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116 results for “climate risk”
Data from: On the use of climate covariates in aquatic species distribution models: are we at risk of throwing the baby out?
Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum Entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: (1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or (2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs. instream models. First, equivalence tests assessed average pairwise differences (site-specific comparisons throughout each species' range) among climate and instream models. Second, 'congruence' tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's <i>D</i> and Warren's <i>I</i> niche overlap statistics quantified range-wide similarity in predicted habitat suitability values from climate vs. instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3×10<sup>-4</sup>). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). <i>D</i> and <i>I</i> statistics reflected a high margin of overlap among climate and instream models (median <i>D</i> = 0.78, median <i>I</i> = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species' distributions at the scale of the Columbia Basin.
Local extinction risk under climate change in a neotropical asymmetrically dispersed epiphyte
1. The long-term fate of populations experiencing disequilibrium conditions with their environment will ultimately depend on how local colonization and extinction dynamics respond to abiotic conditions (e.g. temperature and rainfall), dispersal limitation and biotic interactions (e.g. competition, facilitation, or interactions with natural enemies). Understanding how these factors influence distributional dynamics under climate change is a major knowledge gap, particularly for small ranged and dispersal-limited plant species, which are at higher risk of extinction. Epiphytes are hypothesized to be particularly vulnerable to climate change and we know little about what drives their distribution and how they will respond to climate change. To address this issue, we leveraged a 10-year data set on the occupancy dynamics of the endemic orchid Lepanthes rupestris to identify the drivers of local colonization and extinction dynamics and assess the long-term fate of this population under multiple climate change scenarios. 2. We compared 290 dynamic occupancy models in their ability to predict the colonization and extinction dynamics of a L. rupestris metapopulation. The model set predicted colonization-extinction dynamics as a function of asymmetric patch connectivity, moss area, elevation, temperature (minimum, maximum and variability), and/or rainfall. 3. The best model predicted that local colonization increases with increasing asymmetric patch connectivity but decreases as minimum temperature and maximum temperature variability increase. The best model also predicted that local extinction increases with increasing variability in maximum temperature. Negative effects were more severe in smaller patches. 4. Synthesis: Overall, our results demonstrate the role of asymmetric connectivity, climate and interactions with moss area as drivers of colonization and extinction dynamics. Moreover, our results suggest that asymmetrically dispersed epiphytes may struggle to persist under climate change because their limited connectivity may not be enough to counterbalance the negative effects of increasing mean or variability in temperature.
"Natural" solutions could speed up climate change mitigation, with risks, compared to emissions reductions alone. Additional options are needed.
<p>Mitigation of climate change by intentionally storing carbon in tropical forests, soils and wetlands, and by reducing greenhouse gas fluxes from these settings has been promoted as rapidly deployable and cost-effective. This approach, sometimes referred to as "natural" mitigation, could keep post-industrialization warming below 1.5°C, when coupled with reductions in fossil fuel emissions, as confirmed here with a simple numerical model of future emissions. However, such mitigation could cease in response to changes in future climate, land use or natural resource policies, or there could be CO<sub>2</sub> released from reservoirs of stored carbon. Model simulations suggest cumulative emissions could be similar, under scenarios where carbon storage ceases, or stored carbon is released, to emissions expected in the absence of any natural mitigation. If climate change is to be minimized, low-risk natural mitigation (e.g. by reducing deforestation) should be considered, as emissions targets that could limit warming to 1.5°C cannot be met without mitigation of this magnitude. However, additional mitigation options should also be considered that can reduce CO<sub>2</sub> emissions and remove CO<sub>2</sub> from the air (and store it permanently) and/or reduce the temperature of the atmosphere or the ocean.</p>
The nutritional condition of moose co-varies with climate, but not with density, predation risk, or diet composition
<p>A fundamental question about the ecology of herbivore populations pertains to the relative influence of biotic and abiotic processes on nutritional condition. Nutritional condition is influenced in important, yet poorly understood, ways by plant secondary metabolites (PSMs) which can adversely affect a herbivore's physiology and energetics. Here we assess the relative influence of various abiotic (weather) and biotic (intraspecific competition, predation risk and diet composition) factors on indicators of nutritional condition and the energetic costs of detoxifying PSMs for the moose population in Isle Royale National Park (U.S.A.). Specifically, we observed interannual variation in the ratio of urea nitrogen to creatinine (UN:C), an indicator of nutritional restriction, over 29 years and the ratio of glucuronic acid to creatinine (GA:C), an indicator of energetic investment, in detoxifying PSMs over 19-years. Both UN:C and GA:C were measured in samples of urine-soaked snow. Most importantly, climatic factors explained 66% of the interannual variation in UN:C, with moose being more nutritionally stressed during winters with deep snow and during winters that followed warm summers. None of the biotic factors (density, predation, diet composition) were useful predictors of UN:C or GA:C. The absence of a relationship between diet composition and either UN:C or GA:C suggests that the nutritional ecology of wild herbivores is probably complicated by fine-scale variation in protein content and concentrations of PSMs amongst plants of the same species. UN:C increased with GA:C at both the individual and population-level. That result is consistent with detoxification being energetically costly, such that it impairs nutritional condition and also highlight show spatio-temporal variation in the intake and detoxification of PSMs may influence population dynamics. Lastly, because we observed interannual variation in nutritional condition over three decades and detoxification over two decades these findings are relevant to concerns about how herbivore populations respond to climate change.</p>
Supplementary material 1 from: Guilder J, Copp GH, Thrush MA, Stinton N, Murphy D, Murray J, Tidbury HJ (2022) Threats to UK freshwaters under climate change: Commonly traded aquatic ornamental species and their potential pathogens and parasites. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 73-108. https://doi.org/10.3897/neobiota.76.80215
Threats to UK freshwaters under climate change: Commonly traded aquatic ornamental species and their potential pathogens and parasites
Supplementary material 1 from: Marić A, Špelić I, Radočaj T, Vidović Z, Kanjuh T, Vilizzi L, Piria M, Nikolić V, Škraba Jurlina D, Mrdak D, Simonović P (2022) Changing climate may mitigate the invasiveness risk of non-native salmonids in the Danube and Adriatic basins of the Balkan Peninsula (south-eastern Europe). In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 135-161. https://doi.org/10.3897/neobiota.76.82964
Combined AS-ISK report including the 68 screenings for the 17 salmonid species screened for the Danube and Adriatic basins of Bosnia and Herzegovina, Croatia, Montenegro and Serbia (including Kosovo)
Supplementary material 2 from: Piria M, Radočaj T, Vilizzi L, Britvec M (2022) Climate change may exacerbate the risk of invasiveness of non-native aquatic plants: the case of the Pannonian and Mediterranean regions of Croatia. In: Giannetto D, Piria M, Tarkan AS, Zięba G (Eds) Recent advancements in the risk screening of freshwater and terrestrial non-native species. NeoBiota 76: 25-52. https://doi.org/10.3897/neobiota.76.83320
Combined AS-ISK report for the 24 non-native aquatic plant species screened for their potential risk of invasiveness in the Pannonian and Mediterranean regions of Croatia.
Supporting material for "Assessing the Climate Transition Value at Risk in the Colombian Food Sector".
<p>This repository contains values and code for generating the dataset used in the study "<span>Assessing the Climate Transition Value at Risk in the Colombian </span><span>Food Sector</span>".</p>
Data for "Elevated urban energy risks due to climate-driven biophysical feedbacks"
<p>This dataset contains the global multi-model urban climate and energy projections from Li et al. (2024), "Elevated urban energy risks due to climate-driven biophysical feedbacks", published in <em>Nature Climate Change</em>. It contains global monthly mean projections of urban 2-meter air temperature, and urban cooling and heating energy fluxes derived from 25 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). Details about how this dataset was generated are described in the article. This dataset may be useful for multiple communities interested in future energy risks, climate change impacts and vulnerability, and climate-sensitive adaptation and energy planning.</p> <p>For more details, please refer to the README.md file included in the dataset.</p>
The conjunction of climate warming and earthquake exacerbates the risk of river blockages by landslides in the Sedongpu Gully, Eastern Himalaya
<p>The datasets and results of <span>The conjunction of climate warming and earthquake exacerbates the risk of river blockages by landslides in the Sedongpu Gully, Eastern Himalaya, that has been submitted in Journal Geophysical Research Letters. </span></p>
Fig. 5 in Niche overlap and host specificity in parasitic Maculinea butterflies (Lepidoptera: Lycaenidae) as a measure for potential extinction risks under climate change
Fig. 5 Niche identity tests of Maculinea/Myrmica host associations under the A2a climate change scenario for 2080. The red arrow indicates the measured niche overlap between hosts and parasites
Fig. 3 in Niche overlap and host specificity in parasitic Maculinea butterflies (Lepidoptera: Lycaenidae) as a measure for potential extinction risks under climate change
Fig. 3 Niche identity tests of Maculinea/Myrmica host associations under current climate. The red arrow indicates the measured niche overlap between hosts and parasites derived from ENMs generated
Fig. 2 in Niche overlap and host specificity in parasitic Maculinea butterflies (Lepidoptera: Lycaenidae) as a measure for potential extinction risks under climate change
Fig. 2 Estimated potential distributions of Maculinea butterflies (light grey) and Myrmica ants (dark grey) under current climatic conditions show large geographic overlaps of the butterfly species with their respective main (red) and secondary hosts (orange)
Climate risk index for Italy
<p>We describe a climate risk index that has been developed to inform national climate adaptation planning in Italy and that is further elaborated in this paper. The index supports national authorities in designing adaptation policies and plans, guides the initial problem formulation phase, and identifies administrative areas with higher propensity to being adversely affected by climate change. The index combines (i) climate change-amplified hazards; (ii) high-resolution indicators of exposure of chosen economic, social, natural and built- or manufactured capital (MC) assets and (iii) vulnerability, which comprises both present sensitivity to climateinduced hazards and adaptive capacity. We use standardized anomalies of selected extreme climate indices derived from high-resolution regional climate model simulations of the EURO-CORDEX initiative as proxies of climate change-altered weather and climate-related hazards. The exposure and sensitivity assessment is based on indicators of manufactured, natural, social and economic capital assets exposed to and adversely affected by climate-related hazards. The MC refers to material goods or fixed assets which support the production process (e.g. industrial machines and buildings); Natural Capital comprises natural resources and processes (renewable and non-renewable) producing goods and services for well-being; Social Capital (SC) addressed factors at the individual (people’s health, knowledge, skills) and collective (institutional) level (e.g. families, communities, organizations and schools); and Economic Capital (EC) includes owned and traded goods and services. The results of the climate risk analysis are used to rank the subnational administrative and statistical units according to the climate risk challenges, and possibly for financial resource allocation for climate adaptation. This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.</p>
Desiccation amelioration and climate risk on rocky shores
<p>A large fraction of global biodiversity resides within biogenic habitats that ameliorate physical stresses. In most cases, details of how physical conditions within facilitative habitats respond to external climate forcing remain unknown, hampering climate change predictions for many of the world's species. Using intertidal mussel beds as a model system, we characterize relationships among external climate conditions and within-microhabitat heat and desiccation conditions. We use these data, along with physiological tolerances of two common inhabitant taxa (the isopod <i>Cirolana harfordi</i> and the porcelain crab <i>Petrolisthes cinctipes</i>), to examine the magnitude of climate risk inside and outside biogenic habitat, applying an empirically derived model of evaporation to simulate mortality risk under a high-emissions climate-warming scenario. We found that biogenic microhabitat conditions responded so weakly to external climate parameters that mortality risk was largely unaffected by climate warming. We also found that desiccation outside the biogenic habitat drove substantial mortality in both species at temperatures 4.4 to 8.6 ºC below their hydrated thermal tolerances. This finding emphasizes the importance of warming-exacerbated desiccation to climate-change risk and the role of biogenic habitats in buffering this less-appreciated stressor. Our results suggests that, when biogenic habitats remain intact, climate warming may have weak direct effects on organisms within them. Instead, risk to such taxa is likely to be indirect and tightly coupled with the fate of habitat-forming populations. Our findings emphasize that conserving and/or restoring biogenic habitats that offer climate refugia could support biodiversity conservation in the face of climate warming.</p>
POD6, POD0, O3 concentrations, and Jarvis functions in order to assess the global flux-based ozone risk for wheat up to 2100 under different climate scenarios
<p>Model output associated with the study <em>“Global flux-based assessment reveals declining ozone risk for wheat in future climate change scenarios”</em> (Guaita <em>et al.</em>, 2025).</p> <p>The output is provided under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. Please cite <strong>both this repository and the associated paper</strong> when referencing this output.</p> <p><strong>Associated paper:</strong></p> <blockquote> <p><strong>Guaita, P., et al.</strong> (2025).<br><em>Global flux-based assessment reveals declining ozone risk for wheat in future climate change scenarios.</em><br><em>Global Change Biology (Under review)</em>.<br><a href="https://doi.org/10.xxxx/xxxxx" target="_new" rel="noopener">https://doi.org/10.xxxx/xxxxx</a></p> </blockquote> <p><strong>Model documentation:</strong></p> <blockquote> <p><strong>Guaita, P. R., Marzuoli, R., & Gerosa, G.</strong> (2023).<br><em>A regional scale flux-based O₃ risk assessment for winter wheat in northern Italy, and effects of different spatio-temporal resolutions.</em><br><em>Environmental Pollution</em>, 333, 121860.<br><a href="https://doi.org/10.1016/j.envpol.2023.121860" target="_new" rel="noopener">https://doi.org/10.1016/j.envpol.2023.121860</a></p> </blockquote> <p><strong>Model code:</strong><br>See the GitHub repository <a href="https://github.com/prguaita/O3-Deposition-model-for-wheat"><em>O3-Deposition-model-for-wheat</em></a> (© 2025 Guaita & Gerosa. All rights reserved).</p> <p>⚠️ <strong>Warning:</strong><br>Do <strong>not</strong> cite the preprint <a href="https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2573/?utm_source=chatgpt.com" target="_new" rel="noopener">https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2573/</a> — this version is <strong>deprecated</strong>.</p>
Data for The missing risks of climate change
<p>This repository contains the data behind the quantitative figures (figures 1, 3, and 4) in Rising, James, et al. "The missing risks of climate change." <em>Nature</em> 610.7933 (2022): 643-651. https://www.nature.com/articles/s41586-022-05243-6.</p> <p>The figures directory contains the figures (in their accepted paper form). The data directory contains CSV files with the associated data. The rows and columns are defined as follows:</p> <p> - fig1-mc.csv: Rows describe Monte Carlo draws describing the uncertainty for each scenario (SSP1-2.6 and SSP3-7.0) and outcome variable (in the "variable" column). The "run" column describes the basis for each draw of the uncertainty (e.g., model used). The "unit" column provides the units for the "value" column. The "compound" column is TRUE if the rows report compounded uncertainty, and false if the uncertainty is only from the individual analysis stage.</p> <p> - fig3-zscores.csv: Each row is a grid cell across the globe, reporting z-scores for various hazards and the population from GPW v4.0 (https://sedac.ciesin.columbia.edu/data/collection/gpw-v4). The z-scores are calculated compared to the recent history (1980-2010) from either longer historical data from CRU TS, with the column prefix "hist.", or from SSP3-7.0 estimates from WorldClim bioclimatic variables for 2050, with the column prefix "ssp370.". Column suffixes describe various hazards: "wet" is average precipitation in the wettest month, "dry" is annual precipitation, "logwet" is as "wet" but evaluated in logs, "logdry" is as "dry" but evaluated in logs, "pcv" is precipitation seasonality (coefficient of variation), "hot" is the maximum temperature of the warmest month, and "cld" is the minimum temperature of the coldest month. "topcol" reports the column with the most extreme z-score (with the z-score in "topscore" and a label in "toplabel").</p> <p> - fig4-dmgfunc.csv: Monte Carlo draws of the uncertainty in number of people affected across 16 impacts, reported in "affected" as a fraction of the global population, for each temperature change from preindustrial, reported in "temp".</p> <p> - fig4-pdfs.csv: Monte Carlo draws of the uncertainty in the number of people affected for each of 16 impacts and four aggregates. The fraction of the global population affected in reported in "affected" for the temperature change from preindustrial reported in "temp". The impact is labeled in "name" and the aggregate category is reported in "rname".</p> <p>Additional details on the generation of these data are included in the SI of the paper.</p>
Data from: Evolutionary constraints mediate extinction risk under climate change
<p>Mounting evidence suggests that rapid evolutionary adaptation may rescue some organisms from the impacts of climate change. However, evolutionary constraints might hinder this process, especially when different aspects of environmental change generate antagonistic selection on genetically correlated traits. Here, we use individual-based simulations to explore how genetic correlations underlying the thermal physiology of ectotherms might influence their responses to the two major components of climate change—increases in mean temperature and thermal variability. We found that genetic correlations can influence population dynamics under climate change, with declines in population size varying three-fold depending on the type of correlation present. Surprisingly, populations whose thermal performance curves were constrained by genetic correlations often declined less rapidly than unconstrained populations. Our results suggest that accurate forecasts of the impact of climate change on ectotherms will require an understanding of the genetic architecture of the traits under selection.</p>
Estuarine Hypoxia – Identifying High Risk Catchments Now and Under Future Climate Scenarios - Water Level Dataset
<p>Historic water level data used in determining the inundation characteristics for each of the catchments within the study area. The locations of each water level gauge, a summary of the distribution of water levels and the distribution of data to each catchment is detailed in the Supporting Information accompanying the manuscript "Estuarine Hypoxia – Identifying High Risk Catchments Now and Under Future Climate Scenarios".</p>
Data from: Evolutionary constraints mediate extinction risk under climate change
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