Considering adaptive genetic variation in climate change vulnerability assessment reduces species range loss projections.
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ABSTRACT:
SUBMITTER: Razgour O
PROVIDER: S-EPMC6535011 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
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Project description:A cost-effective climate change adaptation strategy for the forestry sector is to move seed sources to more northern and higher elevation planting sites as part of ongoing reforestation programs. This is meant to match locally adapted populations with anticipated environments, but adaptive traits do not always show population differences suitable to mitigate climate change impacts. For white spruce, drought tolerance is a critical adaptive trait to prevent mortality and productivity losses. Here, we use a 40-year-old provenance experiment that has been exposed to severe drought periods in 1999 and 2002 to retrospectively investigate drought response and the adaptive capacity of white spruce populations across their boreal range. Relying on dendrochronological analysis under experimentally controlled environments, we evaluate population differences in resistance, resilience, and recovery to these extreme events. Results showed evidence for population differentiation in resistance and recovery parameters, but provenances conformed to approximately the same growth rates under drought conditions and had similar resilience metrics. The lack of populations with better growth rates under drought conditions is contrary to expectations for a wide-ranging species with distinct regional climates. Populations from the wettest environments in the northeastern boreal were surprisingly drought-tolerant, suggesting that these populations would readily resist water deficits projected for the 2080s, and supporting the view that northeastern Canada will provide a refugium for boreal species under climate change. The findings also suggest that white spruce is sensitive to growth reductions under climate change in the western boreal. The study highlights that population differentiation in adaptive capacity is species- and trait-specific, and we provide a counterexample for drought tolerance traits, where assisted migration prescriptions may be ineffective to mitigate climate change impacts. For resource managers and policy makers, we provide maps where planning for widespread declines of boreal white spruce forests may be unavoidable.
| S-EPMC6752154 | biostudies-literature
Project description:Climate change vulnerability assessment (CCVA) has become a mainstay conservation decision support tool. CCVAs are recommended to incorporate three elements of vulnerability - exposure, sensitivity and adaptive capacity - yet, lack of data frequently leads to the latter being excluded. Further, weighted or unweighted scoring schemes, based on expert opinion, may be applied. Comparisons of these approaches are rare. In a CCVA for 17 Australian lizard species, we show that membership within three vulnerability categories (low, medium and high) generally remained similar regardless of the framework or scoring scheme. There was one exception however, where, under the warm/dry scenario for 2070, including adaptive capacity lead to five fewer species being classified as highly vulnerable. Two species, Eulamprus leuraensis and E. kosciuskoi, were consistently ranked the most vulnerable, primarily due to projected losses in climatically suitable habitat, narrow thermal tolerance and specialist habitat requirements. Our findings provide relevant information for prioritizing target species for conservation and choosing appropriate conservation actions. We conclude that for the species included in this study, the framework and scoring scheme used had little impact on the identification of the most vulnerable species. We caution, however, that this outcome may not apply to other taxa or regions.
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Project description:BackgroundAlthough the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community's exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard's impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity.MethodsA systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped.ResultsTwo hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16-49%) across all hazards. Distinct spatial patterns were observed - for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region.ConclusionsThe creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.
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Project description:The relationships among species' physiological capacities and the geographical variation of ambient climate are of key importance to understanding the distribution of life on the Earth. Furthermore, predictions of how species will respond to climate change will profit from the explicit consideration of their physiological tolerances. The climatic variability hypothesis, which predicts that climatic tolerances are broader in more variable climates, provides an analytical framework for studying these relationships between physiology and biogeography. However, direct empirical support for the hypothesis is mostly lacking for endotherms, and few studies have tried to integrate physiological data into assessments of species' climatic vulnerability at the global scale. Here, we test the climatic variability hypothesis for endotherms, with a comprehensive dataset on thermal tolerances derived from physiological experiments, and use these data to assess the vulnerability of species to projected climate change. We find the expected relationship between thermal tolerance and ambient climatic variability in birds, but not in mammals-a contrast possibly resulting from different adaptation strategies to ambient climate via behaviour, morphology or physiology. We show that currently most of the species are experiencing ambient temperatures well within their tolerance limits and that in the future many species may be able to tolerate projected temperature increases across significant proportions of their distributions. However, our findings also underline the high vulnerability of tropical regions to changes in temperature and other threats of anthropogenic global changes. Our study demonstrates that a better understanding of the interplay among species' physiology and the geography of climate change will advance assessments of species' vulnerability to climate change.
| S-EPMC4100521 | biostudies-literature
Project description:Climate change is affecting biodiversity worldwide, but conservation responses are constrained by considerable uncertainty regarding the magnitude, rate and ecological consequences of expected climate change. Here we propose a framework to account for several sources of uncertainty in conservation prioritization. Within this framework we account for uncertainties arising from (i) species distributions that shift following climate change, (ii) basic connectivity requirements of species, (iii) alternative climate change scenarios and their impacts, (iv) in the modelling of species distributions, and (v) different levels of confidence about present and future. When future impacts of climate change are uncertain, robustness of decision-making can be improved by quantifying the risks and trade-offs associated with climate scenarios. Sensible prioritization that accounts simultaneously for the present and potential future distributions of species is achievable without overly jeopardising present-day conservation values. Doing so requires systematic treatment of uncertainties and testing of the sensitivity of results to assumptions about climate. We illustrate the proposed framework by identifying priority areas for amphibians and reptiles in Europe.
| S-EPMC3566137 | biostudies-literature
Project description:Desertification is the degradation of drylands, which occupy an increasing proportion of the Earth's surface due to global warming. It is currently the most extensive biome on Earth, occupying 45% and one out of every three inhabitants of the planet live in them. One of the most effective ways to face desertification, as Land Degradation Neutrality points out, is prevention. For this purpose, simulation models are very useful tools. Specifically, System Dynamics models are particularly effective, since they allow bringing together the biophysical and socioeconomic variables involved in the formation of the problem. These integrative models, coupled with other tools such as sensitivity analyses, are used to generate desertification early warning indicators. The objective of this programming routine is to implement climate change scenarios in these simulation models. The script presented here was used to evaluate the sensitivity of dehesa rangelands productivity to the increase in the frequency and intensity of droughts due to climate change.•Integrated simulation models are useful tools to understand complex socioecosystems.•Land-use changes foster the alteration of key hydro-bio-geochemical processes.•By means of automated import processes and data analysis programming, it is possible to implement desertification early warning systems.
| S-EPMC9925954 | biostudies-literature
Project description:BackgroundVarious retrospective studies have reported on the increase of mortality risk due to higher diurnal temperature range (DTR). This study projects the effect of DTR on future mortality across 445 communities in 20 countries and regions.MethodsDTR-related mortality risk was estimated on the basis of the historical daily time-series of mortality and weather factors from Jan 1, 1985, to Dec 31, 2015, with data for 445 communities across 20 countries and regions, from the Multi-Country Multi-City Collaborative Research Network. We obtained daily projected temperature series associated with four climate change scenarios, using the four representative concentration pathways (RCPs) described by the Intergovernmental Panel on Climate Change, from the lowest to the highest emission scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). Excess deaths attributable to the DTR during the current (1985-2015) and future (2020-99) periods were projected using daily DTR series under the four scenarios. Future excess deaths were calculated on the basis of assumptions that warmer long-term average temperatures affect or do not affect the DTR-related mortality risk.FindingsThe time-series analyses results showed that DTR was associated with excess mortality. Under the unmitigated climate change scenario (RCP 8.5), the future average DTR is projected to increase in most countries and regions (by -0·4 to 1·6°C), particularly in the USA, south-central Europe, Mexico, and South Africa. The excess deaths currently attributable to DTR were estimated to be 0·2-7·4%. Furthermore, the DTR-related mortality risk increased as the long-term average temperature increased; in the linear mixed model with the assumption of an interactive effect with long-term average temperature, we estimated 0·05% additional DTR mortality risk per 1°C increase in average temperature. Based on the interaction with long-term average temperature, the DTR-related excess deaths are projected to increase in all countries or regions by 1·4-10·3% in 2090-99.InterpretationThis study suggests that globally, DTR-related excess mortality might increase under climate change, and this increasing pattern is likely to vary between countries and regions. Considering climatic changes, our findings could contribute to public health interventions aimed at reducing the impact of DTR on human health.FundingKorea Ministry of Environment.
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Project description:Understanding how the vulnerability of agricultural production to climate change can differ spatially has practical significance to sustainable management of agricultural systems worldwide. Accordingly, this study developed a conceptual framework to assess the agricultural vulnerability of 243 rural counties on the Chinese Loess Plateau. Indicators representing the climate/agriculture interface were selected to describe exposure and sensitivity, while stocks of certain capitals were used to describe adaptive capacity. A vulnerability index for each county was calculated and the spatial distribution was mapped. Results showed that exposure, sensitivity, and adaptive capacity occur independently, with most contributing indicator values concentrated in a narrow range after normalization. Within the 49 most vulnerable counties, which together encompass 81 % of the vulnerability index range, 42 were characterized by high exposure and sensitivity but low adaptive capacity. The most vulnerable area was found to be located in the central northeast-southwest belt of Loess Plateau. Adaptation measures for both ecological restoration and economic development are needed and potential adaptation options need further investigation.
| S-EPMC4815761 | biostudies-literature
Project description:Background and aimsUnderstanding adaptive genetic variation and whether it can keep pace with predicted future climate change is critical in assessing the genetic vulnerability of species and developing conservation management strategies. The lack of information on adaptive genetic variation in relict species carrying abundant genetic resources hinders the assessment of genetic vulnerability. Using a landscape genomics approach, this study aimed to determine how adaptive genetic variation shapes population divergence and to predict the adaptive potential of Pterocarya macroptera (a vulnerable relict species in China) under future climate scenarios.MethodsWe applied restriction site-associated DNA sequencing (RAD-seq) to obtain 8244 single-nucleotide polymorphisms (SNPs) from 160 individuals across 28 populations. We examined the pattern of genetic diversity and divergence, and then identified outliers by genetic differentiation (FST) and genotype-environment association (GEA) methods. We further dissected the effect of geographical/environmental gradients on genetic variation. Finally, we predicted genetic vulnerability and adaptive risk under future climate scenarios.Key resultsWe identified three genetic lineages within P. macroptera: the Qinling-Daba-Tianmu Mountains (QDT), Western Sichuan (WS) and Northwest Yunnan (NWY) lineages, which showed significant signals of isolation by distance (IBD) and isolation by environment (IBE). IBD and IBE explained 3.7-5.7 and 8.6-12.8 % of the genetic structure, respectively. The identified GEA SNP-related genes were involved in chemical defence and gene regulation and may exhibit higher genetic variation to adapt to the environment. Gradient forest analysis revealed that the genetic variation was mainly shaped by temperature-related variables, indicating its adaptation to local thermal environments. A limited adaptive potential was suggested by the high levels of genetic vulnerability in marginal populations.ConclusionsEnvironmental gradient mainly shaped the population differentiation of P. macroptera. Marginal populations may be at high risk of extinction, and thus proactive management measures, such as assisted gene flow, are required to ensure the survival of these populations.
| S-EPMC10583204 | biostudies-literature
Project description:BackgroundMost studies that have assessed impacts on mortality of future temperature increases have relied on a small number of simulations and have not addressed the variability and sources of uncertainty in their mortality projections.ObjectivesWe assessed the variability of temperature projections and dependent future mortality distributions, using a large panel of temperature simulations based on different climate models and emission scenarios.MethodsWe used historical data from 1990 through 2007 for Montreal, Quebec, Canada, and Poisson regression models to estimate relative risks (RR) for daily nonaccidental mortality in association with three different daily temperature metrics (mean, minimum, and maximum temperature) during June through August. To estimate future numbers of deaths attributable to ambient temperatures and the uncertainty of the estimates, we used 32 different simulations of daily temperatures for June-August 2020-2037 derived from three global climate models (GCMs) and a Canadian regional climate model with three sets of RRs (one based on the observed historical data, and two on bootstrap samples that generated the 95% CI of the attributable number (AN) of deaths). We then used analysis of covariance to evaluate the influence of the simulation, the projected year, and the sets of RRs used to derive the attributable numbers of deaths.ResultsWe found that < 1% of the variability in the distributions of simulated temperature for June-August of 2020-2037 was explained by differences among the simulations. Estimated ANs for 2020-2037 ranged from 34 to 174 per summer (i.e., June-August). Most of the variability in mortality projections (38%) was related to the temperature-mortality RR used to estimate the ANs.ConclusionsThe choice of the RR estimate for the association between temperature and mortality may be important to reduce uncertainty in mortality projections.
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