climate, and land-use changes outside the breeding area have been invoked as causes but have not been tested. We analysed the relationships between conditions on wintering and migration staging areas, and survival in Svalbard pink-footed geese Anser brachyrhynchus. Using mark-recapture data from 14 winters (1989-2002) we estimated survival rates and tested for time trends, and effects of climate, goose density and land-use. 2. Resighting rates differed for males and females, were higher for birds recorded during the previous winter and changed smoothly over time. Survival rates did not differ between sexes, varied over time with a nonsignificant negative trend, and were higher for the first interval after marking (0.88-0.97) than afterwards (0.74-0.93). Average survival estimates were 0.967 (SE 0.026) for the first and 0.861 (SE 0.023) for all later survival intervals. 3. We combined 16 winter and spring climate covariates into two principal components axes. F1 was related to warm/wet winters and an early spring on the Norwegian staging areas and F2 to dry/cold winters. We expected that F1 would be positively related to survival and F2 negatively. F1 explained 23% of survival variation (F1,10=3.24; one-sided P=0.051) when alone in a model and 28% (F1,9=4.50; one-sided P=0.031) in a model that assumed a trend for survival. In contrast, neither F2 nor density, land-use, or scaring practices on important Norwegian spring staging areas had discernible effects on survival. 4. Climate change may thus affect goose population dynamics, with warmer winters and earlier springs enhancing survival and fecundity. A possible mechanism is increased food availability on Danish wintering and Norwegian staging areas. As geese are among the main herbivores in Arctic ecosystems, climate change, by increasing goose populations, may have important indirect effects on Arctic vegetation. Our study also highlights the importance of events outside the breeding area for the population dynamics of migrant species.     

Mutualism influences species distribution predictions for a bromeliad‐breeding anuran under climate change

Ecological niche models, or species distribution models, have been widely used to identify potentially suitable areas for species in future climate change scenarios. However, there are inherent errors to these models due to their inability to evaluate species occurrence influenced by non‐climatic factors. With the intuit to improve the modelling predictions for a bromeliad‐breeding treefrog (Phyllodytes melanomystax, Hylidae), we investigate how the climatic suitability of bromeliads influences the distribution model for the treefrog in the context of baseline and 2050 climate change scenarios. We used point occurrence data on the frog and the bromeliad (Vriesea procera, Bromeliaceae) to generate their predicted distributions based on baseline and 2050 climates. Using a consensus of five algorithms, we compared the accuracy of the models and the geographic predictions for the frog generated from two modelling procedures: (i) a climate‐only model for P. melanomystax and V. procera; and (ii) a climate‐biotic model for P. melanomystax, in which the climatic suitability of the bromeliad was jointly considered with the climatic variables. Both modelling approaches generated strong and similar predictive power for P. melanomystax, yet climate‐biotic modelling generated more concise predictions, particularly for the year 2050. Specifically, because the predicted area of the bromeliad overlaps with the predictions for the treefrog in the baseline climate, both modelling approaches produce reasonable similar predicted areas for the anuran. Alternatively, due to the predicted loss of northern climatically suitable areas for the bromeliad by 2050, only the climate‐biotic models provide evidence that northern populations of P. melanomystax will likely be negatively affected by 2050.     

The future of terrestrial mammals in the Mediterranean basin under climate change

The Mediterranean basin is considered a hotspot of biological diversity with a long history of modification of natural ecosystems by human activities, and is one of the regions that will face extensive changes in climate. For 181 terrestrial mammals (68% of all Mediterranean mammals), we used an ensemble forecasting approach to model the future (approx. 2100) potential distribution under climate change considering five climate change model outputs for two climate scenarios. Overall, a substantial number of Mediterranean mammals will be severely threatened by future climate change, particularly endemic species. Moreover, we found important changes in potential species richness owing to climate change, with some areas (e.g. montane region in central Italy) gaining species, while most of the region will be losing species (mainly Spain and North Africa). Existing protected areas (PAs) will probably be strongly influenced by climate change, with most PAs in Africa, the Middle East and Spain losing a substantial number of species, and those PAs gaining species (e.g. central Italy and southern France) will experience a substantial shift in species composition.     

MigClim: Predicting plant distribution and dispersal in a changing climate

Aim Many studies have forecasted the possible impact of climate change on plant distributions using models based on ecological niche theory, but most of them have ignored dispersal‐limitations, assuming dispersal to be either unlimited or null. Depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under‐ or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of ‘potentially suitable’ and ‘potentially colonizable’ habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed Mig Clim, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. Mig Clim implements various parameters, such as dispersal distance, increase in reproductive potential over time, landscape fragmentation or long‐distance dispersal. Location Western Swiss Alps. Methods Using our Mig Clim model, several simulations were run for two virtual species by varying dispersal distance and other parameters. Each simulation covered the 100‐year period 2001–2100 and three different IPCC‐based temperature warming scenarios were considered. Results of dispersal‐limited projections were compared with unlimited and no‐dispersal projections. Results Our simulations indicate that: (1) using realistic parameter values, the future potential distributions generated using Mig Clim can differ significantly (up to more than 95% difference in colonized surface) from those that ignore dispersal; (2) this divergence increases under more extreme climate warming scenarios and over longer time periods; and (3) the uncertainty associated with the warming scenario can be as large as the one related to dispersal parameters. Main conclusions Accounting for dispersal, even roughly, can importantly reduce uncertainty in projections of species distribution under climate change scenarios.     

The ability of climate envelope models to predict the effect of climate change on species distributions

Climate envelope models (CEMs) have been used to predict the distribution of species under current, past, and future climatic conditions by inferring a species' environmental requirements from localities where it is currently known to occur. CEMs can be evaluated for their ability to predict current species distributions but it is unclear whether models that are successful in predicting current distributions are equally successful in predicting distributions under different climates (i.e. different regions or time periods). We evaluated the ability of CEMs to predict species distributions under different climates by comparing their predictions with those obtained with a mechanistic model (MM). In an MM the distribution of a species is modeled based on knowledge of a species' physiology. The potential distributions of 100 plant species were modeled with an MM for current conditions, a past climate reconstruction (21 000 years before present) and a future climate projection (double preindustrial CO₂ conditions). Point localities extracted from the currently suitable area according to the MM were used to predict current, future, and past distributions with four CEMs covering a broad range of statistical approaches: Bioclim (percentile distributions), Domain (distance metric), GAM (general additive modeling), and Maxent (maximum entropy). Domain performed very poorly, strongly underestimating range sizes for past or future conditions. Maxent and GAM performed as well under current climates as under past and future climates. Bioclim slightly underestimated range sizes but the predicted ranges overlapped more with the ranges predicted with the MM than those predicted with GAM did. Ranges predicted with Maxent overlapped most with those produced with the MMs, but compared with the ranges predicted with GAM they were more variable and sometimes much too large. Our results suggest that some CEMs can indeed be used to predict species distributions under climate change, but individual modeling approaches should be validated for this purpose, and model choice could be made dependent on the purpose of a particular study.     

Protecting endemic seed plants on the Tibetan Plateau under future climate change: migration matters

Aims Climate change in the near future may become a major threat to high-altitude endemics by greatly altering their distribution. Our aims are to (i) assess the potential impacts of future climate change on the diversity and distribution of seed plants endemic to the Tibetan Plateau and (ii) evaluate the conservation effectiveness of the current National Nature Reserves (NNRs) in protecting the endemic plants in the face of climate change.     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

Inconsistent interspecific and intraspecific differentiation of climate envelopes in a subtropical tree

Aims Comparisons of climate envelopes among species have shown that niche conservatism tends to break down over time. Here, we use the Asian tree genusPlatycarya(Juglandaceae) as a case study to test this tendency at relatively short timescales in a single lineage. This, together with a reanalysis of the extant literature, should help evaluate prospects of using correlations between climate and species occurrence data to infer evolutionary processes.