Climate change modifies risk of global biodiversity loss due to land-cover change

Climate change and land-cover change will have major impacts on biodiversity persistence worldwide. These two stressors are likely to interact, but how climate change will mediate the effects of land-cover change remains poorly understood. Here we use an empirically-derived model of the interaction between habitat loss and climate to predict the implications of this for biodiversity loss and conservation priorities at a global scale. Risk analysis was used to estimate the risk of biodiversity loss due to alternative future land-cover change scenarios and to quantify how climate change mediates this risk. We demonstrate that the interaction of climate change with land-cover change could increase the impact of land-cover change on birds and mammals by up to 43% and 24% respectively and alter the spatial distribution of threats. Additionally, we show that the ranking of global biodiversity hotspots by threat depends critically on the interaction between climate change and habitat loss. Our study suggests that the investment of conservation resources will likely change once the interaction between climate change and land-cover change is taken into account. We argue that global conservation efforts must take this into account if we are to develop cost-effective conservation policies and strategies under global change

Citizen social science for more integrative and effective climate action: A science-policy perspective

Governments are struggling to limit global temperatures below the 2°C Paris target with existing climate change policy approaches. This is because conventional climate policies have been predominantly (inter)nationally top-down, which limits citizen agency in driving policy change and influencing citizen behavior. Here we propose elevating Citizen Social Science (CSS) to a new level across governments as an advanced collaborative approach of accelerating climate action and policies that moves beyond conventional citizen science and participatory approaches. Moving beyond the traditional science-policy model of the democratization of science in enabling more inclusive climate policy change, we present examples of how CSS can potentially transform citizen behavior and enable citizens to become key agents in driving climate policy change. We also discuss the barriers that could impede the implementation of CSS and offer solutions to these. In doing this, we articulate the implications of increased citizen action through CSS in moving forward the broader normative and political program of transdisciplinary and co-productive climate change research and policy

Citizen Social Science for more integrative and effective climate action: a science-policy perspective

Governments are struggling to limit global temperatures below the 2°C Paris target with existing climate change policy approaches. This is because conventional climate policies have been predominantly (inter)nationally top-down, which limits citizen agency in driving policy change and influencing citizen behavior. Here we propose elevating Citizen Social Science (CSS) to a new level across governments as an advanced collaborative approach of accelerating climate action and policies that moves beyond conventional citizen science and participatory approaches. Moving beyond the traditional science-policy model of the democratization of science in enabling more inclusive climate policy change, we present examples of how CSS can potentially transform citizen behavior and enable citizens to become key agents in driving climate policy change. We also discuss the barriers that could impede the implementation of CSS and offer solutions to these. In doing this, we articulate the implications of increased citizen action through CSS in moving forward the broader normative and political program of transdisciplinary and co-productive climate change research and policy

Interactive effects of multiple stressors vary with consumer interactions, stressor dynamics and magnitude

Predicting the impacts of multiple stressors is important for informing ecosystem management but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively or antagonistically. Here, we use process-based models to study how interactions generalise across three levels of biological organisation (physiological, population and consumer-resource) for a two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over extended temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management

Anticipated climate and land-cover changes reveal refuge areas for Borneo's orang-utans

Habitat loss and climate change pose a double jeopardy for many threatened taxa, making the identification of optimal habitat for the future a conservation priority. Using a case study of the endangered Bornean orang-utan, we identify environmental refuges by integrating bioclimatic models with projected deforestation and oil-palm agriculture suitability from the 1950s to 2080s. We coupled a maximum entropy algorithm with information on habitat needs to predict suitable habitat for the present day and 1950s. We then projected to the 2020s, 2050s and 2080s in models incorporating only land-cover change, climate change or both processes combined. For future climate, we incorporated projections from four model and emission scenario combinations. For future land cover, we developed spatial deforestation predictions from 10 years of satellite data. Refuges were delineated as suitable forested habitats identified by all models that were also unsuitable for oil palm – a major threat to tropical biodiversity. Our analyses indicate that in 2010 up to 260 000 km2 of Borneo was suitable habitat within the core orang-utan range; an 18–24% reduction since the 1950s. Land-cover models predicted further decline of 15–30% by the 2080s. Although habitat extent under future climate conditions varied among projections, there was majority consensus, particularly in northeastern and western regions. Across projections habitat loss due to climate change alone averaged 63% by 2080, but 74% when also considering land-cover change. Refuge areas amounted to 2000–42 000 km2 depending on thresholds used, with 900–17 000 km2 outside the current species range. We demonstrate that efforts to halt deforestation could mediate some orang-utan habitat loss, but further decline of the most suitable areas is to be expected given projected changes to climate. Protected refuge areas could therefore become increasingly important for ongoing translocation efforts. We present an approach to help identify such areas for highly threatened species given environmental changes expected this century

Impacts of sea level rise and climate change on coastal plant species in the central California coast

Local increases in sea level caused by global climate change pose a significant threat to the persistence of many coastal plant species through exacerbating inundation, flooding, and erosion. In addition to sea level rise (SLR), climate changes in the form of air temperature and precipitation regimes will also alter habitats of coastal plant species. Although numerous studies have analyzed the effect of climate change on future habitats through species distribution models (SDMs), none have incorporated the threat of exposure to SLR. We developed a model that quantified the effect of both SLR and climate change on habitat for 88 rare coastal plant species in San Luis Obispo, Santa Barbara, and Ventura Counties, California, USA (an area of 23,948 km2). Our SLR model projects that by the year 2100, 60 of the 88 species will be threatened by SLR. We found that the probability of being threatened by SLR strongly correlates with a species’ area, elevation, and distance from the coast, and that 10 species could lose their entire current habitat in the study region. We modeled the habitat suitability of these 10 species under future climate using a species distribution model (SDM). Our SDM projects that 4 of the 10 species will lose all suitable current habitats in the region as a result of climate change. While SLR accounts for up to 9.2 km2 loss in habitat, climate change accounts for habitat suitability changes ranging from a loss of 1,439 km2 for one species to a gain of 9,795 km2 for another species. For three species, SLR is projected to reduce future suitable area by as much as 28% of total area. This suggests that while SLR poses a higher risk, climate changes in precipitation and air temperature represents a lesser known but potentially larger risk and a small cumulative effect from both

The interplay of climate and land use change affects the distribution of EU bumblebees

Bumblebees in Europe have been in steady decline since the 1900s. This decline is expected to continue with climate change as the main driver. However, at the local scale, land use and land cover (LULC) change strongly affects the occurrence of bumblebees. At present, LULC change is rarely included in models of future distributions of species. This study's objective is to compare the roles of dynamic LULC change and climate change on the projected distribution patterns of 48 European bumblebee species for three change scenarios until 2100 at the scales of Europe, and Belgium, Netherlands and Luxembourg (BENELUX). We compared three types of models: (1) only climate covariates, (2) climate and static LULC covariates and (3) climate and dynamic LULC covariates. The climate and LULC change scenarios used in the models include, extreme growth applied strategy (GRAS), business as might be usual and sustainable European development goals. We analysed model performance, range gain/loss and the shift in range limits for all bumblebees. Overall, model performance improved with the introduction of LULC covariates. Dynamic models projected less range loss and gain than climate-only projections, and greater range loss and gain than static models. Overall, there is considerable variation in species responses and effects were most pronounced at the BENELUX scale. The majority of species were predicted to lose considerable range, particularly under the extreme growth scenario (GRAS; overall mean: 64% +/- 34). Model simulations project a number of local extinctions and considerable range loss at the BENELUX scale (overall mean: 56% +/- 39). Therefore, we recommend species-specific modelling to understand how LULC and climate interact in future modelling. The efficacy of dynamic LULC change should improve with higher thematic and spatial resolution. Nevertheless, current broad scale representations of change in major land use classes impact modelled future distribution patterns.Peer reviewe

Hindcasting the impacts of land-use changes on bird communities with species distribution models of Bird Atlas data

Habitat loss and degradation induced by human development are among the major threats to biodiversity worldwide. In this study, we tested our ability to predict the response of bird communities (128 species) to land-use changes in southern Quebec (~483,100 km2) over the last 30 yr (between 1984–1989 and 2010–2014) by using species distribution models (299,302 occurrences in 30,408 locations) from a hindcasting perspective. Results were grouped by functional guilds to infer potential impacts on ecosystem services, and to relate model transferability (i.e., ability of our models to be generalized to other times and scales) to specific functional and life-history traits. Overall, our models were able to accurately predict, both in space and time, habitat suitability for 69% of species, especially for granivorous, nonmigrant, tree-nesting species, and species that are tied to agricultural areas under intensive use. These findings indicate that model transferability depends upon specific functional and life-history traits, providing further evidence that species’ ecologies affect the ability of models to accurately predict bird distributions. Declining bird species were mostly short-distance migrants that were associated with open habitats (agricultural and nonproductive forest) with aerial insectivorous or granivorous diets, which may be related to agricultural intensification and land abandonment. Land-use changes were positive for some forest bird species that were mainly associated with mixed and deciduous forests, generalist diets and tree-nesting strategies. Yet cavity-nesting birds have suffered substantial reductions in their distributions, suggesting that cumulative effects of intensive logging and wildfires on mature forests pose a threat for forest-specialist species. Habitat suitability changes predicted by our coarse-scale species distribution models partially agreed with the long-term trends reported by the North American Breeding Bird Survey. Our findings confirm land-use change as a key driving force for shaping bird communities in southern Quebec, together with the need to explicitly incorporate it into global change scenarios that better inform decision-makers on conservation and management