Changes in potential habitat of 147 North American breeding bird species in response to redistribution of trees and climate following predicted climate change (original) (raw)
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Climate change is being implicated in changes in forest structure and function--from species range shifts to increased forest mortality to changes in phenology. Based on historical patterns, the potential for change and even the direction of change will likely be species specific and significant . We take an empirical-statistical modeling approach using species abundance data from well recognized national inventories. For the past 15 years, we have developed and refined abundance-based habitat models utilizing the latest statistical techniques and have generated tools and summaries to explore potential changes of tree species habitats in the eastern U.S. (www.nrs.fs.fed.us/atlas). The DISTRIB model uses a statistically robust, predictive data-mining tool, RandomForest, to predict and map the potential habitat changes for 134 tree species and 147 bird species in the eastern United States. Each species is modeled individually to show current and potential future habitats according to two emission scenarios and three climate models. We produce lists of species tha t have a tendency to increase, decrease, or stay the same for any region. Because we model potential suitable habitats of species, our results should not be interpreted as actual changes in distribution of the species. Nonetheless, our models predict climate change will have large impacts on suitable habitat for many tree species, especially under a high carbon emissions trajectory. To help interpret and supplement the DISTRIB outputs for trees, we assigned modification factors for potential issues that cannot be specifically assessed with the DISTRIB model. We also use a spatially explicit cellular model, SHIFT, to calculate colonization potentials for some species, based on the abundance of the species, the distances between occupied and unoccupied cells and the fragmented nature of the landscape. By combining results from the three efforts, we aim to prepare estimates of potential climate change impacts for forest managers that can be used to aid in management decisions under climate change.
2011
We present an approach to modeling potential cli-mate-driven changes in habitat for tree and bird species in the eastern United States. First, we took an empirical-statistical modeling approach, using randomForest, with species abundance data from national inventories combined with soil, climate, and landscape variables, to build abundance-based habitat models for 134 tree and 147 bird species. We produced lists of species for which suitable habitat tends to increase, decrease, or stay the same for any region. Independent assessments of trends of large trees versus seedlings across the eastern U.S. show that 37 of 40 species in common under both studies are currently trending as modeled. We developed a framework, ModFacs, in which we used the literature to assign default modification factor scores for species characteristics that cannot be readily assessed in such models, including 12 disturbance factors (for example, drought, fire, insect pests), nine biological factors (for exampl...
Biology letters, 2012
Most studies that forecast the ecological consequences of climate change target a single species and a single life stage. Depending on climatic impacts on other life stages and on interacting species, however, the results from simple experiments may not translate into accurate predictions of future ecological change. Research needs to move beyond simple experimental studies and environmental envelope projections for single species towards identifying where ecosystem change is likely to occur and the drivers for this change. For this to happen, we advocate research directions that (i) identify the critical species within the target ecosystem, and the life stage(s) most susceptible to changing conditions and (ii) the key interactions between these species and components of their broader ecosystem. A combined approach using macroecology, experimentally derived data and modelling that incorporates energy budgets in life cycle models may identify critical abiotic conditions that disproportionately alter important ecological processes under forecasted climates.
Ecography, 2013
Correlative species distribution models are frequently used to predict species' range shifts under climate change. However, climate variables often show high collinearity and most statistical approaches require the selection of one among strongly correlated variables. When causal relationships between species presence and climate parameters are unknown, variable selection is often arbitrary, or based on predictive performance under current conditions. While this should only marginally affect current range predictions, future distributions may vary considerably when climate parameters do not change in concert. We investigated this source of uncertainty using four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe. Simulating different parameterization decisions, we generated a) four models including each of the climate variables singly, b) a model taking advantage of all variables simultaneously and c) an un-weighted average of the predictions of a). We compared model accuracy under current conditions, predicted distributions under four scenarios of climate change, and -for one species -evaluated backprojections using historical occurrence data. Although current and future variable-correlations remained constant, and the models' accuracy under contemporary conditions did not differ, future range predictions varied considerably in all climate change scenarios. Averaged models and models containing all climate variables simultaneously produced intermediate predictions; the latter, however, performed best in back-projections. This pattern, consistent across different modelling methods, indicates a benefit from including multiple climate predictors in ambiguous situations. Variable selection proved to be an important source of uncertainty for future range predictions, difficult to control using contemporary information. Small, but diverging changes of climate variables, masked by constant overall correlation patterns, can cause substantial differences between future range predictions which need to be accounted for, particularly when outcomes are intended for conservation decisions.
AIMS Environmental Science, 2017
We used 19 bioclimatic variables, five species distribution modeling (SDM) algorithms, four general circulation models, and two climate scenarios (2050 and 2070) to model nine bird species. Identified as Species of Concern (SOC), we highlighted these birds: Northern/Masked Bobwhite Quail (Colinus virginianus), Scaled Quail (Callipepla squamata), Pinyon Jay (Gymnorhinus cyanocephalus), Juniper Titmouse (Baeolophus ridgwayi), Mexican Spotted Owl (Strix occidentalis lucida), Cassin's Sparrow (Peucaea cassinii), Lesser Prairie-Chicken (Tympanuchus pallidicinctus), Montezuma Quail (Cyrtonyx montezumae), and White-tailed Ptarmigan (Lagopus leucurus). The Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, Multivariate Adaptive Regression Splines, and an ensemble model were used to identify present day core bioclimatic-envelopes for the species. We then projected future distributions of suitable climatic conditions for the species using data derived from four climate models run according to two greenhouse gas Representative Concentration Pathways (RCPs 2.6 and 8.5). Our models predicted changes in suitable bioclimatic-envelopes for all species for the years 2050 and 2070. Among the nine species of birds, the quails were found to be highly susceptible to climate change and appeared to be of most future conservation concern. The White-tailed Ptarmigan would lose about 62% of its suitable climatic habitat by 2050 and 67% by 2070. Among the species distribution models (SDMs), the Boosted Regression Tree model consistently performed fairly well based on Area Under the Curve (AUC range: 0.89 to 0.97) values. The ensemble models showed 359 AIMS Environmental Science Volume 4, Issue 2, 358-385. improved True Skill Statistics (all TSS values > 0.85) and Kappa Statistics (all K values > 0.80) for all species relative to the individual SDMs.
2011
We present an approach to modeling potential climate-driven changes in habitat for tree and bird species in the eastern United States. First, we took an empirical-statistical modeling approach, using randomForest, with species abundance data from national inventories combined with soil, climate, and landscape variables, to build abundance-based habitat models for 134 tree and 147 bird species. We produced lists of species for which suitable habitat tends to increase, decrease, or stay the same for any region. Independent assessments of trends of large trees versus seedlings across the eastern U.S. show that 37 of 40 species in common under both studies are currently trending as modeled. We developed a framework, ModFacs, in which we used the literature to assign default modification factor scores for species characteristics that cannot be readily assessed in such models, including 12 disturbance factors (for example, drought, fire, insect pests), nine biological factors (for example, dispersal, shade tolerance), and assessment scores of novel climates, long-distance extrapolations, and output variability by climate model and emission scenario. We also used a spatially explicit cellular model, SHIFT, to calculate colonization potentials for some species, based on their abundance, historic dispersal distances, and the fragmented nature of the landscape. By combining results from the three efforts, we can create projections of potential climate change impacts over the next 100 years or so. Here we emphasize some of the lessons we have learned over 16 years in hopes that they may help guide future experiments, modeling efforts, and management.
Predicting the impacts of climate change on the distribution of species: are bioclimate envelope …
Global Ecology and Biogeography
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.
The importance of biotic interactions for modelling species distributions under climate change
Global Ecology and Biogeography, 2007
Aim There is a debate as to whether biotic interactions exert a dominant role in governing species distributions at macroecological scales. The prevailing idea is that climate is the key limiting factor; thus models that use present-day climate-species range relationships are expected to provide reasonable means to quantify the impacts of climate change on species distributions. However, there is little empirical evidence that biotic interactions would not constrain species distributions at macroecological scales. We examine this idea, for the first time, and provide tests for two null hypotheses: ( H 0 1) -biotic interactions do not exert a significant role in explaining current distributions of a particular species of butterfly (clouded Apollo, Parnassius mnemosyne ) in Europe; and ( H 0 2) -biotic interactions do not exert a significant role in predictions of altered species' ranges under climate change.