Extinction risk from climate change
University of Leeds1, University of Cambridge2, Royal Society for the Protection of Birds3, Macquarie University4, Durham University5, University of the Witwatersrand6, Conservation International7, Stellenbosch University8, World Conservation Monitoring Centre9, National Autonomous University of Mexico10, University of Kansas11, James Cook University12
TL;DR: Estimates of extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
Abstract: Climate change over the past approximately 30 years has produced numerous shifts in the distributions and abundances of species and has been implicated in one species-level extinction. Using projections of species' distributions for future climate scenarios, we assess extinction risks for sample regions that cover some 20% of the Earth's terrestrial surface. Exploring three approaches in which the estimated probability of extinction shows a power-law relationship with geographical range size, we predict, on the basis of mid-range climate-warming scenarios for 2050, that 15-37% of species in our sample of regions and taxa will be 'committed to extinction'. When the average of the three methods and two dispersal scenarios is taken, minimal climate-warming scenarios produce lower projections of species committed to extinction ( approximately 18%) than mid-range ( approximately 24%) and maximum-change ( approximately 35%) scenarios. These estimates show the importance of rapid implementation of technologies to decrease greenhouse gas emissions and strategies for carbon sequestration.
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TL;DR: P PaleoClim is a free database of downscaled paleoclimate outputs at 2.5-minute resolution that includes surface temperature and precipitation estimates from snapshot-style climate model simulations using HadCM3, a version of the UK Met Office Hadley Centre General Circulation Model.
Abstract: High-resolution, easily accessible paleoclimate data are essential for environmental, evolutionary, and ecological studies. The availability of bioclimatic layers derived from climatic simulations representing conditions of the Late Pleistocene and Holocene has revolutionized the study of species responses to Late Quaternary climate change. Yet, integrative studies of the impacts of climate change in the Early Pleistocene and Pliocene – periods in which recent speciation events are known to concentrate – have been hindered by the limited availability of downloadable, user-friendly climatic descriptors. Here we present PaleoClim, a free database of downscaled paleoclimate outputs at 2.5-minute resolution (~5 km at equator) that includes surface temperature and precipitation estimates from snapshot-style climate model simulations using HadCM3, a version of the UK Met Office Hadley Centre General Circulation Model. As of now, the database contains climatic data for three key time periods spanning from 3.3 to 0.787 million years ago: the Marine Isotope Stage 19 (MIS19) in the Pleistocene (~787 ka), the mid-Pliocene Warm Period (~3.264–3.025 Ma), and MIS M2 in the Late Pliocene (~3.3 Ma). Machine-accessible metadata file describing the reported data (ISA-Tab format)
224 citations
TL;DR: Analysis of 37 years of data from an individual-based study of great tits in the Netherlands shows how climate change-induced mismatch can have strong effects on the relative fitness of phenotypes within years, but weak effects on mean demographic rates across years.
Abstract: Populations are shifting their phenology in response to climate change, but these shifts are often asynchronous among interacting species. Resulting phenological mismatches can drive simultaneous changes in natural selection and population demography, but the links between these interacting processes are poorly understood. Here we analyse 37 years of data from an individual-based study of great tits (Parus major) in the Netherlands and use mixed-effects models to separate the within- and across-year effects of phenological mismatch between great tits and caterpillars (a key food source for developing nestlings) on components of fitness at the individual and population levels. Several components of individual fitness were affected by individual mismatch (i.e. late breeding relative to the caterpillar food peak date), including the probability of double-brooding, fledgling success, offspring recruitment probability and the number of recruits. Together these effects contributed to an overall negative relationship between relative fitness and laying dates, that is, selection for earlier laying on average. Directional selection for earlier laying was stronger in years where birds bred on average later than the food peak, but was weak or absent in years where the phenology of birds and caterpillars matched (i.e. no population mismatch). The mean number of fledglings per female was lower in years when population mismatch was high, in part because fewer second broods were produced. Population mismatch had a weak effect on the mean number of recruits per female, and no effect on mean adult survival, after controlling for the effects of breeding density and the quality of the autumnal beech (Fagus sylvatica) crop. These findings illustrate how climate change-induced mismatch can have strong effects on the relative fitness of phenotypes within years, but weak effects on mean demographic rates across years. We discuss various general mechanisms that influence the extent of coupling between breeding phenology, selection and population dynamics in open populations subject to strong density regulation and stochasticity.
224 citations
Cites background from "Extinction risk from climate change..."
...Climate change, for example, is thought to represent perhaps the biggest threat to global biodiversity (Thomas et al. 2004; Malcolm et al. 2006), yet we know surprisingly little about how changes in climate translate into changes in local selective pressures and how these, in turn, influence the demographic responses of populations (Reed, Schindler & Waples 2010)....
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...Climate change, for example, is thought to represent perhaps the biggest threat to global biodiversity (Thomas et al. 2004; Malcolm et al. 2006), yet we know surprisingly little about how changes in climate translate into changes in local selective pressures and how these, in turn, influence the…...
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TL;DR: DST-NRF Centre of Excellence for Invasion Biology, the University of Pretoria, EU FP6 MACIS species targeted project (Minimisation of and Adaptation to Climate change: Impacts on biodiversity, contract No. 044399) and EU FP 6 ECOCHANGE integrated project (Challenges in assessing and forecasting biodiversity and ecosystem changes in Europe).
Abstract: DST-NRF Centre of Excellence for Invasion Biology, the University of Pretoria, EU FP6 MACIS species targeted project (Minimisation of and Adaptation to Climate change: Impacts on biodiversity, contract No.: 044399) and EU FP6 ECOCHANGE integrated project (Challenges in assessing and forecasting biodiversity and ecosystem changes in Europe).
223 citations
Cites background from "Extinction risk from climate change..."
...It is a broad assumption that is commonly used, and represents two opposing extremes of how climate change may affect species ranges, based only on the dispersal abilities of species (Thomas et al., 2004; Thuiller et al., 2005)....
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TL;DR: In this article, the authors developed a bioclimatic envelope model for cheatgrass (Bromus tectorum), a non-native invasive grass in the western US, based on its invaded distribution.
Abstract: Interactions between climate change and non-native invasive species may combine to increase invasion risk to native ecosystems. Changing climate creates risk as new terrain becomes climatically suitable for invasion. However, climate change may also create opportunities for ecosystem restoration on invaded lands that become climatically unsuitable for invasive species. Here, I develop a bioclimatic envelope model for cheatgrass (Bromus tectorum), a non-native invasive grass in the western US, based on its invaded distribution. The bioclimatic envelope model is based on the Mahalanobis distance using the climate variables that best constrain the species’ distribution. Of the precipitation and temperature variables measured, the best predictors of cheatgrass are summer, annual, and spring precipitation, followed by winter temperature. I perform a sensitivity analysis on potential cheatgrass distributions using the projections of 10 commonly used atmosphere–ocean general circulation models (AOGCMs) for 2100. The AOGCM projections for precipitation vary considerably, increasing uncertainty in the assessment of invasion risk. Decreased precipitation, particularly in the summer, causes an expansion of suitable land area by up to 45%, elevating invasion risk in parts of Montana, Wyoming, Utah, and Colorado. Conversely, increased precipitation reduces habitat by as much as 70%, decreasing invasion risk. The strong influence of precipitation conditions on this species’ distribution suggests that relying on temperature change alone to project future change in plant distributions may be inadequate. A sensitivity analysis provides a framework for identifying key climate variables that may limit invasion, and for assessing invasion risk and restoration opportunities with climate change.
223 citations
Cites background from "Extinction risk from climate change..."
...Climate change may lead to a shift of suitable ranges to higher altitudes and/or poleward (e.g., McCarty, 2001; Walther et al., 2002), loss of native species (Sala et al., 2000; Thuiller et al., 2005a), or even extinction (Thomas et al., 2004)....
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TL;DR: In this paper, the authors used presence-only modeling techniques and climatic data (minimum temperature, average temperature, precipitation, humidity and daily temperature range) for four different climate change scenarios (IPCC scenarios ranging from the most extreme A1FI, A2, B2 to the least severe, B1) to predict the potential geographic distribution of 28 European bat species in Europe grouped according to their biogeographic patterns for the years 2020-2030, 2050-2060 and 2090-2100.
Abstract: There has been considerable recent interest concerning the impact of climate change on a wide range of taxa. However, little is known about how the biogeographic affinities of taxa may affect their responses to these impacts. Our main aim was to study how predicted climate change will affect the distribution of 28 European bat species grouped by their biogeographic patterns as determined by a spatial Principal Component Analysis. Using presence-only modelling techniques and climatic data (minimum temperature, average temperature, precipitation, humidity and daily temperature range) for four different climate change scenarios (IPCC scenarios ranging from the most extreme A1FI, A2, B2 to the least severe, B1), we predict the potential geographic distribution of bat species in Europe grouped according to their biogeographic patterns for the years 2020-2030, 2050-2060 and 2090-2100. Biogeographic patterns exert a great influence on a species' response to climate change. Bat species more associated with colder climates, hence northern latitudes, could be more severely affected with some extinctions predicted by the end of the century. The Mediterranean and Temperate groups seem to be more tolerant of temperature increases, however, their projections varied considerably under different climate change scenarios. Scenario A1FI was clearly the most detrimental for European bat diversity, with several extinctions and declines in occupied area predicted for several species. The B scenarios were less damaging and even predicted that some species could increase their geographical ranges. However, all models only took into account climatic envelopes whereas available habitat and species interactions will also probably play an important role in delimiting future distribution patterns. The models may therefore generate 'best case' predictions about future changes in the distribution of European bats.
222 citations
Cites background from "Extinction risk from climate change..."
...Thomas et al. (2004) indicated that an alarming number of species may lose a part of their range and consequently become extinct....
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...In this process, local extinctions may occur and populations may become highly fragmented (Thomas et al., 2004)....
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...Several important climate change studies generated predictions with respect to taxonomic groups (e.g. Thomas et al., 2004; Araújo et al., 2006)....
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...However, it is usual for future climate change studies to cluster their results by taxa (Araújo et al., 2006) or species are simply grouped into a single dataset (Thomas et al., 2004)....
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...Matching phenology with future climatic conditions will also constitute one of the main pressures for bat populations, especially for example if temperature-driven gestation times become out of synchrony with food abundance (Sanz et al., 2003; Thomas et al., 2004)....
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References
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TL;DR: A ‘silver bullet’ strategy on the part of conservation planners, focusing on ‘biodiversity hotspots’ where exceptional concentrations of endemic species are undergoing exceptional loss of habitat, is proposed.
Abstract: Conservationists are far from able to assist all species under threat, if only for lack of funding. This places a premium on priorities: how can we support the most species at the least cost? One way is to identify 'biodiversity hotspots' where exceptional concentrations of endemic species are undergoing exceptional loss of habitat. As many as 44% of all species of vascular plants and 35% of all species in four vertebrate groups are confined to 25 hotspots comprising only 1.4% of the land surface of the Earth. This opens the way for a 'silver bullet' strategy on the part of conservation planners, focusing on these hotspots in proportion to their share of the world's species at risk.
24,867 citations
"Extinction risk from climate change..." refers background in this paper
...Second, for cerrado vegetation in Brazil, high rates of habitat destructio...
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TL;DR: In this article, the authors present an overview of the climate system and its dynamics, including observed climate variability and change, the carbon cycle, atmospheric chemistry and greenhouse gases, and their direct and indirect effects.
Abstract: Summary for policymakers Technical summary 1. The climate system - an overview 2. Observed climate variability and change 3. The carbon cycle and atmospheric CO2 4. Atmospheric chemistry and greenhouse gases 5. Aerosols, their direct and indirect effects 6. Radiative forcing of climate change 7. Physical climate processes and feedbacks 8. Model evaluation 9. Projections of future climate change 10. Regional climate simulation - evaluation and projections 11. Changes in sea level 12. Detection of climate change and attribution of causes 13. Climate scenario development 14. Advancing our understanding Glossary Index Appendix.
13,366 citations
TL;DR: A diagnostic fingerprint of temporal and spatial ‘sign-switching’ responses uniquely predicted by twentieth century climate trends is defined and generates ‘very high confidence’ (as laid down by the IPCC) that climate change is already affecting living systems.
Abstract: Causal attribution of recent biological trends to climate change is complicated because non-climatic influences dominate local, short-term biological changes. Any underlying signal from climate change is likely to be revealed by analyses that seek systematic trends across diverse species and geographic regions; however, debates within the Intergovernmental Panel on Climate Change (IPCC) reveal several definitions of a 'systematic trend'. Here, we explore these differences, apply diverse analyses to more than 1,700 species, and show that recent biological trends match climate change predictions. Global meta-analyses documented significant range shifts averaging 6.1 km per decade towards the poles (or metres per decade upward), and significant mean advancement of spring events by 2.3 days per decade. We define a diagnostic fingerprint of temporal and spatial 'sign-switching' responses uniquely predicted by twentieth century climate trends. Among appropriate long-term/large-scale/multi-species data sets, this diagnostic fingerprint was found for 279 species. This suite of analyses generates 'very high confidence' (as laid down by the IPCC) that climate change is already affecting living systems.
9,761 citations
"Extinction risk from climate change..." refers background in this paper
...gif" NDATA ITEM> ]> Climate change over the past ∼30 years has produced numerous shifts in the distributions and abundances of specie...
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University of Buenos Aires1, University of Alaska Fairbanks2, University of Chile3, University of California, Berkeley4, Environmental Defense Fund5, National Autonomous University of Mexico6, Technische Universität München7, New Mexico State University8, Duke University9, Arizona State University10, University of Notre Dame11, Stanford University12, Colorado State University13, Commonwealth Scientific and Industrial Research Organisation14
TL;DR: This study identified a ranking of the importance of drivers of change, aranking of the biomes with respect to expected changes, and the major sources of uncertainties in projections of future biodiversity change.
Abstract: Scenarios of changes in biodiversity for the year 2100 can now be developed based on scenarios of changes in atmospheric carbon dioxide, climate, vegetation, and land use and the known sensitivity of biodiversity to these changes. This study identified a ranking of the importance of drivers of change, a ranking of the biomes with respect to expected changes, and the major sources of uncertainties. For terrestrial ecosystems, land-use change probably will have the largest effect, followed by climate change, nitrogen deposition, biotic exchange, and elevated carbon dioxide concentration. For freshwater ecosystems, biotic exchange is much more important. Mediterranean climate and grassland ecosystems likely will experience the greatest proportional change in biodiversity because of the substantial influence of all drivers of biodiversity change. Northern temperate ecosystems are estimated to experience the least biodiversity change because major land-use change has already occurred. Plausible changes in biodiversity in other biomes depend on interactions among the causes of biodiversity change. These interactions represent one of the largest uncertainties in projections of future biodiversity change.
8,401 citations
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26 May 1995TL;DR: In this article, the authors present a hierarchical dynamic puzzle to understand the relationship between habitat diversity and species diversity and the evolution of the relationships between habitats diversity and diversity in evolutionary time.
Abstract: Preface 1 The road ahead 2 Patterns in space 3 Temporal patterns 4 Dimensionless patterns 5 Speciation 6 Extinction 7 Evolution of the relationship between habitat diversity and species diversity 8 Species-area curves in ecological time 9 Species-area curves in evolutionary time 10 Paleobiological patterns 11 Other patterns with dynamic roots 12 Energy flow and diversity 13 A hierarchical dynamic puzzle References Index
4,812 citations