Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready‐to‐use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically‐based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta‐analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web‐based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.

This contribution is part of a special series of Inaugural Articles by members of the National Academy of Sciences elected in 2014.Contributed by Janet Franklin, February 2, 2016 (sent for review October 8, 2015; reviewed by Gregory P. Asner, Monica G. Turner, and Peter M. Vitousek)Anthropogenic drivers of global change include rising atmospheric concentrations of carbon dioxide and other greenhouse gasses and resulting changes in the climate, as well as nitrogen deposition, biotic invasions, altered disturbance regimes, and land-use change. Predicting the effects of global change on terrestrial plant communities is crucial because of the ecosystem services vegetation provides, from climate regulation to forest products. In this paper, we present a framework for detecting vegetation changes and attributing them to global change drivers that incorporates multiple lines of evidence from spatially extensive monitoring networks, distributed experiments, remotely sensed data, and historical records. Based on a literature review, we summarize observed changes and then describe modeling tools that can forecast the impacts of multiple drivers on plant communities in an era of rapid change. Observed responses to changes in temperature, water, nutrients, land use, and disturbance show strong sensitivity of ecosystem productivity and plant population dynamics to water balance and long-lasting effects of disturbance on plant community dynamics. Persistent effects of land-use change and human-altered fire regimes on vegetation can overshadow or interact with climate change impacts. Models forecasting plant community responses to global change incorporate shifting ecological niches, population dynamics, species interactions, spatially explicit disturbance, ecosystem processes, and plant functional responses. Monitoring, experiments, and models evaluating multiple change drivers are needed to detect and predict vegetation changes in response to 21st century global change.climate change | drought | forests | global change | land-use change T errestrial plant communities include forests, woodlands, shrublands, and grasslands; they support economic activities including forestry and grazing and provide other ecosystem services (1) such as carbon sequestration and water delivery. Plant communities play a key role in global biogeochemical cycles of carbon, oxygen, water, and nitrogen, with feedbacks to the oceans, atmosphere, and climate. The distribution of animals on land is often influenced by the distribution of vegetation, and therefore, plant community dynamics affect biodiversity. Changes in the Earth's vegetation in response to climate change, and associated faunal changes, may have played a role in the evolution of the human lineage (2, 3). Thus, human populations have a vested interest in understanding rapid global change effects on terrestrial plant communities.Anthropogenic drivers of global change include rising atmospheric concentrations of CO 2 and other greenhouse gasses and associated changes in the climate, as well...

Aim Plant distributions and vegetation dynamics underpin key global phenomena, including biogeochemical cycling, ecosystem productivity and terrestrial biodiversity patterns. Aggregated and remotely collected ‘big data’ are required to forecast the effects of global change on plant communities. We synthesize advances in developing and exploiting big data in global change plant ecology, and identify challenges to their effective use in global change studies. Location Global. Methods We explored databases, catalogues and registries with respect to their accessibility, geographical and taxonomic extent, sample bias and other types of uncertainty, from the perspective both users and contributors. We identified four kinds of big data needed to predict the impacts of global change on plant populations and communities using spatially explicit models: remotely sensed and other environmental maps, species occurrence records, community composition (plots) and species traits, especially demographics. Results Digital environmental maps, including remotely sensed data, are the most mature class of big data discussed herein whereby protocols for archiving, discovering and analysing them have developed over three decades. Species locality records are being aggregated into databases that are easy to search and access, and while methods for addressing uncertainties are a major research focus, better spatial representation is still needed. Plot data from inventories have tremendous potential for monitoring and modelling the impacts of global change on plant communities but tend to be restricted to forests or concentrated in certain geographical areas. Ongoing efforts to aggregate plot and trait data from multiple sources are challenged by their heterogeneous coverage, attributes and protocols and a lack of data standards. Main conclusions Future goals include developing systematic frameworks for selecting geospatial data, improving tools for assessing the quality of species occurrence records and increased aggregation and discoverability of plot and trait data. Aggregated data collected by scientists, not sensors, provide more meaningful insights when data collectors are involved in analysis.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids thus fail to reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions are controlled and most terrestrial species reside. Here we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0-5 and 5-15 cm depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all of the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding 2 m gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (3.6 ± 2.3°C warmer than gridded air temperature), whereas soils in warm and humid environments are on average slightly cooler (0.7 ± 2.3°C cooler). The observed substantial and biome-specific offsets underpin that the projected impacts of climate and climate change on biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining global gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Research in environmental science relies heavily on global climatic grids derived from estimates of air temperature at around 2 meter above ground1-3. These climatic grids however fail to reflect conditions near and below the soil surface, where critical ecosystem functions such as soil carbon storage are controlled and most biodiversity resides4-8. By using soil temperature time series from over 8500 locations across all of the world’s terrestrial biomes4, we derived global maps of soil temperature-related variables at 1 km resolution for the 0–5 and 5–15 cm depth horizons. Based on these maps, we show that mean annual soil temperature differs markedly from the corresponding 2 m gridded air temperature, by up to 10°C, with substantial variation across biomes and seasons. Soils in cold and/or dry biomes are annually substantially warmer (3.6°C ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are slightly cooler (0.7 ± 2.3°C). As a result, annual soil temperature varies less (by 17%) across the globe than air temperature. The effect of macroclimatic conditions on the difference between soil and air temperature highlights the importance of considering that macroclimate warming may not result in the same level of soil temperature warming. Similarly, changes in precipitation could alter the relationship between soil and air temperature, with implications for soil-atmosphere feedbacks9. Our results underpin that the impacts of climate and climate change on biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments.

Context Predicting climate-driven species' range shifts depend substantially on species' exposure to climate change. Mountain landscapes contain a wide range of topoclimates and soil characteristics that are thought to mediate range shifts and buffer species' exposure. Quantifying fine-scale patterns of exposure across mountainous terrain is a key step in understanding vulnerability of species to regional climate change. Objectives We demonstrated a transferable, flexible approach for mapping climate change exposure in a moisture-limited, mountainous California landscape across 4 climate change projections under phase 5 of the Coupled Model Intercomparison Project (CMIP5) for mid-(2040-2069) and endof-century (2070-2099). Methods We produced a 149-year dataset (1951-2099) of modeled climatic water deficit (CWD), which is strongly associated with plant distributions, at 30-m resolution to map climate change exposure in the Tehachapi Mountains, California, USA. We defined climate change exposure in terms of departure from the 1951-1980 mean and historical range of variability in CWD in individual years and three-year moving windows. Results Climate change exposure was generally greatest at high elevations across all future projections, though we encountered moderate topographic buffering on poleward-facing slopes. Historically dry lowlands demonstrated the least exposure to climate change. Conclusions In moisture-limited, Mediterranean-climate landscapes, high elevations may experience the greatest exposure to climate change in the 21 st Century. High elevation species may thus be especially vulnerable to continued climate change as habitats shrink and historically energylimited locations become increasingly moisture-limited in the future.

As trees are long-lived organisms, the impacts of climate change on forest communities may not be apparent on the time scale of years to decades. While lagged responses to environmental change are common in forested systems, potential for abrupt transitions under climate change may occur in environments where alternative vegetation states are influenced by disturbances, such as fire. The Klamath mountains (northern California and southwest Oregon, USA) are currently dominated by carbon rich and hyper-diverse temperate conifer forests, but climate change could disrupt the mechanisms promoting forest stability– regeneration and fire tolerance— via shifts in the fire regime in conjunction with lower fitness of conifers under a hotter climate. Understanding how this landscape will respond to near-term climate change (before 2100) is critical for predicting potential climate change feedbacks and to developing sound forest conservation and management plans. Using a landscape simulation model, we estimate that 1/3 of the Klamath could transition from conifer forest to shrub/hardwood chaparral, triggered by an enhanced fire activity coupled with lower post-fire conifer establishment. Such shifts were more prevalent under higher climate change forcing (RCP 8.5) but were also simulated under the climate of 1950-2000, reflecting the joint influences of early warming trends and historical forest legacies. Our results demonstrate that there is a large potential for loss of conifer forest dominance—and associated carbon stocks and biodiversity- in the Klamath before the end of the century, and that some losses would likely occur even without the influence of climate change. Thus, in the Klamath and other forested landscapes subject to similar feedback dynamics, major ecosystem shifts should be expected when climate change disrupts key stabilizing feedbacks that maintain the dominance of long-lived, slowly regenerating trees.

Aim Forest regeneration data provide an early signal of the persistence and migration of tree species, so we investigated whether species shifts due to climate change exhibit a common signal of response or whether changes vary by species.Location California Floristic Province, United States; mediterranean biome. MethodsWe related Forest Inventory and Analysis (FIA) data from 2000−07 for 13 tree species to high-resolution climate and geographical data. Using methods from invasion ecology, we derived indices of species-specific regeneration overlap and central tendency change (range-wide global indicators) based on kernel density estimation of presence and absence of regeneration. We then built regeneration surfaces to identify areas of occurrence of high regeneration (regeneration hotspots, local indicators) in both geographical and climate space for 13 common tree species. ResultsDifferences between presence and absence of regeneration in forests varied in magnitude across species, with little evidence that tree regeneration is shifting to higher latitudes and elevations, the expected geographical fingerprint of climate change. We also identified potential topographic mediators of regeneration dynamics. Multiple regeneration hotspots were found for many species, suggesting the influence of non-climatic factors on regeneration. Differences between the presence and absence of regeneration in geographic and climate spaces were not always congruent, suggesting that shifting climate space and range area are not entirely coupled. Main conclusionsThe distributions of regeneration in Californian forests show diverse signals, not always tracking the higher latitudinal-elevation fingerprint of climate change. Local regeneration hotspots are common in our analysis, suggesting spatially varying persistence of forest linked to natural and anthropogenic disturbances. Our results emphasize that projections of tree range shifts in the context of climate change should consider the variation of regeneration drivers within species ranges, beyond the overall climate signal.