Hotspots of uncertainty in land‐use and land‐cover change projections: a global‐scale model comparison

Model‐based global projections of future land‐use and land‐cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global‐scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.     

Biodiversity scenarios neglect future land‐use changes

Efficient management of biodiversity requires a forward‐looking approach based on scenarios that explore biodiversity changes under future environmental conditions. A number of ecological models have been proposed over the last decades to develop these biodiversity scenarios. Novel modelling approaches with strong theoretical foundation now offer the possibility to integrate key ecological and evolutionary processes that shape species distribution and community structure. Although biodiversity is affected by multiple threats, most studies addressing the effects of future environmental changes on biodiversity focus on a single threat only. We examined the studies published during the last 25 years that developed scenarios to predict future biodiversity changes based on climate, land‐use and land‐cover change projections. We found that biodiversity scenarios mostly focus on the future impacts of climate change and largely neglect changes in land use and land cover. The emphasis on climate change impacts has increased over time and has now reached a maximum. Yet, the direct destruction and degradation of habitats through land‐use and land‐cover changes are among the most significant and immediate threats to biodiversity. We argue that the current state of integration between ecological and land system sciences is leading to biased estimation of actual risks and therefore constrains the implementation of forward‐looking policy responses to biodiversity decline. We suggest research directions at the crossroads between ecological and environmental sciences to face the challenge of developing interoperable and plausible projections of future environmental changes and to anticipate the full range of their potential impacts on biodiversity. An intergovernmental platform is needed to stimulate such collaborative research efforts and to emphasize the societal and political relevance of taking up this challenge.     

Choice of baseline climate data impacts projected species' responses to climate change

Climate data created from historic climate observations are integral to most assessments of potential climate change impacts, and frequently comprise the baseline period used to infer species‐climate relationships. They are often also central to downscaling coarse resolution climate simulations from General Circulation Models (GCMs) to project future climate scenarios at ecologically relevant spatial scales. Uncertainty in these baseline data can be large, particularly where weather observations are sparse and climate dynamics are complex (e.g. over mountainous or coastal regions). Yet, importantly, this uncertainty is almost universally overlooked when assessing potential responses of species to climate change. Here, we assessed the importance of historic baseline climate uncertainty for projections of species' responses to future climate change. We built species distribution models (SDMs) for 895 African bird species of conservation concern, using six different climate baselines. We projected these models to two future periods (2040–2069, 2070–2099), using downscaled climate projections, and calculated species turnover and changes in species‐specific climate suitability. We found that the choice of baseline climate data constituted an important source of uncertainty in projections of both species turnover and species‐specific climate suitability, often comparable with, or more important than, uncertainty arising from the choice of GCM. Importantly, the relative contribution of these factors to projection uncertainty varied spatially. Moreover, when projecting SDMs to sites of biodiversity importance (Important Bird and Biodiversity Areas), these uncertainties altered site‐level impacts, which could affect conservation prioritization. Our results highlight that projections of species' responses to climate change are sensitive to uncertainty in the baseline climatology. We recommend that this should be considered routinely in such analyses.     

Land-cover data improve bioclimatic models for anurans and turtles at a regional scale

Aim We investigated whether accounting for land cover could improve bioclimatic models for eight species of anurans and three species of turtles at a regional scale. We then tested whether accounting for spatial autocorrelation could significantly improve bioclimatic models after statistically controlling for the effects of land cover. Location Nova Scotia, eastern Canada. Methods Species distribution data were taken from a recent (1999–2003) herpetofaunal atlas. Generalized linear models were used to relate the presence or absence of each species to climate and land‐cover variables at a 10‐km resolution. We then accounted for spatial autocorrelation using an autocovariate or third‐order trend surface of the geographical coordinates of each grid square. Finally, variance partitioning was used to explore the independent and joint contributions of climate, land cover and spatial autocorrelation. Results The inclusion of land cover significantly increased the explanatory power of bioclimatic models for 10 of the 11 species. Furthermore, including land cover significantly increased predictive performance for eight of the 11 species. Accounting for spatial autocorrelation improved model fit for rare species but generally did not improve prediction success. Variance partitioning demonstrated that this lack of improvement was a result of the high correlation between climate and trend‐surface variables. Main conclusions The results of this study suggest that accounting for the effects of land cover can significantly improve the explanatory and predictive power of bioclimatic models for anurans and turtles at a regional scale. We argue that the integration of climate and land‐cover data is likely to produce more accurate spatial predictions of contemporary herpetofaunal diversity. However, the use of land‐cover simulations in climate‐induced range‐shift projections introduces additional uncertainty into the predictions of bioclimatic models. Further research is therefore needed to determine whether accounting for the effects of land cover in range‐shift projections is merited.     

Impacts of Land Cover Data Selection and Trait Parameterisation on Dynamic Modelling of Species’ Range Expansion

Dynamic models for range expansion provide a promising tool for assessing species’ capacity to respond to climate change by shifting their ranges to new areas. However, these models include a number of uncertainties which may affect how successfully they can be applied to climate change oriented conservation planning. We used RangeShifter, a novel dynamic and individual-based modelling platform, to study two potential sources of such uncertainties: the selection of land cover data and the parameterization of key life-history traits. As an example, we modelled the range expansion dynamics of two butterfly species, one habitat specialist (Maniola jurtina) and one generalist (Issoria lathonia). Our results show that projections of total population size, number of occupied grid cells and the mean maximal latitudinal range shift were all clearly dependent on the choice made between using CORINE land cover data vs. using more detailed grassland data from three alternative national databases. Range expansion was also sensitive to the parameterization of the four considered life-history traits (magnitude and probability of long-distance dispersal events, population growth rate and carrying capacity), with carrying capacity and magnitude of long-distance dispersal showing the strongest effect. Our results highlight the sensitivity of dynamic species population models to the selection of existing land cover data and to uncertainty in the model parameters and indicate that these need to be carefully evaluated before the models are applied to conservation planning.     

What can and can't we say about indirect land‐use change in Brazil using an integrated economic – land‐use change model?

It is commonly recognized that large uncertainties exist in modelled biofuel‐induced indirect land‐use change, but until now, spatially explicit quantification of such uncertainties by means of error propagation modelling has never been performed. In this study, we demonstrate a general methodology to stochastically calculate direct and indirect land‐use change (dLUC and iLUC) caused by an increasing demand for biofuels, with an integrated economic – land‐use change model. We use the global Computable General Equilibrium model MAGNET, connected to the spatially explicit land‐use change model PLUC. We quantify important uncertainties in the modelling chain. Next, dLUC and iLUC projections for Brazil up to 2030 at different spatial scales and the uncertainty herein are assessed. Our results show that cell‐based (5 × 5 km²) probabilities of dLUC range from 0 to 0.77, and of iLUC from 0 to 0.43, indicating that it is difficult to project exactly where dLUC and iLUC will occur, with more difficulties for iLUC than for dLUC. At country level, dLUC area can be projected with high certainty, having a coefficient of variation (cv) of only 0.02, while iLUC area is still uncertain, having a cv of 0.72. The latter means that, considering the 95% confidence interval, the iLUC area in Brazil might be 2.4 times as high or as low as the projected mean. Because this confidence interval is so wide that it is likely to straddle any legislation threshold, our opinion is that threshold evaluation for iLUC indicators should not be implemented in legislation. For future studies, we emphasize the need for provision of quantitative uncertainty estimates together with the calculated LUC indicators, to allow users to evaluate the reliability of these indicators and the effects of their uncertainty on the impacts of land‐use change, such as greenhouse gas emissions.     

Land‐use change outweighs projected effects of changing rainfall on tree cover in sub‐Saharan Africa

Global change will likely affect savanna and forest structure and distributions, with implications for diversity within both biomes. Few studies have examined the impacts of both expected precipitation and land use changes on vegetation structure in the future, despite their likely severity. Here, we modeled tree cover in sub‐Saharan Africa, as a proxy for vegetation structure and land cover change, using climatic, edaphic, and anthropic data (R² = 0.97). Projected tree cover for the year 2070, simulated using scenarios that include climate and land use projections, generally decreased, both in forest and savanna, although the directionality of changes varied locally. The main driver of tree cover changes was land use change; the effects of precipitation change were minor by comparison. Interestingly, carbon emissions mitigation via increasing biofuels production resulted in decreases in tree cover, more severe than scenarios with more intense precipitation change, especially within savannas. Evaluation of tree cover change against protected area extent at the WWF Ecoregion scale suggested areas of high biodiversity and ecosystem services concern. Those forests most vulnerable to large decreases in tree cover were also highly protected, potentially buffering the effects of global change. Meanwhile, savannas, especially where they immediately bordered forests (e.g. West and Central Africa), were characterized by a dearth of protected areas, making them highly vulnerable. Savanna must become an explicit policy priority in the face of climate and land use change if conservation and livelihoods are to remain viable into the next century.     

An empirical test of the relative and combined effects of land‐cover and climate change on local colonization and extinction

Land‐cover and climate change are two main drivers of changes in species ranges. Yet, the majority of studies investigating the impacts of global change on biodiversity focus on one global change driver and usually use simulations to project biodiversity responses to future conditions. We conduct an empirical test of the relative and combined effects of land‐cover and climate change on species occurrence changes. Specifically, we examine whether observed local colonization and extinctions of North American birds between 1981–1985 and 2001–2005 are correlated with land‐cover and climate change and whether bird life history and ecological traits explain interspecific variation in observed occurrence changes. We fit logistic regression models to test the impact of physical land‐cover change, changes in net primary productivity, winter precipitation, mean summer temperature, and mean winter temperature on the probability of Ontario breeding bird local colonization and extinction. Models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local colonization for 30%, 27%, and 29% of species, respectively. Conversely, models with climate change, land‐cover change, and the combination of these two drivers were the top ranked models of local extinction for 61%, 7%, and 9% of species, respectively. The quantitative impacts of land‐cover and climate change variables also vary among bird species. We then fit linear regression models to test whether the variation in regional colonization and extinction rate could be explained by mean body mass, migratory strategy, and habitat preference of birds. Overall, species traits were weakly correlated with heterogeneity in species occurrence changes. We provide empirical evidence showing that land‐cover change, climate change, and the combination of multiple global change drivers can differentially explain observed species local colonization and extinction.     

Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates

Africa is predicted to be highly vulnerable to 21st century climatic changes. Assessing the impacts of these changes on Africa's biodiversity is, however, plagued by uncertainties, and markedly different results can be obtained from alternative bioclimatic envelope models or future climate projections. Using an ensemble forecasting framework, we examine projections of future shifts in climatic suitability, and their methodological uncertainties, for over 2500 species of mammals, birds, amphibians and snakes in sub‐Saharan Africa. To summarize a priori the variability in the ensemble of 17 general circulation models, we introduce a consensus methodology that combines co‐varying models. Thus, we quantify and map the relative contribution to uncertainty of seven bioclimatic envelope models, three multi‐model climate projections and three emissions scenarios, and explore the resulting variability in species turnover estimates. We show that bioclimatic envelope models contribute most to variability, particularly in projected novel climatic conditions over Sahelian and southern Saharan Africa. To summarize agreements among projections from the bioclimatic envelope models we compare five consensus methodologies, which generally increase or retain projection accuracy and provide consistent estimates of species turnover. Variability from emissions scenarios increases towards late‐century and affects southern regions of high species turnover centred in arid Namibia. Twofold differences in median species turnover across the study area emerge among alternative climate projections and emissions scenarios. Our ensemble of projections underscores the potential bias when using a single algorithm or climate projection for Africa, and provides a cautious first approximation of the potential exposure of sub‐Saharan African vertebrates to climatic changes. The future use and further development of bioclimatic envelope modelling will hinge on the interpretation of results in the light of methodological as well as biological uncertainties. Here, we provide a framework to address methodological uncertainties and contextualize results.     

The fate of Amazonian ecosystems over the coming century arising from changes in climate,atmospheric CO2, and land use

There is considerable interest in understanding the fate of the Amazon over the coming century in the face of climate change, rising atmospheric CO₂ levels, ongoing land transformation, and changing fire regimes within the region. In this analysis, we explore the fate of Amazonian ecosystems under the combined impact of these four environmental forcings using three terrestrial biosphere models (ED2, IBIS, and JULES) forced by three bias‐corrected IPCC AR4 climate projections (PCM1, CCSM3, and HadCM3) under two land‐use change scenarios. We assess the relative roles of climate change, CO₂ fertilization, land‐use change, and fire in driving the projected changes in Amazonian biomass and forest extent. Our results indicate that the impacts of climate change are primarily determined by the direction and severity of projected changes in regional precipitation: under the driest climate projection, climate change alone is predicted to reduce Amazonian forest cover by an average of 14%. However, the models predict that CO₂ fertilization will enhance vegetation productivity and alleviate climate‐induced increases in plant water stress, and, as a result, sustain high biomass forests, even under the driest climate scenario. Land‐use change and climate‐driven changes in fire frequency are predicted to cause additional aboveground biomass loss and reductions in forest extent. The relative impact of land use and fire dynamics compared to climate and CO₂ impacts varies considerably, depending on both the climate and land‐use scenario, and on the terrestrial biosphere model used, highlighting the importance of improved quantitative understanding of all four factors – climate change, CO₂ fertilization effects, fire, and land use – to the fate of the Amazon over the coming century.     

Contribution of crop model structure,parameters and climate projections to uncertainty in climate change impact assessments

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple‐ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple‐ensemble probabilistic assessment, the median of simulated yield change was ?4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981–2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple‐ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources.     

Defining the importance of including transient ecosystem responses to simulate C‐cycle dynamics in a global change model

The ability of plant species to migrate is one of the critical issues in assessing accurately the future response of the terrestrial biosphere to climate change. This ability is confined by both natural and human‐induced changes in land cover. In this paper we present land‐cover and Carbon (C) cycle models designed to simulate the biospheric consequences of different types of land‐cover changes. These models, imbedded in the larger integrated assessment model IMAGE 2, were used to demonstrate the importance of considering spatial aspects for global C‐cycle modelling. A gradual‐migration, an unlimited‐migration and a no‐migration case were compared to show the range of possible consequences. Major differences between these cases were simulated for land‐cover patterns and the carbon budget. A large geographical variation in the biospheric response was also simulated. The strongest response was simulated in high‐latitude regions, especially for the migration cases in which land‐cover changes were permitted. In low‐latitudes regions the differences between the migration cases were smaller, mainly due to the effects of land‐use changes. The geographical variation among, and the different responses, the migration cases clearly demonstrate how essential it is to assess biospheric responses to climate change and land use simultaneously. Moreover, it also shows the urgent need for enhanced understanding of spatial and temporal dynamics of the biospheric responses.     

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 north‐eastern 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.     

Using model‐data fusion to interpret past trends,and quantify uncertainties in future projections,of terrestrial ecosystem carbon cycling

Uncertainties in model projections of carbon cycling in terrestrial ecosystems stem from inaccurate parameterization of incorporated processes (endogenous uncertainties) and processes or drivers that are not accounted for by the model (exogenous uncertainties). Here, we assess endogenous and exogenous uncertainties using a model‐data fusion framework benchmarked with an artificial neural network (ANN). We used 18 years of eddy‐covariance carbon flux data from the Harvard forest, where ecosystem carbon uptake has doubled over the measurement period, along with 15 ancillary ecological data sets relative to the carbon cycle. We test the ability of combinations of diverse data to constrain projections of a process‐based carbon cycle model, both against the measured decadal trend and under future long‐term climate change. The use of high‐frequency eddy‐covariance data alone is shown to be insufficient to constrain model projections at the annual or longer time step. Future projections of carbon cycling under climate change in particular are shown to be highly dependent on the data used to constrain the model. Endogenous uncertainties in long‐term model projections of future carbon stocks and fluxes were greatly reduced by the use of aggregated flux budgets in conjunction with ancillary data sets. The data‐informed model, however, poorly reproduced interannual variability in net ecosystem carbon exchange and biomass increments and did not reproduce the long‐term trend. Furthermore, we use the model‐data fusion framework, and the ANN, to show that the long‐term doubling of the rate of carbon uptake at Harvard forest cannot be explained by meteorological drivers, and is driven by changes during the growing season. By integrating all available data with the model‐data fusion framework, we show that the observed trend can only be reproduced with temporal changes in model parameters. Together, the results show that exogenous uncertainty dominates uncertainty in future projections from a data‐informed process‐based model.     

Potential impacts of future land use and climate change on the Red List status of the Proteaceae in the Cape Floristic Region, South Africa

Using spatial predictions of future threats to biodiversity, we assessed for the first time the relative potential impacts of future land use and climate change on the threat status of plant species. We thus estimated how many taxa could be affected by future threats that are usually not included in current IUCN Red List assessments. Here, we computed the Red List status including future threats of 227 Proteaceae taxa endemic to the Cape Floristic Region, South Africa, and compared this with their Red List status excluding future threats. We developed eight different land use and climate change scenarios for the year 2020, providing a range of best‐ to worst‐case scenarios. Four scenarios include only the effects of future land use change, while the other four also include the impacts of projected anthropogenic climate change (HadCM2 IS92a GGa), using niche‐based models. Up to a third of the 227 Proteaceae taxa are uplisted (become more threatened) by up to three threat categories if future threats as predicted for 2020 are included, and the proportion of threatened Proteaceae taxa rises on average by 9% (range 2–16%), depending on the scenario. With increasing severity of the scenarios, the proportion of Critically Endangered taxa increases from about 1% to 7% and almost 2% of the 227 Proteaceae taxa become Extinct because of climate change. Overall, climate change has the most severe effects on the Proteaceae, but land use change also severely affects some taxa. Most of the threatened taxa occur in low‐lying coastal areas, but the proportion of threatened taxa changes considerably in inland mountain areas if future threats are included. Our approach gives important insights into how, where and when future threats could affect species persistence and can in a sense be seen as a test of the value of planned interventions for conservation.     

Modelling changes in species’ abundance in response to projected climate change

Aim Existing climate envelope models give an indication of broad scale shifts in distribution, but do not specifically provide information on likely future population changes useful for conservation prioritization and planning. We demonstrate how these techniques can be developed to model likely future changes in absolute density and population size as a result of climate change. Location Great Britain. Methods Generalized linear models were used to model breeding densities of two northerly‐ and two southerly‐distributed bird species as a function of climate and land use. Models were built using count data from extensive national bird monitoring data and incorporated detectability to estimate absolute abundance. Projections of likely future changes in the distribution and abundance of these species were made by applying these models to projections of future climate change under two emissions scenarios. Results Models described current spatial variation in abundance for three of the four species and produced modelled current estimates of national populations that were similar to previously published estimates for all species. Climate change was projected to result in national population declines in the two northerly‐distributed species, with declines for Eurasian curlew Numenius arquata projected to be particularly severe. Conversely, the abundances of the two southerly distributed species were projected to increase nationally. Projected maps of future abundance may be used to identify priority areas for the future conservation of each species. Main conclusions The analytical methods provide a framework to make projections of impacts of climate change on species abundance, rather than simply projected range changes. Outputs may be summarized at any spatial scale, providing information to inform future conservation planning at national, regional and local scales. Results suggest that as a consequence of climate change, northerly distributed bird species in Great Britain are likely to become an increasingly high conservation priority within the UK.     

Modelling biome shifts and tree cover change for 2050 in West Africa

Aim Africa is expected to face severe changes in climatic conditions. Our objectives are: (1) to model trends and the extent of future biome shifts that may occur by 2050, (2) to model a trend in tree cover change, while accounting for human impact, and (3) to evaluate uncertainty in future climate projections. Location West Africa. Methods We modelled the potential future spatial distribution of desert, grassland, savanna, deciduous and evergreen forest in West Africa using six bioclimatic models. Future tree cover change was analysed with generalized additive models (GAMs). We used climate data from 17 general circulation models (GCMs) and included human population density and fire intensity to model tree cover. Consensus projections were derived via weighted averages to: (1) reduce inter‐model variability, and (2) describe trends extracted from different GCM projections. Results The strongest predicted effect of climate change was on desert and grasslands, where the bioclimatic envelope of grassland is projected to expand into the desert by an area of 2 million km². While savannas are predicted to contract in the south (by 54 ± 22 × 10⁴ km²), deciduous and evergreen forest biomes are expected to expand (64 ± 13 × 10⁴ km² and 77 ± 26 × 10⁴ km²). However, uncertainty due to different GCMs was particularly high for the grassland and the evergreen biome shift. Increasing tree cover (1–10%) was projected for large parts of Benin, Burkina Faso, Côte d’Ivoire, Ghana and Togo, but a decrease was projected for coastal areas (1–20%). Furthermore, human impact negatively affected tree cover and partly changed the direction of the projected change from increase to decrease. Main conclusions Considering climate change alone, the model results of potential vegetation (biomes) show a ‘greening’ trend by 2050. However, the modelled effects of human impact suggest future forest degradation. Thus, it is essential to consider both climate change and human impact in order to generate realistic future tree cover projections.     

Disentangling the potential effects of land‐use and climate change on stream conditions

Land‐use and climate change are significantly affecting stream ecosystems, yet understanding of their long‐term impacts is hindered by the few studies that have simultaneously investigated their interaction and high variability among future projections. We modeled possible effects of a suite of 2030, 2060, and 2090 land‐use and climate scenarios on the condition of 70,772 small streams in the Chesapeake Bay watershed, United States. The Chesapeake Basin‐wide Index of Biotic Integrity, a benthic macroinvertebrate multimetric index, was used to represent stream condition. Land‐use scenarios included four Special Report on Emissions Scenarios (A1B, A2, B1, and B2) representing a range of potential landscape futures. Future climate scenarios included quartiles of future climate changes from downscaled Coupled Model Intercomparison Project ‐ Phase 5 (CMIP5) and a watershed‐wide uniform scenario (Lynch2016). We employed random forests analysis to model individual and combined effects of land‐use and climate change on stream conditions. Individual scenarios suggest that by 2090, watershed‐wide conditions may exhibit anywhere from large degradations (e.g., scenarios A1B, A2, and the CMIP5 25th percentile) to small degradations (e.g., scenarios B1, B2, and Lynch2016). Combined land‐use and climate change scenarios highlighted their interaction and predicted, by 2090, watershed‐wide degradation in 16.2% (A2 CMIP5 25th percentile) to 1.0% (B2 Lynch2016) of stream kilometers. A goal for the Chesapeake Bay watershed is to restore 10% of stream kilometers over a 2008 baseline; our results suggest meeting and sustaining this goal until 2090 may require improvement in 11.0%–26.2% of stream kilometers, dependent on land‐use and climate scenario. These results highlight inherent variability among scenarios and the resultant uncertainty of predicted conditions, which reinforces the need to incorporate multiple scenarios of both land‐use (e.g., development, agriculture, etc.) and climate change in future studies to encapsulate the range of potential future conditions.     

Uncertainty in assessing the impacts of global change with coupled dynamic species distribution and population models

Concern over rapid global changes and the potential for interactions among multiple threats are prompting scientists to combine multiple modelling approaches to understand impacts on biodiversity. A relatively recent development is the combination of species distribution models, land‐use change predictions, and dynamic population models to predict the relative and combined impacts of climate change, land‐use change, and altered disturbance regimes on species' extinction risk. Each modelling component introduces its own source of uncertainty through different parameters and assumptions, which, when combined, can result in compounded uncertainty that can have major implications for management. Although some uncertainty analyses have been conducted separately on various model components – such as climate predictions, species distribution models, land‐use change predictions, and population models – a unified sensitivity analysis comparing various sources of uncertainty in combined modelling approaches is needed to identify the most influential and problematic assumptions. We estimated the sensitivities of long‐run population predictions to different ecological assumptions and parameter settings for a rare and endangered annual plant species (Acanthomintha ilicifolia, or San Diego thornmint). Uncertainty about habitat suitability predictions, due to the choice of species distribution model, contributed most to variation in predictions about long‐run populations.     

A framework integrating physiology,dispersal and land‐use to project species ranges under climate change

To study the potential effects of climate change on species, one of the most popular approaches are species distribution models (SDMs). However, they usually fail to consider important species‐specific biological traits, such as species’ physiological capacities or dispersal ability. Furthermore, there is consensus that climate change does not influence species distributions in isolation, but together with other anthropogenic impacts such as land‐use change, even though studies investigating the relative impacts of different threats on species and their geographic ranges are still rare. Here we propose a novel integrative approach which produces refined future range projections by combining SDMs based on distribution, climate, and physiological tolerance data with empirical data on dispersal ability as well as current and future land‐use. Range projections based on different combinations of these factors show strong variation in projected range size for our study species Emberiza hortulana. Using climate and physiological data alone, strong range gains are projected. However, when we account for land‐use change and dispersal ability, future range‐gain may even turn into a future range loss. Our study highlights the importance of accounting for biological traits and processes in species distribution models and of considering the additive effects of climate and land‐use change to achieve more reliable range projections. Furthermore, with our approach we present a new tool to assess species’ vulnerability to climate change which can be easily applied to multiple species.