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Aim Interest in species distribution models (SDMs) and related niche studies has increased dramatically in recent years, with several books and reviews being prepared since 2000. The earliest SDM studies are dealt with only briefly even in the books. Consequently, many researchers are unaware of when the first SDM software package (bioclim) was developed and how a broad range of applications using the package was explored within the first 8 years following its release. The purpose of this study is to clarify these early developments and initial applications, as well as to highlight bioclim's continuing relevance to current studies. Location Mainly Australia and New Zealand, but also some global applications. Methods We outline the development of the bioclim package, early applications (1984–1991) and its current relevance. Results bioclim was the first SDM package to be widely used. Early applications explored many of the possible uses of SDMs in conservation biogeography, such as quantifying the environmental niche of species, identifying areas where a species might be invasive, assisting conservation planning and assessing the likely impacts of climate change on species distributions. Main conclusions Understanding this pioneering work is worthwhile as bioclim was for many years one of the leading SDM packages and remains widely used. Climate interpolation methods developed for bioclim were used to create the WorldClim database, the most common source of climate data for SDM studies, and bioclim variables are used in about 76% of recent published MaxEnt analyses of terrestrial ecosystems. Also, some of the bioclim studies from the late 1980s, such as measuring niche (both realized and fundamental) and assessing possible impacts of climate change, are still highly relevant to key conservation biogeography issues.

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Nascent peatlands represent an emerging, nature‐based carbon sink in the global climate system. A warming climate and changing precipitation regime could drive peat initiation beyond the current latitudinal and altitudinal boundaries of the peatland bioclimatic envelope, through increases in plant productivity and moisture availability, with potential implications for global radiative forcing. However, contemporaneous observations of new peat formation remain scarce. We investigate peat initiation within the deglaciating Rob Roy valley in the Southern Alps, Aotearoa/New Zealand. We find that montane peats have developed across the head of the valley since ∼1949 C.E., coinciding with regional climate warming and glacial retreat. Further, we identify a common ecological succession, characterized by a rise in brown mosses (mainly Bryum) beginning around ∼1963 C.E. Our findings indicate the potential for wider peat expansion in increasingly warm and wet montane landscapes. However, further bioclimatic modeling is required to elucidate where future peatland developments may occur.

Human-aided movement of species populations in large-scale reforestation programs could be a potent and cost-effective climate change adaptation strategy. Such large-scale management interventions, however, tend to entail the risks of unintended consequences, and we propose that three conditions should be met before implementing assisted migration in reforestation programs: (1) evidence of a climate-related adaptational lag, (2) observed biological impacts, and (3) robust model projections to target assisted migration efforts. In a case study of aspen ( Populus tremuloides Michaux.) we use reciprocal transplant experiments to study adaptation of tree populations to local environments. Second, we monitor natural aspen populations using the MODIS enhanced vegetation index as a proxy for forest health and productivity. Last, we report results from bioclimate envelope models that predict suitable habitat for locally adapted genotypes under observed and predicted climate change. The combined results support assisted migration prescriptions and indicate that the risk of inaction likely exceeds the risk associated with changing established management practices. However, uncertainty in model projections also implies that we are restricted to a relatively short 20-year planning horizon for prescribing seed movement in reforestation programs. We believe that this study exemplifies a safe and realistic climate change adaptation strategy based on multiple sources of information and some understanding of the uncertainty associated with recommendations for assisted migration. Ad hoc migration prescriptions without a similar level of supporting information should be avoided in reforestation programs.

Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. The Western Cape of South Africa. We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy-guiding applications along with a full appreciation of uncertainty.

Aim Ocean warming has been observed in a number of marine ecosystems and is believed to influence marine species in many ways, such as through changes in distribution range and abundance. In this study, we investigated the potential impacts of climate change on the distribution and maximum catch potential of 34 warm water fishes from 2000 to 2060. Location Yellow Sea, China. Methods We used a dynamic bioclimate envelope model under the RCP2.6 and RCP8.5 scenarios with Earth system models, including GFDL, IPSL, MPI and their ensemble average, to predict current species distributions and their relative abundance and project future species distributions and maximum catch potential (MCP). Results are subsequently summarized by indices such latitudinal centroid (LC) and mean temperature of relative abundance (MTRA). Results Our results showed that the 34 warm water fish species in the Yellow Sea will likely shift to lower latitude regions under future climate change scenarios. In particular, the average LC in the Earth system models of GFDL, IPSL and MPI from 1970 to 2060 is projected to shift at rate of −2.96 ± 1.29 (SE) and −3.20 ± 1.94 (SE) km per decade under the RCP2.6 and RCP8.5 scenarios, respectively. In addition, the corresponding maximum catch potential is decreased under the above climate change scenarios. The projected changes in the distribution may have major ecological and socio‐economic importance as well as implications for invasive species management, marine ranching construction and shifts in fishing grounds. Main conclusions The projected distribution of 34 warm water fish species in the Yellow Sea shifted to lower latitudes from 2000 to 2060 following both RCP scenarios and Earth system models. This result is contrary to the projections of previous studies suggesting that fish species can shift to higher latitudes or deeper waters under increased temperature scenarios. This difference might be due to the semi‐enclosed shelf sea of Yellow Sea, which is commonly influenced by the fluctuation of the coastal current, the warm current, the cold water mass and overfishing.

Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species' bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent context.

1. Climate change has been inducing range shifts for many species as they follow their suitable climate space and further shifts are projected. Whether species will be able to colonize regions where climate conditions become suitable, so-called 'new climate space', depends on species traits and habitat fragmentation. 2. By combining bioclimate envelope models with dispersal models, we identified areas where the spatial cohesion of the ecosystem pattern is expected to be insufficient to allow colonization of new climate space. 3. For each of three ecosystem types, three species were selected that showed a shift in suitable climate space and differed in habitat fragmentation sensitivity. 4. For the 2020 and 2050 time slices, the amount of climatically suitable habitat in northwest Europe diminished for all studied species. Additionally, significant portions of new suitable habitat could not be colonized because of isolation. Together, this will result in a decline in the amount of suitable habitat protected in Natura 2000 sites. 5. We develop several adaptation strategies to combat this problem: (i) link isolated habitat that is within a new suitable climate zone to the nearest climate-proof network; (ii) increase colonizing capacity in the overlap zone, the part of a network that remains suitable in successive time frames; (iii) optimize sustainable networks in climate refugia, the part of a species' range where the climate remains stable. 6. Synthesis and applications. Following the method described in this study, we can identify those sites across Europe where ecosystem patterns are not cohesive enough to accommodate species' responses to climate change. The best locations for climate corridors where improving connectivity is most urgent and potential gain is highest can then be pinpointed.

Species distribution models, linked to climate projections, are widely used in extinction-risk assessment and conservation planning. However, the degree of confidence that we can place on future climate-change projections depends on global climate-model performance and involves uncertainties that need to be assessed rigorously via climate-model evaluation. Performance assessments are important because the choice of climate model influences projections of species' range movement and extinction risk. A consensus view from the climate modeling community is that no single climate model is superior in its ability to forecast key climatic features. Despite this, the advantages of using multi-model ensemble-averaged climate forecasts to account for climate-model uncertainties have not been recognized by ecologists. Here we propose a method to use a range of skill and convergence metrics to rank commonly used atmosphere-ocean general circulation models (AOGCMs) according to their skill in reproducing 20-year observed patterns of regional and global climates of interest, and to assess their consistency with other AOGCMs. By eliminating poorly performing models and averaging the remainder with equal weights, we show how downscaled annual multi-climate-model ensemble-averaged forecasts, which have a strong regional focus, can be generated. We demonstrate that: (1) model ranking (match of simulated to observed conditions) differs according to the skill metric used, as well as the climate variable and season considered; (2) although the multi-model averaged result tends to outperform single models at a global scale, at the continental scale at least some models can perform better than the multi-model average; and (3) forecasts for the Australian region, which are often based on a single AOGCM (CSIRO-3.0), show spatial patterns of change that differ noticeably from ensemble-average projections based on a subset of better-performing AOGCMs. Our suggested approach-novel in the ecology discipline-provides a straightforward, consistent, and defensible method for conservation practitioners and natural-resource managers to generate estimates of future climate change at a spatial resolution suitable for biodiversity impact studies.

Continued warming of oceans has caused global shifts in marine species distributions. This can result in changes in the spatial distribution of landings and have distributional impacts on marine resource-dependent communities. We evaluated the spatial dynamics of American lobster ( Homarus americanus ) landings in coastal Maine, which supports one of the most valuable U.S. fisheries. We coupled a bioclimate envelope model and a generalized additive model to project spatial dynamics of lobster landings under possible climate scenarios. This coupled model was then used to forecast future lobster habitat suitability based on IPCC RCP climate scenarios and predict distributions of fishery landings from this projected lobster habitat suitability. The historical spatial distribution of fishery landings shows the highest proportional landings in Maine’s Southern (southwest) regions. The current distribution of landings shows higher proportional landings in Downeast (northeast) regions with the highest proportional landings in Midcoast (middle) regions. Our results suggest that while the proportion of landings in each zone will remain stable, changes in habitat suitability in the spring and fall will reduce total landings. Future habitat suitability is projected to decrease in spring but increase in fall in Downeast areas. Downeast landings are projected to decrease in the next 30 years, then increase over the subsequent 80 years, depending on RCP scenarios and abundance regimes. Midcoast landings are projected to decrease while Southcoast landings are expected to stay constant. This study develops an approach to link climate change effects to fishery landings. These findings have long-term implications for sustainable, localized management of the Maine lobster fishery in a changing climate.