Contemporary climatic analogs for 540 North American urban areas in the late 21st century

It is challenging to communicate abstract future climate estimates. Here the authors utilized climate-analog mapping and they identified that North American urban areas’ climate by the 2080’s will become similar to the contemporary climate of locations hundreds of kilometers away and mainly to the south, while many urban areas will have no modern equivalent analogs under the RCP8.5 scenario

Contour-time approach to the Bose-Hubbard model in the strong coupling regime: Studying two-point spatio-temporal correlations at the Hartree-Fock-Bogoliubov level

We develop a formalism that allows the study of correlations in space and time in both the superfluid and Mott insulating phases of the Bose-Hubbard Model. Specifically, we obtain a two particle irreducible effective action within the contour-time formalism that allows for both equilibrium and out of equilibrium phenomena. We derive equations of motion for both the superfluid order parameter and two-point correlation functions. To assess the accuracy of this formalism, we study the equilibrium solution of the equations of motion and compare our results to existing strong coupling methods as well as exact methods where possible. We discuss applications of this formalism to out of equilibrium situations.Comment: 41 pages, 7 figures. arXiv admin note: substantial text overlap with arXiv:1606.0411

MaxEnt versus MaxLike: Empirical comparisons with ant species distributions

MaxEnt is one of the most widely used tools in ecology, biogeography, and evolution for modeling and mapping species distributions using presence-only occurrence records and associated environmental covariates. Despite its popularity, the exponential model implemented by MaxEnt does not directly estimate occurrence probability, the natural quantity of interest when modeling species distributions. Instead, MaxEnt generates an index of relative habitat suitability. MaxLike, a newly introduced maximum-likelihood technique, has been shown to overcome the problem of directly estimating the probability of occurrence using presence-only data. However, the performance and relative merits of MaxEnt and MaxLike remain largely untested, especially when modeling species with relatively few occurrence data that encompass only a portion of the geographic range of the species. Using georeferenced occurrence records for six species of ants in New England, we provide comparisons of MaxEnt and MaxLike. We show that by most quantitative metrics, the performance of MaxLike exceeds that of MaxEnt, regardless of whether MaxEnt models account for sampling bias and include greater model complexity than implemented in MaxLike. More importantly, for most species, the relative suitability index estimated by MaxEnt often was poorly correlated with the probability of occurrence estimated by MaxLike, suggesting that the two methods are estimating different quantities. For species distribution modeling, MaxLike, and similar models that are based on an explicit sampling process and that directly estimate probability of occurrence, should be considered as important alternatives to the widely-used MaxEnt framework. © 2013 Fitzpatrick et al

The Past, Present, and Future of the Hemlock Woolly Adelgid (\u3cem\u3eAdelges tsugae\u3c/em\u3e) and Its Ecological Interactions with Eastern Hemlock (\u3cem\u3eTsuga canadensis\u3c/em\u3e) Forests

The nonnative hemlock woolly adelgid is steadily killing eastern hemlock trees in many parts of eastern North America. We summarize impacts of the adelgid on these forest foundation species; review previous models and analyses of adelgid spread dynamics; and examine how previous forecasts of adelgid spread and ecosystem dynamics compare with current conditions. The adelgid has reset successional sequences, homogenized biological diversity at landscape scales, altered hydrological dynamics, and changed forest stands from carbon sinks into carbon sources. A new model better predicts spread of the adelgid in the south and west of the range of hemlock, but still under-predicts its spread in the north and east. Whether these underpredictions result from inadequately modeling accelerating climate change or accounting for people inadvertently moving the adelgid into new locales needs further study. Ecosystem models of adelgid-driven hemlock dynamics have consistently forecast that forest carbon stocks will be little affected by the shift from hemlock to early-successional mixed hardwood stands, but these forecasts have assumed that the intermediate stages will remain carbon sinks. New forecasting models of adelgid-driven hemlock decline should account for observed abrupt changes in carbon flux and ongoing and accelerating human-driven land-use and climatic changes

Modeling Range Dynamics In Heterogeneous Landscapes: Invasion Of The Hemlock Woolly Adelgid In Eastern North America

Range expansion by native and exotic species will continue to be a major component of global change. Anticipating the potential effects of changes in species distributions requires models capable of forecasting population spread across realistic, heterogeneous landscapes and subject to spatiotemporal variability in habitat suitability. Several decades of theory and model development, as well as increased computing power and availability of fine-resolution GIS data, now make such models possible. Still unanswered, however, is the question of how well this new generation of dynamic models will anticipate range expansion. Here we develop a spatially explicit stochastic model that combines dynamic dispersal and population processes with fine-resolution maps characterizing spatiotemporal heterogeneity in climate and habitat to model range expansion of the hemlock woolly adelgid (HWA; Adelges tsugae). We parameterize this model using multiyear data sets describing population and dispersal dynamics of HWA and apply it to eastern North America over a 57-year period (1951–2008). To evaluate the model, the observed pattern of spread of HWA during this same period was compared to model predictions. Our model predicts considerable heterogeneity in the risk of HWA invasion across space and through time, and it suggests that spatiotemporal variation in winter temperature, rather than hemlock abundance, exerts a primary control on the spread of HWA. Although the simulations generally matched the observed current extent of the invasion of HWA and patterns of anisotropic spread, it did not correctly predict when HWA was observed to arrive in different geographic regions. We attribute differences between the modeled and observed dynamics to an inability to capture the timing and direction of long-distance dispersal events that substantially affected the ensuing pattern of spread.Organismic and Evolutionary Biolog

Should Species Distribution Models Account for Spatial Autocorrelation? A Test of Model Projections Across Eight Millennia of Climate Change

Aim: The distributions of many organisms are spatially autocorrelated, but it is unclear whether including spatial terms in species distribution models (SDMs) improves projections of species distributions under climate change. We provide one of the first comparative evaluations of the ability of a purely spatial SDM, a purely non-spatial SDM and a SDM that combines spatial and environmental information to project species distributions across eight millennia of climate change. Location: Eastern North America. Methods: To distinguish between the importance of climatic versus spatial explanatory variables we fit three Bayesian SDMs to modern occurrence data for Fagus and Tsuga, two tree genera whose distributions can be reliably inferred from fossil pollen: a spatially varying intercept model, a non-spatial model with climatic variables and a spatially varying intercept plus climate model. Using palaeoclimate data with a high temporal resolution, we hindcasted the SDMs in 1000-year time steps for 8000 years, and compared model projections with palynological data for the same periods. Results: For both genera, spatial SDMs provided better fits to the calibration data, more accurate predictions of a hold-out validation dataset of modern trees and higher variance in current predictions and hindcasted projections than non-spatial SDMs. Performance of non-spatial and spatial SDMs according to the area under the receiver operating curve varied by genus. For both genera, false negative rates between non-spatial and spatial models were similar, but spatial models had lower false positive rates than non-spatial models. Main conclusions: The inclusion of computationally demanding spatial random effects in SDMs may be warranted when ecological or evolutionary processes prevent taxa from shifting their distributions or when the cost of false positives is high.Organismic and Evolutionary Biolog