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“Space-for-time” substitution is widely used in biodiversity modeling to infer past or future trajectories of ecological systems from contemporary spatial patterns. However, the foundational assumption—that drivers of spatial gradients of species composition also drive temporal changes in diversity—rarely is tested. Here, we empirically test the space-for-time assumption by constructing orthogonal datasets of compositional turnover of plant taxa and climatic dissimilarity through time and across space from Late Quaternary pollen records in eastern North America, then modeling climate-driven compositional turnover. Predictions relying on space-for-time substitution were ∼72% as accurate as “time-for-time” predictions. However, space-for-time substitution performed poorly during the Holocene when temporal variation in climate was small relative to spatial variation and required subsampling to match the extent of spatial and temporal climatic gradients. Despite this caution, our results generally support the judicious use of space-for-time substitution in modeling community responses to climate change.

Natural ecosystems store large amounts of carbon globally, as organisms absorb carbon from the atmosphere to build large, long-lasting, or slow-decaying structures such as tree bark or root systems. An ecosystem’s carbon sequestration potential is tightly linked to its biological diversity. Yet when considering future projections, many carbon sequestration models fail to account for the role biodiversity plays in carbon storage. Here, we assess the consequences of plant biodiversity loss for carbon storage under multiple climate and land-use change scenarios. We link a macroecological model projecting changes in vascular plant richness under different scenarios with empirical data on relationships between biodiversity and biomass. We find that biodiversity declines from climate and land use change could lead to a global loss of between 7.44-103.14 PgC (global sustainability scenario) and 10.87-145.95 PgC (fossil-fueled development scenario). This indicates a self-reinforcing feedback loop, where higher levels of climate change lead to greater biodiversity loss, which in turn leads to greater carbon emissions and ultimately more climate change. Conversely, biodiversity conservation and restoration can help achieve climate change mitigation goals.

Climate change is causing geographical redistribution of plant and animal species globally. These distributional shifts are leading to new ecosystems and ecological communities, changes that will affect human society. Pecl et al. review these current and future impacts and assess their implications for sustainable development goals. Science , this issue p. eaai9214 Distributions of Earth’s species are changing at accelerating rates, increasingly driven by human-mediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation’s Sustainable Development Goals.

Protected areas are widely considered essential for biodiversity conservation. However, few global studies have demonstrated that protection benefits a broad range of species. Here, using a new global biodiversity database with unprecedented geographic and taxonomic coverage, we compare four biodiversity measures at sites sampled in multiple land uses inside and outside protected areas. Globally, species richness is 10.6% higher and abundance 14.5% higher in samples taken inside protected areas compared with samples taken outside, but neither rarefaction-based richness nor endemicity differ significantly. Importantly, we show that the positive effects of protection are mostly attributable to differences in land use between protected and unprotected sites. Nonetheless, even within some human-dominated land uses, species richness and abundance are higher in protected sites. Our results reinforce the global importance of protected areas but suggest that protection does not consistently benefit species with small ranges or increase the variety of ecological niches. Protected areas are thought essential for biodiversity conservation, but few studies confirm that protection benefits species. Here, Gray and Hill et al . analyse a global, taxonomically broad database to show that local species richness and abundance are higher inside protected areas than outside.

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed areas. Since the spatial bias generally results in environmental bias, the difference between occurrence collection and background sampling may lead to inaccurate models. To correct the estimation, we propose choosing background data with the same bias as occurrence data. We investigate theoretical and practical implications of this approach. Accurate information about spatial bias is usually lacking, so explicit biased sampling of background sites may not be possible. However, it is likely that an entire target group of species observed by similar methods will share similar bias. We therefore explore the use of all occurrences within a target group as biased background data. We compare model performance using target-group background and randomly sampled background on a comprehensive collection of data for 226 species from diverse regions of the world. We find that target-group background improves average performance for all the modeling methods we consider, with the choice of background data having as large an effect on predictive performance as the choice of modeling method. The performance improvement due to target-group background is greatest when there is strong bias in the target-group presence records. Our approach applies to regression-based modeling methods that have been adapted for use with occurrence data, such as generalized linear or additive models and boosted regression trees, and to Maxent, a probability density estimation method. We argue that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions.

Vast gaps in available information on the spatial distribution of biodiversity pose a major challenge for regional conservation planning in many parts of the world. This problem is often addressed by basing such planning on various biodiversity surrogates. In some situations, distributional data for selected taxa may be used as surrogates for biodiversity as a whole. However, this approach is less effective in data-poor regions, where there may be little choice but to base conservation planning on some form of remote environmental mapping, derived, for example, from interpretation of satellite imagery or from numerical classification of abiotic environmental layers. Although this alternative approach confers obvious benefits in terms of cost-effectiveness and rapidity of application, problems may arise if congruence is poor between mapped land-classes and actual biological distributions. I propose three strategies for making more effective use of available biological data and knowledge to alleviate such problems by (1) more closely integrating biological and environmental data through predictive modeling, with increased emphasis on modeling collective properties of biodiversity rather than individual entities; (2) making more rigorous use of remotely mapped surrogates in conservation planning by incorporating knowledge of heterogeneity within land-classes, and of varying levels of distinctiveness between classes, into measures of conservation priority and achievement; and (3) using relatively data-rich regions as test-beds for evaluating the performance of surrogates that can be readily applied across data-poor regions.

Monitoring programs typically focus on selected, better-known elements of biodiversity (e.g., birds and other vertebrates), and monitoring sites are sparsely and patchily distributed, especially outside Europe and North America. [...] to obtain a more geographically complete picture of overall biodiversity change, global assessments commonly supplement measures derived from in situ monitoring with less direct measures derived from satellite-borne remote sensing and other forms of remote mapping.

Land use and related pressures have reduced local terrestrial biodiversity, but it is unclear how the magnitude of change relates to the recently proposed planetary boundary (\"safe limit\"). We estimate that land use and related pressures have already reduced local biodiversity intactness–the average proportion of natural biodiversity remaining in local ecosystems–beyond its recently proposed planetary boundary across 58.1% of the world's land surface, where 71.4% of the human population live. Biodiversity intactness within most biomes (especially grassland biomes), most biodiversity hotspots, and even some wilderness areas is inferred to be beyond the boundary. Such widespread transgression of safe limits suggests that biodiversity loss, if unchecked, will undermine efforts toward long-term sustainable development.

Global maps constructed using climate-change velocities to derive spatial trajectories for climatic niches between 1960 and 2100 show past and future shifts in ecological climate niches; properties of these trajectories are used to infer changes in species distributions, and thus identify areas that will act as climate sources and sinks, and geographical barriers to species migrations. Species mobility in a changing climate To survive in a changing climate, a species may need to move in order to stay in an area with a constant average temperature. Such mobility would depend on an ability to keep pace with a moving climate — and on the absence of physical barriers to migration. These authors use the velocity of climate change to construct a global map of how ecological climate niches have shifted in recent decades and go on to predict changes in species distribution to the end of this century. The map indicates areas that will act as climate sources and sinks, and geographical barriers likely to impede species migration. The data show that geographical connections and physical barriers — mostly coasts — have profound effects on the expected ability of organisms to track their preferred climate. This work underlines the importance of migration corridors linking warmer and cooler areas as a means of maintaining biodiversity. The reorganization of patterns of species diversity driven by anthropogenic climate change, and the consequences for humans 1 , are not yet fully understood or appreciated 2 , 3 . Nevertheless, changes in climate conditions are useful for predicting shifts in species distributions at global 4 and local scales 5 . Here we use the velocity of climate change 6 , 7 to derive spatial trajectories for climatic niches from 1960 to 2009 (ref. 7 ) and from 2006 to 2100, and use the properties of these trajectories to infer changes in species distributions. Coastlines act as barriers and locally cooler areas act as attractors for trajectories, creating source and sink areas for local climatic conditions. Climate source areas indicate where locally novel conditions are not connected to areas where similar climates previously occurred, and are thereby inaccessible to climate migrants tracking isotherms: 16% of global surface area for 1960 to 2009, and 34% of ocean for the ‘business as usual’ climate scenario (representative concentration pathway (RCP) 8.5) 8 representing continued use of fossil fuels without mitigation. Climate sink areas are where climate conditions locally disappear, potentially blocking the movement of climate migrants. Sink areas comprise 1.0% of ocean area and 3.6% of land and are prevalent on coasts and high ground. Using this approach to infer shifts in species distributions gives global and regional maps of the expected direction and rate of shifts of climate migrants, and suggests areas of potential loss of species richness.

Species distributions and abundances are undergoing rapid changes worldwide. This highlights the significance of reliable, integrated information for guiding and assessing actions and policies aimed at managing and sustaining the many functions and benefits of species. Here we synthesize the types of data and approaches that are required to achieve such an integration and conceptualize ‘essential biodiversity variables’ (EBVs) for a unified global capture of species populations in space and time. The inherent heterogeneity and sparseness of raw biodiversity data are overcome by the use of models and remotely sensed covariates to inform predictions that are contiguous in space and time and global in extent. We define the species population EBVs as a space–time–species–gram (cube) that simultaneously addresses the distribution or abundance of multiple species, with its resolution adjusted to represent available evidence and acceptable levels of uncertainty. This essential information enables the monitoring of single or aggregate spatial or taxonomic units at scales relevant to research and decision-making. When combined with ancillary environmental or species data, this fundamental species population information directly underpins a range of biodiversity and ecosystem function indicators. The unified concept we present links disparate data to downstream uses and informs a vision for species population monitoring in which data collection is closely integrated with models and infrastructure to support effective biodiversity assessment. Changes in species distribution and abundance can be captured using essential biodiversity variables (EBVs). Here, the authors synthesize the data and approaches needed for EBVs that allow monitoring of populations in both space and time.