Multi-extent analysis of the relationship between pteridophyte species richness and climate

Aim To determine the relationship between the distribution of climate, climatic heterogeneity and pteridophyte species richness gradients in Australia, using an approach that does not assume potential relationships are spatially invariant and allows for scale effects (extent of analysis) to be explicitly examined. Location Australia, extending from 10° S to 43° S and 112° E to 153° E. Method Species richness within 50 × 50 km grid cells was determined using point distribution data. Climatic surfaces representing the distribution and availability of water and energy at 1 km and 5 km cell resolutions were obtained. Climate at the 50 km resolution of analysis was represented by their mean and standard deviation in that area. Relationships were assessed using geographically weighted linear regression at a range of spatial bandwidths to investigate scale effects. Results The parameters and the predictive strength of all models varied across space at all extents of analysis. Overall, climatic variables representing water availability were more highly correlated to pteridophyte richness gradients in Australia than those representing energy. Their variance in cells further increased the strength of the relationships in topographically heterogeneous regions. Relationships with water were strong across all extents of analysis, particularly in the tropical and subtropical parts of the continent. Water availability explained less of the variation in richness at higher latitudes. Main conclusions This study brings into question the ability of aspatial and single‐extent models, searching for a unified explanation of macro‐scaled patterns in gradients of diversity, to adequately represent reality. It showed that, across Australia, there is a positive relationship between pteridophyte species richness and water availability but the strength and nature of the relationship varies spatially with scale in a highly complex manner. The spatial variance, or actual complexity, in these relationships could not have been demonstrated had a traditional aspatial global regression approach been used. Regional scale variation in relationships may be at least as important as more general relationships for a true understanding of the distribution of broad‐scale diversity.     

Ice age climate, evolutionary constraints and diversity patterns of European dung beetles

Current climate and Pleistocene climatic changes are both known to be associated with geographical patterns of diversity. We assess their associations with the European Scarabaeinae dung beetles, a group with high dispersal ability and well-known adaptations to warm environments. By assessing spatial stationarity in climate variability since the last glacial maximum (LGM), we find that current scarab richness is related to the location of their limits of thermal tolerance during the LGM. These limits mark a strong change in their current species richness-environment relationships. Furthermore, northern scarab assemblages are nested and composed of a phylogenetically clustered subset of large-range sized generalist species, whereas southern ones are diverse and variable in composition. Our results show that species responses to current climate are limited by the evolution of assemblages that occupied relatively climatically stable areas during the Pleistocene, and by post-glacial dispersal in those that were strongly affected by glaciations.     

基于GWR模型的土地覆盖与地表温度的关系——以京津唐城市群为例

基于Landsat TM土地覆盖分类数据和MODIS地表温度数据,探讨京津唐城市群不同土地覆盖的地表温度(7日),并采用常用的普通线性回归(OLS)和地理加权回归(GWR)方法分别拟合土地覆盖比例与地表温度的关系.结果表明: 研究区不同土地覆盖类型的地表温度差异明显,人工表面(40.92±3.49 ℃)和耕地(39.74±3.74 ℃)的平均温度较高,林地(34.43±4.16 ℃)和湿地(35.42±4.33 ℃)的平均温度较低;土地覆盖比例与地表温度显著相关,且两者之间的定量关系存在空间非稳定性,地理位置以及周围环境影响的差异是空间非稳定性产生的主要原因;GWR模型的拟合结果优于OLS模型(R_GWR²>R_OLS²),并且GWR模型可以量化土地覆盖比例与地表温度两者关系的空间非稳定性特征.     

The role of climate,habitat, and species co‐occurrence as drivers of change in small mammal distributions over the past century

Species distribution models are commonly used to predict species responses to climate change. However, their usefulness in conservation planning and policy is controversial because they are difficult to validate across time and space. Here we capitalize on small mammal surveys repeated over a century in Yosemite National Park, USA, to assess accuracy of model predictions. Historical (1900–1940) climate, vegetation, and species occurrence data were used to develop single‐ and multi‐species multivariate adaptive regression spline distribution models for three species of chipmunk. Models were projected onto the current (1980–2007) environmental surface and then tested against modern field resurveys of each species. We evaluated models both within and between time periods and found that even with the inclusion of biotic predictors, climate alone is the dominant predictor explaining the distribution of the study species within a time period. However, climate was not consistently an adequate predictor of the distributional change observed in all three species across time. For two of the three species, climate alone or climate and vegetation models showed good predictive performance across time. The stability of the distribution from the past to present observed in the third species, however, was not predicted by our modeling approach. Our results demonstrate that correlative distribution models are useful in understanding species' potential responses to environmental change, but also show how changes in species‐environment correlations through time can limit the predictive performance of models.     

Expanding northward: influence of climate change,forest connectivity,and population processes on a threatened species' range shift

Species' ranges are dynamic, shifting in response to a large number of interrelated ecological and anthropogenic processes. Climate change is thought to be one of the most influential drivers of range shifts, but the effects of other confounded ecological processes are often ignored even though these processes may modify expected range responses to climate change. To determine the relative effects of climate, forest availability, connectivity, and biotic processes such as immigration and establishment, we examine range changes occurring in a species of bird, the Hooded Warbler (Wilsonia citrina). We focus predominantly on the periphery of the species' northern range in Canada but we also examine data from the entire species' range. Nesting records in southern Ontario were obtained from two breeding bird Atlases of Ontario separated by a period of 20 years (1981–1985 and 2001–2005), and the rate of range expansion was estimated by comparing the number of occupied areas in each Atlas. Twelve hypotheses of the relationship between the rate of range expansion and factors known to influence range change were examined using model‐selection techniques and a mixed modeling approach (zero‐inflated Poisson's regression). Cooler temperatures were positively related to a lack of range expansion indicating that climate constrained the species' distribution. Establishment probability (based on the number of occupied, neighboring Atlas squares) and immigration from populations to the south (estimated using independent data from the North American Breeding Bird Survey) were also important predictors of range expansion. These biotic process variables can mask the effects of forest availability and connectivity on range expansion. Expansion due to climate change may be slower in fragmented systems, but the rate of expansion will be influenced largely by biotic processes such as proximity to neighboring populations.     

The influence of non‐climate predictors at local and landscape resolutions depends on the autecology of the species

Species distribution models have come under criticism for being too simplistic for making robust future forecasts, partly because they assume that climate is the main determinant of geographical range at large spatial extents and coarse resolutions, with non‐climate predictors being important only at finer scales. We suggest that this paradigm might be obscured by species movement patterns. To explore this we used contrasting kangaroo (family Macropodidae) case studies: two species with relatively small, stable home ranges (Macropus giganteus and M. robustus) and three species with more extensive, adaptive ranging behaviour (M. antilopinus, M. fuliginosus and M. rufus). We predicted that non‐climate predictors will be most influential to model fit and predictive performance at local spatial resolution for the former species and at landscape resolution for the latter species. We compared residuals autocovariate – boosted regression tree (RAC‐BRT) model statistics with and without species‐specific non‐climate predictors (habitat, soil, fire, water and topography), at local‐ and landscape‐level spatial resolutions (5 and 50 km). As predicted, the influence of non‐climate predictors on model fit and predictive performance (compared with climate‐only models) was greater at 50 compared with 5 km resolution for M. rufus and M. fuliginosus and the opposite trend was observed for M. giganteus. The results for M. robustus and M. antilopinus were inconclusive. Also notable was the difference in inter‐scale importance of climate predictors in the presence of non‐climate predictors. In conclusion, differences in autecology, particularly relating to space use, may contribute to the importance of non‐climate predictors at a given scale, not model scale per se. Further exploration of this concept across a range of species is encouraged and findings may contribute to more effective conservation and management of species at ecologically meaningful scales.     

Impacts of past habitat loss and future climate change on the range dynamics of South African Proteaceae

Aim

To assess how habitat loss and climate change interact in affecting the range dynamics of species and to quantify how predicted range dynamics depend on demographic properties of species and the severity of environmental change.

Location

South African Cape Floristic Region.

Methods

We use data‐driven demographic models to assess the impacts of past habitat loss and future climate change on range size, range filing and abundances of eight species of woody plants (Proteaceae). The species‐specific models employ a hybrid approach that simulates population dynamics and long‐distance dispersal on top of expected spatio‐temporal dynamics of suitable habitat.

Results

Climate change was mainly predicted to reduce range size and range filling (because of a combination of strong habitat shifts with low migration ability). In contrast, habitat loss mostly decreased mean local abundance. For most species and response measures, the combination of habitat loss and climate change had the most severe effect. Yet, this combined effect was mostly smaller than expected from adding or multiplying effects of the individual environmental drivers. This seems to be because climate change shifts suitable habitats to regions less affected by habitat loss. Interspecific variation in range size responses depended mostly on the severity of environmental change, whereas responses in range filling and local abundance depended mostly on demographic properties of species. While most surviving populations concentrated in areas that remain climatically suitable, refugia for multiple species were overestimated by simply overlying habitat models and ignoring demography.

Main conclusions

Demographic models of range dynamics can simultaneously predict the response of range size, abundance and range filling to multiple drivers of environmental change. Demographic knowledge is particularly needed to predict abundance responses and to identify areas that can serve as biodiversity refugia under climate change. These findings highlight the need for data‐driven, demographic assessments in conservation biogeography.

     

Geographic disparities and moral hazards in the predicted impacts of climate change on human populations

Aim It has been qualitatively understood for a long time that climate change will have widely varying effects on human well‐being in different regions of the world. The spatial complexities underlying our relationship to climate and the geographical disparities in human demographic change have, however, precluded the development of global indices of the predicted regional impacts of climate change on humans. Humans will be most negatively affected by climate change in regions where populations are strongly dependent on climate and favourable climatic conditions decline. Here we use the relationship between the distribution of human population density and climate as a basis to develop the first global index of predicted impacts of climate change on human populations. Location Global. Methods We use spatially explicit models of the present relationship between human population density and climate along with forecasted climate change to predict climate vulnerabilities over the coming decades. We then globally represent regional disparities in human population dynamics estimated with our ecological niche model and with a demographic forecast and contrast these disparities with CO₂ emissions data to quantitatively evaluate the notion of moral hazard in climate change policies. Results Strongly negative impacts of climate change are predicted in Central America, central South America, the Arabian Peninsula, Southeast Asia and much of Africa. Importantly, the regions of greatest vulnerability are generally distant from the high‐latitude regions where the magnitude of climate change will be greatest. Furthermore, populations contributing the most to greenhouse gas emissions on a per capita basis are unlikely to experience the worst impacts of climate change, satisfying the conditions for a moral hazard in climate change policies. Main conclusions Regionalized analysis of relationships between distribution of human population density and climate provides a novel framework for developing global indices of human vulnerability to climate change. The predicted consequences of climate change on human populations are correlated with the factors causing climate change at the regional level, providing quantitative support for many qualitative statements found in international climate change assessments.     

Impacts of global change on species distributions: obstacles and solutions to integrate climate and land use

Aim The impact of multiple stressors on biodiversity is one of the most pressing questions in ecology and biodiversity conservation. Here we critically assess how often and efficiently two main drivers of global change have been simultaneously integrated into research, with the aim of providing practical solutions for better integration in the future. We focus on the integration of climate change (CC) and land‐use change (LUC) when studying changes in species distributions. Location Global. Methods We analysed the peer‐reviewed literature on the effects of CC and LUC on observed changes in species distributions, i.e. including species range and abundance, between 2000 and 2014. Results Studies integrating CC and LUC remain extremely scarce, which hampers our ability to develop appropriate conservation strategies. The lack of CC–LUC integration is likely to be a result of insufficient recognition of the co‐occurrence of CC and LUC at all scales, covariation and interactions between CC and LUC, as well as correlations between species thermal and habitat requirements. Practical guidelines for the study of these interactive effects include considering multiple drivers and processes when designing studies, using available long‐term datasets on multiple drivers, revisiting single‐driver studies with additional drivers or conducting comparative studies and meta‐analyses. Combining various methodological approaches, including time lags and adaptation processes, represent further avenues to improve global change science. Main conclusions Despite repeated claims for a better integration of multiple drivers, the effects of CC and LUC on species distributions and abundances have been mostly studied in isolation, which calls for a shift of standards towards more integrative global change science. The guidelines proposed here will encourage study designs that account for multiple drivers and improve our understanding of synergies or antagonisms among drivers.     

Differential effects of past climate warming on mountain and flatland species distributions: a multispecies North American mammal assessment

Aim The magnitude of predicted range shifts during climate change is likely to be different for species living in mountainous environments compared with those living in flatland environments. The southern edges of ranges in mountain species may not shift northwards during warming as populations instead migrate up available elevational gradients; overall latitudinal range appears therefore to expand. In contrast, flatland species should shift range centroids northwards but not expand or contract their latitudinal range extent. These hypotheses were tested utilizing Late Pleistocene and modern occurrence data. Location North America. Methods The location and elevation of modern and Late Pleistocene species occurrences were collected from data bases for 26 species living in mountain or flatland environments. Regressions of elevation change over latitude, and southern and northern range edges were calculated for each species for modern and fossil data sets. A combination of regressions and anova s were used to test whether flatland species shift range edges and latitudinal extents more than mountain species do. Results Flatland species had significantly larger northward shifts at southern range edges than did mountain‐dwelling species from the Late Pleistocene to the present. There was also a significant negative correlation between the amount of change in the latitude of the southern edge of the range and the amount of elevational shifting from the Late Pleistocene to the present. Although significant, only c. 25% of the variance could be explained by this relationship. In addition, there was a weak indication that overall range expansion was less in flatland‐dwelling than in mountain‐dwelling species. Main conclusions The approach used here was to examine past species’ range responses to warming that occurred after the last ice ages as a means to better predict potential future responses to continued warming. The results confirm predictions of differential southern edge and overall range shifts for species occupying mountain and flatland regions in North America. The findings may be broadly applicable in other regions, thus allowing better modelling of future range and distribution related responses.     

Spatial and management factors associated with exposure of smallholder dairy cattle in Tanzania to tick-borne pathogens

A cross-sectional study of serum antibody responses of cattle to tick-borne pathogens (Theileria parva, Theileria mutans,Anaplasma marginale, Babesia bigemina and Babesia bovis) was conducted on smallholder dairy farms in Tanga and Iringa Regions of Tanzania. Seroprevalence was highest for T. parva (48% in Iringa and 23% in Tanga) and B. bigemina (43% in Iringa and 27% in Tanga) and lowest for B. bovis (12% in Iringa and 6% in Tanga). We use spatial and non-spatial models, fitted using classical and Bayesian methods, to explore risk factors associated with seroprevalence. These include both fixed effects (age, grazing history and breeding status) and random effects (farm and local spatial effects). In both regions, seroprevalence for all tick-borne pathogens increased significantly with age. Animals pasture grazed in the 3 months prior to the start of the sampling period were significantly more likely to be seropositive for Theileria spp. and Babesia spp. Pasture grazed animals were more likely to be seropositive than zero-grazed animals for A. marginale, but the relationship was weaker than that observed for the other four pathogens. This study did not detect any significant differences in seroprevalence associated with other management-related variables, including the method or frequency of acaricide application. After adjusting for age, there was weak evidence of localised (<5 km) spatial correlation in exposure to some of the tick borne diseases. However, this was small compared with the 'farm-effect', suggesting that risk factors specific to the farm were more important than those common to the local neighbourhood. Many animals were seropositive for more than one pathogen and the correlation between exposure to the different pathogens remained after adjusting for the identified risk factors. Identifying the determinants of exposure to multiple tick-borne pathogens and characterizing local variation in risk will assist in the development of more effective control strategies for smallholder dairy farms.     

Beyond climate control on species range: The importance of soil data to predict distribution of Amazonian plant species

Aim

To evaluate the relative importance of climatic versus soil data when predicting species distributions for Amazonian plants and to gain understanding of potential range shifts under climate change.

Location

Amazon rain forest.

Methods

We produced species distribution models (SDM) at 5‐km spatial resolution for 42 plant species (trees, palms, lianas, monocot herbs and ferns) using species occurrence data from herbarium records and plot‐based inventories. We modelled species distribution with Bayesian logistic regression using either climate data only, soil data only or climate and soil data together to estimate their relative predictive powers. For areas defined as unsuitable to species occurrence, we mapped the difference between the suitability predictions obtained with climate‐only versus soil‐only models to identify regions where climate and soil might restrict species ranges independently or jointly.

Results

For 40 out of the 42 species, the best models included both climate and soil predictors. The models including only soil predictors performed better than the models including only climate predictors, but we still detected a drought‐sensitive response for most of the species. Edaphic conditions were predicted to restrict species occurrence in the centre, the north‐west and in the north‐east of Amazonia, while the climatic conditions were identified as the restricting factor in the eastern Amazonia, at the border of Roraima and Venezuela and in the Andean foothills.

Main conclusions

Our results revealed that soil data are a more important predictor than climate of plant species range in Amazonia. The strong control of species ranges by edaphic features might reduce species’ abilities to track suitable climate conditions under a drought‐increase scenario. Future challenges are to improve the quality of soil data and couple them with process‐based models to better predict species range dynamics under climate change.     

Spatial variation of climatic and non-climatic controls on species distribution: the range limit of Tsuga heterophylla

Aim To assess which climatic variables control the distribution of western hemlock (Tsuga heterophylla), how climatic controls vary over latitude and between disjunct coastal and interior sub‐distributions, and whether non‐climatic factors, such as dispersal limitation and interspecific competition, affect range limits in areas of low climatic control. Location North‐western North America. Methods We compared four bioclimatic variables [actual evapotranspiration (AET), water deficit (DEF), mean temperature of the coldest month (MTCO), and growing degree‐days (GDD5)] with the distribution of T. heterophylla at a 2‐km grid cell resolution. The distribution is based on a zonal ecosystem classification where T. heterophylla is the dominant late‐successional species. For each bioclimatic variable and at each degree of latitude, we calculated the threshold that best defines the T. heterophylla distribution and assessed the extent to which T. heterophylla was segregated to one end of the bioclimatic gradient. We also fitted two forms of multivariate bioclimatic models to predict the T. heterophylla distribution: a simple threshold model and a complex Gaussian mixture model. Each model was trained separately on the coastal and interior distributions, and predicted areas outside of the T. heterophylla distribution (overprediction) were evaluated with respect to known outlier populations. Results Actual evapotranspiration was the most accurate predictor across the T. heterophylla distribution; other variables were important only in certain areas. There was strong latitudinal variation in the thresholds of all variables except AET, and the interior distribution had wider bioclimatic thresholds than the coastal distribution. The coastal distribution was predicted accurately by both bioclimatic models; areas of overprediction rarely occurred > 10 km from the observed distribution and generally matched small outlier populations. In contrast, the interior distribution was poorly predicted by both models; areas of overprediction occurred up to 140 km from the observed distribution and did not match outlier populations. The greatest overprediction occurred in Idaho and Montana in areas supporting species that typically co‐exist with T. heterophylla. Main conclusions The high predictive capacity of AET is consistent with this species’ physiological requirements for a mild and humid climate. Spatial variation of MTCO, GDD5 and DEF thresholds probably reflects both the correlation of these variables with AET and ecotypic variation. The level of overprediction in portions of the interior suggests that T. heterophylla has not completely expanded into its potential habitat. Tsuga heterophylla became common in the interior 2000–3500 years ago, compared with > 9000 years ago in the coastal region. The limited time for dispersal, coupled with frequent fires at the margins of the distribution and competition with disturbance‐adapted species, may have retarded range expansion in the interior. This study demonstrates that bioclimatic modelling can help identify various climatic and non‐climatic controls on species distributions.     

Continental‐scale determinants of population trends in European amphibians and reptiles

The continuous decline of biodiversity is determined by the complex and joint effects of multiple environmental drivers. Still, a large part of past global change studies reporting and explaining biodiversity trends have focused on a single driver. Therefore, we are often unable to attribute biodiversity changes to different drivers, since a multivariable design is required to disentangle joint effects and interactions. In this work, we used a meta‐regression within a Bayesian framework to analyze 843 time series of population abundance from 17 European amphibian and reptile species over the last 45 years. We investigated the relative effects of climate change, alien species, habitat availability, and habitat change in driving trends of population abundance over time, and evaluated how the importance of these factors differs across species. A large number of populations (54%) declined, but differences between species were strong, with some species showing positive trends. Populations declined more often in areas with a high number of alien species, and in areas where climate change has caused loss of suitability. Habitat features showed small variation over the last 25 years, with an average loss of suitable habitat of 0.1%/year per population. Still, a strong interaction between habitat availability and the richness of alien species indicated that the negative impact of alien species was particularly strong for populations living in landscapes with less suitable habitat. Furthermore, when excluding the two commonest species, habitat loss was the main correlate of negative population trends for the remaining species. By analyzing trends for multiple species across a broad spatial scale, we identify alien species, climate change, and habitat changes as the major drivers of European amphibian and reptile decline.     

Precipitation of the warmest quarter and temperature of the warmest month are key to understanding the effect of climate change on plant species diversity in Southern African savannah

In this study, we test for the key bioclimatic variables that significantly explain the current distribution of plant species richness in a southern African ecosystem as a preamble to predicting plant species richness under a changed climate. We used 54,000 records of georeferenced plant species data to calculate species richness and spatially interpolated climate data to derive nineteen bioclimatic variables. Next, we determined the key bioclimatic variables explaining variation in species richness across Zimbabwe using regression analysis. Our results show that two bioclimatic variables, that is, precipitation of the warmest quarter (R² = 0.92, P < 0.001) and temperature of the warmest month (R² = 0.67, P < 0.001) significantly explain variation in plant species richness. In addition, results of bioclimatic modelling using future climate change projections show a reduction in the current bio‐climatically suitable area that supports high plant species richness. However, in high‐altitude areas, plant richness is less sensitive to climate change while low‐altitude areas show high sensitivity. Our results have important implications to biodiversity conservation in areas sensitive to climate change; for example, high‐altitude areas are likely to continue being biodiversity hotspots, as such future conservation efforts should be concentrated in these areas.     

Indication of antagonistic interaction between climate change and erosion on plant species richness and soil properties in semiarid Mediterranean ecosystems

We analyzed the consequences of climate change and the increase in soil erosion, as well as their interaction on plant and soil properties in semiarid Mediterranean shrublands in Eastern Spain. Current models on drivers of biodiversity change predict an additive or synergistic interaction between drivers that will increase the negative effects of each one. We used a climatic gradient that reproduces the predicted climate changes in temperature and precipitation for the next 40 years of the wettest and coldest end of the gradient; we also compared flat areas with 20° steep hillslopes. We found that plant species richness and plant cover are negatively affected by climate change and soil erosion, which in turn negatively affects soil resistance to erosion, nutrient content and water holding capacity. We also found that plant species diversity correlates weakly with plant cover but strongly with soil properties related to fertility, water holding capacity and resistance to erosion. Conversely, these soil properties correlate weaker with plant species cover. The joint effect of climate change and soil erosion on plant species richness and soil characteristics is antagonistic. That is, the absolute magnitude of change is smaller than the sum of both effects. However, there is no interaction between climate change and soil erosion on plant cover and their effects fit the additive model. The differences in the interaction model between plant cover and species richness supports the view that several soil properties are more linked to the effect that particular plant species have on soil processes than to the quantity and quality of the plant cover and biomass they support. Our findings suggest that plant species richness is a better indicator than plant cover of ecosystems services related with soil development and protection to erosion in semiarid Mediterranean climates.