Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site‐specific frequency distributions of occurrence probabilities across a species' range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1–42.5 thousand km; this was predicted to decline to 0.5–7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.     

Temperature,invaders and patchy habitat interact to limit the distribution of a vulnerable freshwater fish

Interacting global‐change drivers such as invasive species and climate warming are likely to have major and potentially unexpected influences on aquatic ecosystems. In river networks, modified water temperature combined with patchy physical conditions will likely cause shifts in the amount and distribution of suitable habitat, with influential invasive species further altering habitat availability. We examined how distributions of a thermally sensitive galaxiid fish native to the alpine rivers of New Zealand, Galaxias paucispondylus, were influenced by these drivers using spatially extensive presence–absence electrofishing surveys of 46 sites spread over four subcatchments. A unimodal response to water temperature and an interaction with substratum size meant G. paucispondylus were limited to streams with average summer water temperatures between 10.6 and 13.8 °C and were absent when average substratum sizes were <36 mm, regardless of temperature. In addition, non‐native trout >150 mm long excluded G. paucispondylus, but were only found in streams with average summer water temperatures <10.6 °C. These influences of trout likely strengthened the unimodal temperature response of G. paucispondylus and led to a very small G. paucispondylus realized niche. When predicted temperature increases were applied to catchment models, G. paucispondylus distributions were patchy and variable across subcatchments. Moreover, local physical characteristics of river networks were particularly important because of the non‐linear and interactive influences of temperature and substratum size on the outcome of species interactions. Therefore, substratum sizes, water temperature and a non‐native predator combined to influence the distribution of this thermally sensitive fish, illustrating how the effects of climate warming will likely be strongly context‐dependent and interactive.     

Biotic interactions influence the projected distribution of a specialist mammal under climate change

Aim

To measure the effects of including biotic interactions on climate‐based species distribution models (SDMs) used to predict distribution shifts under climate change. We evaluated the performance of distribution models for an endangered marsupial, the northern bettong (Bettongia tropica), comparing models that used only climate variables with models that also took into account biotic interactions.

Location

North‐east Queensland, Australia.

Methods

We developed separate climate‐based distribution models for the northern bettong, its two main resources and a competitor species. We then constructed models for the northern bettong by including climate suitability estimates for the resources and competitor as additional predictor variables to make climate + resource and climate + resource + competition models. We projected these models onto seven future climate scenarios and compared predictions of northern bettong distribution made by these differently structured models, using a ‘global’ metric, the I similarity statistic, to measure overlap in distribution and a ‘local’ metric to identify where predictions differed significantly.

Results

Inclusion of food resource biotic interactions improved model performance. Over moderate climate changes, up to 3.0 °C of warming, the climate‐only model for the northern bettong gave similar predictions of distribution to the more complex models including interactions, with differences only at the margins of predicted distributions. For climate changes beyond 3.0 °C, model predictions diverged significantly. The interactive model predicted less contraction of distribution than the simpler climate‐only model.

Main conclusions

Distribution models that account for interactions with other species, in particular direct resources, improve model predictions in the present‐day climate. For larger climate changes, shifts in distribution of interacting species cause predictions of interactive models to diverge from climate‐only models. Incorporating interactions with other species in SDMs may be needed for long‐term prediction of changes in distribution of species under climate change, particularly for specialized species strongly dependent on a small number of biotic interactions.     

Climate and host plant availability impact the future distribution of the bean leaf beetle (Cerotoma trifurcata)

The bean leaf beetle, Cerotoma trifurcata, has become a major pest of soybean throughout its North American range. With a changing climate, there is the potential for this pest to further expand its distribution and become an increasingly severe pest in certain regions. To examine this possibility, we developed bioclimatic envelope models for both the bean leaf beetle, and its most important agronomic host plant, soybean (Glycine max). These two models were combined to examine the potential future pest status of the beetle using climate change projections from multiple general circulation models (GCMs) and climate change scenarios. Despite the broad tolerances of soybean, incorporation of host plant availability substantially decreased the suitable and favourable areas for the bean leaf beetle as compared to an evaluation based solely on the climate envelope of the beetle, demonstrating the importance of incorporating biotic interactions in these predictions. The use of multiple GCM–scenario combinations also revealed differences in predictions depending on the choice of GCM, with scenario choice having less of an impact. While the Norwegian model predicted little northward expansion of the beetle from its current northern range limit of southern Ontario and overall decreases in suitable and favourable areas over time, the Canadian and Russian models predict that much of Ontario and Quebec will become suitable for the beetle in the future, as well as Manitoba under the Russian model. The Russian model also predicts expansion of the suitable and favourable areas for the beetle over time. Two predictions that do not depend on our choice of GCM include a decrease in suitability of the Mississippi Delta region and continued favourability of the southeastern United States.     

Assessing congruence of opportunistic records and systematic surveys for predicting Hispaniolan mammal species distributions

Comparative assessment of the relative information content of different independent spatial data types is necessary to evaluate whether they provide congruent biogeographic signals for predicting species ranges. Opportunistic occurrence records and systematically collected survey data are available from the Dominican Republic for Hispaniola’s surviving endemic non‐volant mammals, the Hispaniolan solenodon (Solenodon paradoxus) and Hispaniolan hutia (Plagiodontia aedium); opportunistic records (archaeological, historical and recent) exist from across the entire country, and systematic survey data have been collected from seven protected areas. Species distribution models were developed in maxent for solenodons and hutias using both data types, with species habitat suitability and potential country‐level distribution predicted using seven biotic and abiotic environmental variables. Three different models were produced and compared for each species: (a) opportunistic model, with starting model incorporating abiotic‐only predictors; (b) total survey model, with starting model incorporating biotic and abiotic predictors; and (c) reduced survey model, with starting model incorporating abiotic‐only predictors to allow further comparison with the opportunistic model. All models predict suitable environmental conditions for both solenodons and hutias across a broadly congruent, relatively large area of the Dominican Republic, providing a spatial baseline of conservation‐priority landscapes that might support native mammals. Correlation between total and reduced survey models is high for both species, indicating the substantial explanatory power of abiotic variables for predicting Hispaniolan mammal distributions. However, correlation between survey models and opportunistic models is only moderately positive. Species distribution models derived from different data types can provide different predictions about habitat suitability and conservation‐priority landscapes for threatened species, likely reflecting incompleteness and bias in spatial sampling associated with both data types. Models derived using both opportunistic and systematic data must therefore be applied critically and cautiously.     

Ecological differentiation and habitat unsuitability maintaining a ground beetle hybrid zone

Exogenous selection via interactions between organisms and environments may influence the dynamics of hybrid zones between species in multiple ways. Two major models of a hybrid zone allowed us to hypothesize that environmental conditions influence hybrid zone dynamics in two ways. In the first model, an environmental gradient determines the mosaic distribution at the boundary between ecologically differentiated species (mosaic hybrid zone model). In the second model, a patch of unsuitable habitat traps a hybrid zone between species whose hybrids are unfit (tension zone model). To test these, we examined the environmental factors influencing the spatial structure of a hybrid zone between the ground beetles Carabus maiyasanus and C. iwawakianus using GIS‐based quantification of environmental factors and a statistical comparison of species distribution models (SDMs). We determined that both of the hypothetical processes can be important in the hybrid zone. We detected interspecific differences in the environmental factors in presence localities and their relative contribution in SDMs. SDMs were not identical between species even within contact areas, but tended to be similar within the range of each species. These results suggest an association between environments and species, and provide evidence that ecological differentiation between species plays a role in the maintenance of the hybrid zone. Contact areas were characterized by a relatively high temperature, low precipitation, and high topological wetness. Thus, the contact areas were regarded as being located in an unsuitable habitat with a drier climate, where those populations are likely to occur in patches with limited precipitation concentrated. A comparison of spatial scales suggests that exogenous selection via environmental factors may be weaker than endogenous selection via genitalic incompatibility.     

Mechanistic variables can enhance predictive models of endotherm distributions: the American pika under current,past, and future climates

How climate constrains species’ distributions through time and space is an important question in the context of conservation planning for climate change. Despite increasing awareness of the need to incorporate mechanism into species distribution models (SDMs), mechanistic modeling of endotherm distributions remains limited in this literature. Using the American pika (Ochotona princeps) as an example, we present a framework whereby mechanism can be incorporated into endotherm SDMs. Pika distribution has repeatedly been found to be constrained by warm temperatures, so we used Niche Mapper, a mechanistic heat‐balance model, to convert macroclimate data to pika‐specific surface activity time in summer across the western United States. We then explored the difference between using a macroclimate predictor (summer temperature) and using a mechanistic predictor (predicted surface activity time) in SDMs. Both approaches accurately predicted pika presences in current and past climate regimes. However, the activity models predicted 8–19% less habitat loss in response to annual temperature increases of ~3–5 °C predicted in the region by 2070, suggesting that pikas may be able to buffer some climate change effects through behavioral thermoregulation that can be captured by mechanistic modeling. Incorporating mechanism added value to the modeling by providing increased confidence in areas where different modeling approaches agreed and providing a range of outcomes in areas of disagreement. It also provided a more proximate variable relating animal distribution to climate, allowing investigations into how unique habitat characteristics and intraspecific phenotypic variation may allow pikas to exist in areas outside those predicted by generic SDMs. Only a small number of easily obtainable data are required to parameterize this mechanistic model for any endotherm, and its use can improve SDM predictions by explicitly modeling a widely applicable direct physiological effect: climate‐imposed restrictions on activity. This more complete understanding is necessary to inform climate adaptation actions, management strategies, and conservation plans.     

Range position and climate sensitivity: The structure of among‐population demographic responses to climatic variation

Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North America. Using long‐term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long‐term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species‐interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate.     

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

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Geographical pattern of genetic diversity in Capsella bursa‐pastoris (Brassicaceae)—A global perspective

We analyzed the global genetic variation pattern of Capsella bursa‐pastoris (Brassicaceae) as expressed in allozymic (within‐locus) diversity and isozymic (between‐locus) diversity. Results are based on a global sampling of more than 20,000 C. bursa‐pastoris individuals randomly taken from 1,469 natural provenances in the native and introduced range, covering a broad spectrum of the species’ geographic distribution. We evaluated data for population genetic parameters and F‐statistics, and Mantel tests and AMOVA were performed. Geographical distribution patterns of alleles and multilocus genotypes are shown in maps and tables. Genetic diversity of introduced populations is only moderately reduced in comparison with native populations. Global population structure was analyzed with structure, and the obtained cluster affiliation was tested independently with classification approaches and macroclimatic data using species distribution modeling. Analyses revealed two main clusters: one distributed predominantly in warm arid to semiarid climate regions and the other predominantly in more temperate humid to semihumid climate regions. We observed admixture between the two lineages predominantly in regions with intermediate humidity in both the native and non‐native ranges. The genetically derived clusters are strongly supported in macroclimatic data space. The worldwide distribution patterns of genetic variation in the range of C. bursa‐pastoris can be explained by intensive intra‐ and intercontinental migration, but environmental filtering due to climate preadaption seems also involved. Multiple independent introductions of genotypes from different source regions are obvious. “Endemic” genotypes might be the outcome of admixture or of de novo mutation. We conclude that today's successfully established Capsella genotypes were preadapted and found matching niche conditions in the colonized range parts.