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The MaxEnt software package is one of the most popular tools for species distribution and environmental niche modeling, with over 1000 published applications since 2006. Its popularity is likely for two reasons: 1) MaxEnt typically outperforms other methods based on predictive accuracy and 2) the software is particularly easy to use. MaxEnt users must make a number of decisions about how they should select their input data and choose from a wide variety of settings in the software package to build models from these data. The underlying basis for making these decisions is unclear in many studies, and default settings are apparently chosen, even though alternative settings are often more appropriate. In this paper, we provide a detailed explanation of how MaxEnt works and a prospectus on modeling options to enable users to make informed decisions when preparing data, choosing settings and interpreting output. We explain how the choice of background samples reflects prior assumptions, how nonlinear functions of environmental variables (features) are created and selected, how to account for environmentally biased sampling, the interpretation of the various types of model output and the challenges for model evaluation. We demonstrate MaxEnt's calculations using both simplified simulated data and occurrence data from South Africa on species of the flowering plant family Proteaceae. Throughout, we show how MaxEnt's outputs vary in response to different settings to highlight the need for making biologically motivated modeling decisions.

Changes in spring and autumn phenology of temperate plants in recent decades have become iconic bio-indicators of rapid climate change. These changes have substantial ecological and economic impacts. However, autumn phenology remains surprisingly little studied. Although the effects of unfavorable environmental conditions (e.g., frost, heat, wetness, and drought) on autumn phenology have been observed for over 60 y, how these factors interact to influence autumn phenological events remain poorly understood. Using remotely sensed phenology data from 2001 to 2012, this study identified and quantified significant effects of a suite of environmental factors on the timing of fall dormancy of deciduous forest communities in New England, United States. Cold, frost, and wet conditions, and high heat-stress tended to induce earlier dormancy of deciduous forests, whereas moderate heat- and drought-stress delayed dormancy. Deciduous forests in two eco-regions showed contrasting, nonlinear responses to variation in these explanatory factors. Based on future climate projection over two periods (2041–2050 and 2090–2099), later dormancy dates were predicted in northern areas. However, in coastal areas earlier dormancy dates were predicted. Our models suggest that besides warming in climate change, changes in frost and moisture conditions as well as extreme weather events (e.g., drought- and heat-stress, and flooding), should also be considered in future predictions of autumn phenology in temperate deciduous forests. This study improves our understanding of how multiple environmental variables interact to affect autumn phenology in temperate deciduous forest ecosystems, and points the way to building more mechanistic and predictive models.

Knowledge of species' geographic distributions is critical for understanding and forecasting population dynamics, responses to environmental change, biodiversity patterns, and conservation planning. While many suggestive correlative occurrence models have been used to these ends, progress lies in understanding the underlying population biology that generates patterns of range dynamics. Here, we show how to use a limited quantity of demographic data to produce demographic distribution models (DDMs) using integral projection models for size-structured populations. By modeling survival, growth, and fecundity using regression, integral projection models can interpolate across missing size data and environmental conditions to compensate for limited data. To accommodate the uncertainty associated with limited data and model assumptions, we use Bayesian models to propagate uncertainty through all stages of model development to predictions. DDMs have a number of strengths: 1) DDMs allow a mechanistic understanding of spatial occurrence patterns; 2) DDMs can predict spatial and temporal variation in local population dynamics; 3) DDMs can facilitate extrapolation under altered environmental conditions because one can evaluate the consequences for individual vital rates. To illustrate these features, we construct DDMs for an overstory perennial shrub in the Proteaceae family in the Cape Floristic Region of South Africa. We find that the species' population growth rate is limited most strongly by adult survival throughout the range and by individual growth in higher rainfall regions. While the models predict higher population growth rates in the core of the range under projected climates for 2050, they also suggest that the species faces a threat along arid range margins from the interaction of more frequent fire and drying climate. The results (and uncertainties) are helpful for prioritizing additional sampling of particular demographic parameters along these gradients to iteratively refine projections. In the appendices, we provide fully functional R code to perform all analyses.

The rate at which ecosystems recover from disturbance can greatly influence their resilience to environmental change. We used more than a decade of satellite data to model how the extraordinarily biodiverse shrublands of South Africa recover following fire and how recovery rates vary with temperature and precipitation across the region. We found that climate strongly affects how quickly plant communities can recover after fire. We also used global climate models to project ecosystem recovery into the future and found that warmer winter temperatures will likely speed up postfire recovery unless precipitation declines as temperature increases (as some models project). Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080–2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.

Models of the geographic distributions of species have wide application in ecology. But the nonspatial, single-level, regression models that ecologists have often employed do not deal with problems of irregular sampling intensity or spatial dependence, and do not adequately quantify uncertainty. We show here how to build statistical models that can handle these features of spatial prediction and provide richer, more powerful inference about species niche relations, distributions, and the effects of human disturbance. We begin with a familiar generalized linear model and build in additional features, including spatial random effects and hierarchical levels. Since these models are fully specified statistical models, we show that it is possible to add complexity without sacrificing interpretability. This step-by-step approach, together with attached code that implements a simple, spatially explicit, regression model, is structured to facilitate self-teaching. All models are developed in a Bayesian framework. We assess the performance of the models by using them to predict the distributions of two plant species (Proteaceae) from South Africa's Cape Floristic Region. We demonstrate that making distribution models spatially explicit can be essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results. Adding hierarchical levels to the models has further advantages in allowing human transformation of the landscape to be taken into account, as well as additional features of the sampling process.

AIM: Accurate spatial information on species occurrence is essential to address global change. Models for presence‐only data are central to predicting species distributions because these represent the only geographical information available for many species. In this paper we introduce extensions to incorporate a variety of types of additional spatially explicit sources of information in Maxent and Poisson point process models. This spatial information comes from the output of other statistical or conceptual models. INNOVATION: Our approach relies on minimizing the relative (or cross) entropy (known as Minxent) between the predicted distribution and a prior distribution. In many scenarios, researchers have some additional information or expectations about the species distribution, such as outputs from previous models. Here, we show how to use this information to improve predictions of both niche models and spatial distributions, depending on what types of spatially explicit prior information is available and how it is incorporated in the model. MAIN CONCLUSIONS: We illustrate applications of Minxent that include models for sampling bias, explicitly incorporating dispersal/other ecological processes, combining native and invasive range data, incorporating expert maps, and borrowing strength across taxonomic relatives. These applications focus on addressing biological scenarios where range modelling is extremely challenging – non‐equilibrium species distributions and rare and narrowly distributed species – due to data limitations. When data are limited, we are typically forced to make informal assumptions or lean on predictions of other models in order to obtain useful predictions; our applications of Minxent provide a formal way of describing these assumptions and connections to other models.

Forecasting ecological responses to climate change, invasion, and their interaction must rely on understanding underlying mechanisms. However, such forecasts require extrapolation into new locations and environments. We linked demography and environment using experimental biogeography to forecast invasive and native species’ potential ranges under present and future climate in New England, United States to overcome issues of extrapolation in novel environments. We studied two potentially nonequilibrium invasive plants’ distributions, Alliaria petiolata (garlic mustard) and Berberis thunbergii (Japanese barberry), each paired with their native ecological analogs to better understand demographic drivers of invasions. Our models predict that climate change will considerably reduce establishment of a currently prolific invader (A. petiolata) throughout New England driven by poor demographic performance in warmer climates. In contrast, invasion of B. thunbergii will be facilitated because of higher growth and germination in warmer climates, with higher likelihood to establish farther north and in closed canopy habitats in the south. Invasion success is in high fecundity for both invasive species and demographic compensation for A. petiolata relative to native analogs. For A. petiolata, simulations suggest that eradication efforts would require unrealistic efficiency; hence, management should focus on inhibiting spread into colder, currently unoccupied areas, understanding source–sink dynamics, and understanding community dynamics should A. petiolata (which is allelopathic) decline. Our results—based on considerable differences with correlative occurrence models typically used for such biogeographic forecasts—suggest the urgency of incorporating mechanism into range forecasting and invasion management to understand how climate change may alter current invasion patterns.

Societal Impact Statement Madagascar is famous for its unique forests and their fauna. Most of the island is covered by flammable grassy ecosystems long considered to be of human origin and threatening the remaining forests. Yet new studies show that many plants and animals of the grassy systems are unique to Madagascar and restricted to these open habitats. Open grassy ecosystems have markedly different management requirements from forests and bring different contributions to society. We argue that the grassy ecosystems can benefit Madagascar if understood and managed wisely using expanded knowledge bases that also include collaboration with locals. Summary Until recently, nearly all research and interests in Madagascar focused on forested habitats. To help place Madagascar's grassy ecosystems in context, we provide a summary of the origin, development, and evolution of open tropical, C4 grassy ecosystems elsewhere, especially those from Africa; we summarize similarities and differences with the distribution of C3 and C4 grasses in the Malagasy landscape, their plant traits, and inferences on the evolutionary legacy of grasses. We also discuss the animal communities that use and have coevolved in these grassy systems; to help resolve controversies over the pre‐settlement extent of grassy ecosystems, we suggest a variety of complementary geochemical, palaeobotanical, and molecular genetic tools that have been effectively used elsewhere to untangle forest/grassy ecosystem mosaics and the ecological and evolutionary processes that influence them. Many of these tools can and should be employed in Madagascar to fully understand the spatio‐temporal dynamics of open, grassy, and closed forest systems across the island; as regards conservation, we discuss the ecosystem services provided by grassy systems, which are too often ignored in general, not only as a biome, vis‐à‐vis forests, but also for their global importance as a carbon sink and role they play in water management and providing goods to local villagers. We conclude by outlining the necessary research to better manage open ecosystems across Madagascar without threatening endangered forest ecosystems. L'ile de Madagascar est célèbre pour ses forêts uniques et leur faune. Mais la plupart de l'île est recouverte d'écosystèmes herbeux inflammables considérés depuis longtemps d'origine humaine et capable de compromettre les forêts restantes. Pourtant, de nouvelles études montrent que de nombreuses plantes et animaux des systèmes herbeux sont uniques à Madagascar, et limités à ces habitats ouverts. Les écosystèmes herbeux ouverts ont des exigences de gestion nettement différentes de celles des forêts et offrent des divers biens et services à la société. Nous soutenons que les écosystèmes herbeux jouent un rôle beaucoup plus important pour Madagascar s'ils sont bien compris et gérés judicieusement en utilisant des bases de connaissances élargies qui incluent également la collaboration avec les habitants. Ny nosin'ny Madagasikara dia malaza tsy manam‐paharoa amin'ny fananany ala sy bibidia manokana. Nefa ny ankamaroan'ny faritry ny nosy dia rakotry ny tontolo iainana feno bozaka mora mirehitra, izay noheverina hatramin'izay fa vokatry ny nataon'ny olombelona ary atahoraha izany asan'ny olombelona izany hanimba ireo ala sisa tavela. Na izany aza, ny fanadihadiana vaovao taty aoriana dia mampiseho fa maro ny zavamaniry sy biby hita ao amin'ny tontolo iainana feno bozaka ireo fa tokana sady mampiavaka an'i Madagasikara, ary tsy hita raha tsy amin'ireo toeram‐ponenana malalaka sy misokatra ireo. Ny tontolo iainana mivelatra amin'ny bozaka dia manana fepetra takian'ny fitantanana tsy mitovy amin'ny fitantanana ny ala ary mitondra anjara biriky sy akony maro eo amin'ny fianan'ny fiaraha‐monina. Manamafy izahay fa manana anjara toerana lehibe ho an'i Madagasikara ny tontolon'ny bozaka raha mitohy ny fanadihadiana mikasika izany sy voatantana ara‐pahendrena izy ireo, izay miankina amin'ny fahalalàna mivelatra miaraka amin'ny fiaraha‐miasa amin'ny mponina eny ifotony. Madagascar is famous for its unique forests and their fauna. Most of the island is covered by flammable grassy ecosystems long considered to be of human origin and threatening the remaining forests. Yet new studies show that many plants and animals of the grassy systems are unique to Madagascar and restricted to these open habitats. Open grassy ecosystems have markedly different management requirements from forests and bring different contributions to society. We argue that the grassy ecosystems can benefit Madagascar if understood and managed wisely using expanded knowledge bases that also include collaboration with locals.

The fact that plant invasions are an ongoing process makes generalizations of invasive spread extraordinarily challenging. This is particularly true given the idiosyncratic nature of invasions, in which both historical and local conditions affect establishment success and hinder our ability to generate guidelines for early detection and eradication of invasive species. To overcome these limitations we have implemented a comprehensive approach that examines plant invasions at three spatial scales: regional, landscape, and local levels. At each scale, in combination with the others, we have evaluated the role of key environmental variables such as climate, landscape structure, habitat type, and canopy closure in the spread of three commonly found invasive woody plant species in New England, Berberis thunbergii, Celastrus orbiculatus, and Euonymus alatus. We developed a spatially explicit hierarchical Bayesian model that allowed us to take into account the ongoing nature of the spread of invasive species and to incorporate presence/absence data from the species' native ranges as well as from the invaded regions. Comparisons between predictions from climate‐only models with those from the multiscale forecasts emphasize the importance of including landscape structure in our models of invasive species' potential distributions. In addition, predictions generated using only native range data performed substantially worse than those that incorporated data from the target range. This points out important limitations in extrapolating distributional ranges from one region to another.

Plant leaf phenology is typically observed either via ground‐based visual observations on individuals or via remote sensing of land surface vegetation. To integrate phenological information from both data sources, collected at different spatial scales using different observational protocols, digital cameras were deployed spanning canopy areas with enough spatial resolution to identify temporal changes in individual deciduous tree species with continuous observations. Comparisons of phenology between camera photography and in situ observations have been reported in prior studies; however, it is still unclear that how these camera images relate to field observations at individual and species levels, and how the metrics from those images provide comparable species‐specific phenological responses to environmental variation. We set a suite of digital time‐lapse cameras to acquire continuous photographs of deciduous tree canopies and conducted ground‐based visual observations in Connecticut, USA, from 2012 to 2014. Comparisons between image‐derived dates and observed phenological dates showed that both green and red color indices could be matched to ground observations, and red color indices showed good performance in matching autumn phenology across our group of eight tree species that dominate the southern New England forests. Linear mixed‐effects models were applied to investigate the relationships between climatic/weather conditions and the timing of peak and of intensity of red color in fall foliage for each species. Model results suggested that temperature, precipitation, drought stress in autumn, and heat stress in summer are all important factors to the timing of peak fall foliage color and that higher minimum temperatures (or lower cold degree‐day accumulation) in the autumn are linked to higher intensity of red coloration at least in sugar maples. This study improves our understanding of temporal and spatial variation in the phenology of deciduous trees captured by digital cameras. As well, this provides insights into relating species‐specific information on phenology from visual observations in the field to near‐surface remote sensing and points to the need for further research on autumn phenology using the change in redness of tree canopies.