Global estimates of stress-reflecting indices reveal key climatic drivers of climate-induced forest range shifts.

Climate change has the potential to cause forest range shifts at a broad scale and consequently can alter crucial forest functions, including carbon sequestration. However, global-scale projections of future forest range shifts remain challenging because our knowledge of the physiological responses of plants to climatic stress is limited to particular species and is insufficient for wide-range projections, in addition to the uncertainties in the impacts of non-climatic factors, such as wildfire, wind, and insect outbreaks. To evaluate the vulnerability and resilience of forests to climate change, we developed a new empirical approach using climatic indices reflecting physiological stressors on plants. We calculated the global distributions of seven indices based on primary climatic stressors (drought, solar radiation, and temperature) at high resolution. We then modeled the relationship between the seven indices and global forest extent. We found two key stressors driving climate-induced forest range shifts on a global scale: low temperature under high radiation and drought. At high latitudes of the Northern Hemisphere, forest establishment became difficult when the mean temperature was less than approximately 7.2 °C in the highest radiation quarter. Forest sensitivity to drought was more pronounced at mid-latitudes. In areas where the humidity index (ratio of precipitation to potential evapotranspiration) was below 0.45, shrubland and grassland became more dominant than forests. Our results also suggested that the impacts of climate change on global forest range shifts will be geographically biased depending on the areas affected by the key climatic stressors. Potential forest gain was remarkable in boreal regions due to increasing temperature. Potential forest loss was remarkable in current tropical grassland and temperate forest/grassland ecoregions due to increasing drought. Our approach using stress-reflecting indices could improve our ability to detect the roles of climatic stressors on climate-induced forest range shifts.

Biodiversity can benefit from climate stabilization despite adverse side effects of land-based mitigation.

Limiting the magnitude of climate change via stringent greenhouse gas (GHG) mitigation is necessary to prevent further biodiversity loss. However, some strategies to mitigate GHG emission involve greater land-based mitigation efforts, which may cause biodiversity loss from land-use changes. Here we estimate how climate and land-based mitigation efforts interact with global biodiversity by using an integrated assessment model framework to project potential habitat for five major taxonomic groups. We find that stringent GHG mitigation can generally bring a net benefit to global biodiversity even if land-based mitigation is adopted. This trend is strengthened in the latter half of this century. In contrast, some regions projected to experience much growth in land-based mitigation efforts (i.e., Europe and Oceania) are expected to suffer biodiversity loss. Our results support the enactment of stringent GHG mitigation policies in terms of biodiversity. To conserve local biodiversity, however, these policies must be carefully designed in conjunction with land-use regulations and societal transformation in order to minimize the conversion of natural habitats.

Modeling future wildlife habitat suitability: serious climate change impacts on the potential distribution of the Rock Ptarmigan Lagopus muta japonica in Japan's northern Alps.

BACKGROUND: The Rock Ptarmigan Lagopus muta japonica lives in the alpine zones of central Japan, which is the southern limit of the global distribution for this species. This species is highly dependent on alpine habitats, which are considered vulnerable to rapid climate change. This study aimed to assess the impact of climate change on potential L. muta japonica habitat based on predicted changes to alpine vegetation, to identify population vulnerability under future climatic conditions for conservation planning. We developed species distribution models, which considered the structure of the alpine ecosystem by incorporating spatial hierarchy on specific environmental factors to assess the potential habitats for L. muta japonica under current and future climates. We used 24 general circulation models (GCMs) for 2081-2100 as future climate conditions. RESULTS: The predicted potential habitat for L. muta japonica was similar to the actual distribution of the territories in the study area of Japan's northern Alps (36.25-36.5°N, 137.5-137.7°E). Future potential habitat for L. muta japonica was projected to decrease to 0.4% of the current potential habitat in the median of occurrence probabilities under 24 GCMs, due to a decrease in alpine vegetation communities. Some potential habitats in the central and northwestern part of the study area were predicted to be sustained in the future, depending on the GCMs. CONCLUSIONS: Our model results predicted that the potential habitats for L. muta japonica in Japan's northern Alps, which provides core habitat for this subspecies, would be vulnerable by 2081-2100. Small sustainable habitats may serve as refugia, facilitating the survival of L. muta japonica populations under future climatic conditions. Impact assessment studies of the effect of climate change on L. muta japonica habitats at a nationwide scale are urgently required to establish effective conservation planning for this species, which includes identifying candidate areas for assisted migration as an adaptive strategy.

The effects of future nationwide forest transition to discharge in the 21st century with regard to general circulation model climate change scenarios.

Forest disturbance (or land-cover change) and climatic variability are commonly recognised as two major drivers interactively influencing hydrology in forested watersheds. Future climate changes and corresponding changes in forest type and distribution are expected to generate changes in rainfall runoff that pose a threat to river catchments. It is therefore important to understand how future climate changes will effect average rainfall distribution and temperature and what effect this will have upon forest types across Japan. Recent deforestation of the present-day coniferous forest and expected increases in evergreen forest are shown to influence runoff processes and, therefore, to influence future runoff conditions. We strongly recommend that variations in forest type be considered in future plans to ameliorate projected climate changes. This will help to improve water retention and storage capacities, enhance the flood protection function of forests, and improve human health. We qualitatively assessed future changes in runoff including the effects of variation in forest type across Japan. Four general circulation models (GCMs) were selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to provide the driving fields: the Model for Interdisciplinary Research on Climate (MIROC), the Meteorological Research Institute Atmospheric General Circulation Model (MRI-GCM), the Hadley Centre Global Environment Model (HadGEM), and the Geophysical Fluid Dynamics Laboratory (GFDL) climate model. The simulations consisted of an ensemble including multiple physics configurations and different reference concentration pathways (RCP2.6, 4.5, and 8.5), the results of which have produced monthly data sets for the whole of Japan. The impacts of future climate changes on forest type in Japan are based on the balance amongst changes in rainfall distribution, temperature and hydrological factors. Methods for assessing the impact of such changes include the Catchment Simulator modelling frameworks based on the Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO) model, which was expanded to estimate discharge by incorporating the effects of forest-type transition across the whole of Japan. The results indicated that, by the 2090s, annual runoff will increase above present-day values. Increases in annual variation in runoff by the 2090s was predicted to be around 14.1% when using the MRI-GCM data and 44.4% when using the HadGEM data. Analysis by long-term projection showed the largest increases in runoff in the 2090s were related to the type of forest, such as evergreen. Increased runoff can have negative effects on both society and the environment, including increased flooding events, worsened water quality, habitat destruction and changes to the forest moisture-retaining function. Prediction of the impacts of future climate change on water generation is crucial for effective environmental planning and management.

Land abandonment and changes in snow cover period accelerate range expansions of sika deer.

Ongoing climate change and land-use change have the potential to substantially alter the distribution of large herbivores. This may result in drastic changes in ecosystems by changing plant-herbivore interactions. Here, we developed a model explaining sika deer persistence and colonization between 25 years in terms of neighborhood occupancy and habitat suitability. We used climatic, land-use, and topographic variables to calculate the habitat suitability and evaluated the contributions of the variables to past range changes of sika deer. We used this model to predict the changes in the range of sika deer over the next 100 years under four scenario groups with the combination of land-use change and climate change. Our results showed that both climate change and land-use change had affected the range of sika deer in the past 25 years. Habitat suitability increased in northern or mountainous regions, which account for 71.6% of Japan, in line with a decrease in the snow cover period. Habitat suitability decreased in suburban areas, which account for 28.4% of Japan, corresponding to land-use changes related to urbanization. In the next 100 years, the decrease in snow cover period and the increase in land abandonment were predicted to accelerate the range expansion of sika deer. Comparison of these two driving factors revealed that climate change will contribute more to range expansion, particularly from the 2070s onward. In scenarios that assumed the influence of both climate change and land-use change, the total sika deer range increased by between +4.6% and +11.9% from the baseline scenario. Climate change and land-use change will require additional efforts for future management of sika deer, particularly in the long term.

Multivariate forecasts of potential distributions of invasive plant species.

The fact that plant invasions are an ongoing process makes generalizations of invasive spread extraordinarily challenging. This is particularly true given the idiosyncratic nature of invasions, in which both historical and local conditions affect establishment success and hinder our ability to generate guidelines for early detection and eradication of invasive species. To overcome these limitations we have implemented a comprehensive approach that examines plant invasions at three spatial scales: regional, landscape, and local levels. At each scale, in combination with the others, we have evaluated the role of key environmental variables such as climate, landscape structure, habitat type, and canopy closure in the spread of three commonly found invasive woody plant species in New England, Berberis thunbergii, Celastrus orbiculatus, and Euonymus alatus. We developed a spatially explicit hierarchical Bayesian model that allowed us to take into account the ongoing nature of the spread of invasive species and to incorporate presence/absence data from the species' native ranges as well as from the invaded regions. Comparisons between predictions from climate-only models with those from the multiscale forecasts emphasize the importance of including landscape structure in our models of invasive species' potential distributions. In addition, predictions generated using only native range data performed substantially worse than those that incorporated data from the target range. This points out important limitations in extrapolating distributional ranges from one region to another.