{"id":493056,"date":"2026-06-10T20:19:26","date_gmt":"2026-06-10T20:19:26","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/493056\/"},"modified":"2026-06-10T20:19:26","modified_gmt":"2026-06-10T20:19:26","slug":"a-global-early-warning-system-for-predicting-exposure-of-biodiversity-to-extreme-heat","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/493056\/","title":{"rendered":"A global early warning system for predicting exposure of biodiversity to extreme heat"},"content":{"rendered":"

IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Hans-Otto P\u00f6rtner, H.-O. & Roberts, D. C.) (Cambridge Univ. Press, 2023).<\/p>\n

Renner, H. M. et al. Catastrophic and persistent loss of common murres after a marine heatwave. Science 386, 1272\u20131276 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mahecha, M. D. et al. Biodiversity and climate extremes: known interactions and research gaps. Earth\u2019s Future 12, e2023EF003963 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770\u20131774 (2000).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Boult, V. L. Forecast-based action for conservation. Conserv. Biol. 37, e14054 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Aswin, K. S., Dorji, U., Sherub, K. & Gurung, M. M. Waterhole utilization pattern of mammals in Jigme Singye Wangchuck National Park, Bhutan. J. Threat. Taxa 17, 26331\u201326340 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Perkins, J. in Sustainability in Developing Countries: Case Studies From Botswana\u2019s Journey Towards 2030 Agenda (eds Keitumetse, S. O. et al.) 51\u201389 (Springer, 2020).<\/p>\n

Leggett, K. Effect of artificial water points on the movement and behaviour of desert-dwelling elephants of north-western Namibia. Pachyderm 40, 24\u201334 (2006).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ratnayake, H. U., Kearney, M. R., Govekar, P., Karoly, D. & Welbergen, J. A. Forecasting wildlife die-offs from extreme heat events. Anim. Conserv. 22, 386\u2013395 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Beaman, J. E. et al. Resident wild koalas show resilience to large-scale translocation of bushfire-rescued koalas. Conserv. Physiol. 11, coac088 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Matthews, K. R. Response of mountain yellow-legged frogs, Rana muscosa, to short distance translocation. J. Herpetol. 37, 621\u2013626 (2003).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Murray, M. H., Becker, D. J., Hall, R. J. & Hernandez, S. M. Wildlife health and supplemental feeding: a review and management recommendations. Biol. Conserv. 204, 163\u2013174 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Milner, J. M., Van Beest, F. M., Schmidt, K. T., Brook, R. K. & Storaas, T. To feed or not to feed? Evidence of the intended and unintended effects of feeding wild ungulates. J. Wildl. Manage. 78, 1322\u20131334 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Prugh, L. R. et al. Ecological winners and losers of extreme drought in California. Nat. Clim. Change 8, 819\u2013824 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dietze, M. C. et al. Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl Acad. Sci. USA 115, 1424\u20131432 (2018).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dietze, M. et al. Near-term ecological forecasting for climate change action. Nat. Clim. Change 14, 1236\u20131244 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lala, J., Lee, D., Bazo, J. & Block, P. Evaluating prospects for subseasonal-to-seasonal forecast-based anticipatory action from a global perspective. Weather Clim. Extrem. 38, 100510 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ewers, R. M. An audacious approach to conservation. Trends Ecol. Evol. 39, 995\u20131003 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bradford, J. B., Betancourt, J. L., Butterfield, B. J., Munson, S. M. & Wood, T. E. Anticipatory natural resource science and management for a changing future. Front. Ecol. Environ. 16, 295\u2013303 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Foden, W. B. et al. Climate change vulnerability assessment of species. Wiley Interdiscip. Rev. Clim. Change 10, e551 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: biodiversity conservation in a changing climate. Science 332, 53\u201358 (2011).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Molod, A. et al. GEOS-S2S version 2: the GMAO high-resolution coupled model and assimilation system for seasonal prediction. J. Geophys. Res. Atmos. 125, e2019JD031767 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Slater, L. J., Villarini, G. & Bradley, A. A. Evaluation of the skill of North-American Multi-Model Ensemble (NMME) global climate models in predicting average and extreme precipitation and temperature over the continental USA. Clim. Dyn. 53, 7381\u20137396 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Roy, T. et al. Global evaluation of seasonal precipitation and temperature forecasts from NMME. J. Hydrometeor. 21, 2473\u20132486 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lim, Y.-K., Arnold, N. P., Molod, A. M. & Pawson, S. Seasonality in prediction skill of the Madden-Julian oscillation and associated dynamics in version 2 of NASA\u2019s GEOS-S2S forecast system. J. Geophys. Res. Atmos. 126, e2021JD034961 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lim, Y., Molod, A. M., Koster, R. D. & Santanello, J. A. The role of land\u2013atmosphere coupling in subseasonal surface air temperature prediction. Hydrol. Earth Syst. Sci. 29, 3435\u20133445 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ren, L., Hackert, E. C., Molod, A. & Nakada, K. Evaluation of marine heatwaves forecast in GEOS-S2S version 3 system. In American Geophysical Union, Ocean Sciences Meeting (2024).<\/p>\n

Thomas, N., Bosilovich, M. & Li, Z. Seasonal prediction of an extreme temperature index over the United States. In 104th American Meteorological Society (AMS) Annual Meeting (2024).<\/p>\n

Vitart, F. & Robertson, A. W. The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Clim. Atmos. Sci. 1, 3 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kaufman, D. S. & McKay, N. P. Past and future warming\u2013direct comparison on multi-century timescales. Clim. Past 18, 911\u2013917 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Clusella-Trullas, S., Garcia, R. A., Terblanche, J. S. & Hoffmann, A. A. How useful are thermal vulnerability indices?. Trends Ecol. Evol. 36, 1000\u20131010 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. 111, 5610\u20135615 (2014).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704\u2013706 (2010).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gallagher, R. V., Allen, S. & Wright, I. J. Safety margins and adaptive capacity of vegetation to climate change. Sci. Rep. 9, 8241 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325\u2013331 (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Charnov, E. L. & Gillooly, J. F. Size and temperature in the evolution of fish life histories. Integr. Comp. Biol. 44, 494\u2013497 (2004).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species\u2019 ranges. Ecol. Lett. 12, 334\u2013350 (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cleland, E. E. et al. Phenological tracking enables positive species responses to climate change. Ecology 93, 1765\u20131771 (2012).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Foden, W. B. et al. Identifying the world\u2019s most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PLOS ONE 8, e65427 (2013).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579\u2013587 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hawkins, E. et al. Observed emergence of the climate change signal: from the familiar to the unknown. Geophys. Res. Lett. 47, e2019GL086259 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534\u2013545 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bland, L. M., Keith, D. A., Miller, R. M., Murray, N. J. & Rodr\u00edguez, J. P. Guidelines for the Application of IUCN Red List of Ecosystems Categories and Criteria Version 2.0 (IUCN, 2017).<\/p>\n

Harrison, P. A. et al. Linkages between biodiversity attributes and ecosystem services: a systematic review. Ecosyst. Serv. 9, 191\u2013203 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gaston, K. J. et al. Population abundance and ecosystem service provision: the case of birds. BioScience 68, 264\u2013272 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

S\u00e4ttele, M., Br\u00fcndl, M. & Straub, D. Quantifying the effectiveness of early warning systems for natural hazards. Nat. Hazards Earth Syst. Sci. 16, 149\u2013166 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ma, C.-S., Ma, G. & Pincebourde, S. Survive a warming climate: insect responses to extreme high temperatures. Annu. Rev. Entomol. 66, 163\u2013184 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

San-Miguel-Ayanz, J. et al. Comprehensive monitoring of wildfires in Europe: the European Forest Fire Information System (EFFIS). In Approaches to Managing Disaster \u2013 Assessing Hazards, Emergencies and Disaster Impacts (ed. Tiefenbacher, J. P.) (InTech, 2012).<\/p>\n

Geng, G. et al. Tracking air pollution in China: near real-time PM2. 5 retrievals from multisource data fusion. Environ. Sci. Technol. 55, 12106\u201312115 (2021).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Becker, E. J., Kirtman, B. P., L\u2019Heureux, M., Mu\u00f1oz, \u00c1G. & Pegion, K. A Decade of the North American Multimodel Ensemble (NMME): research, application, and future directions. Bull. Am. Meteorol. Soc. 103, E973\u2013E995 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tompkins, A. M. et al. The climate-system historical forecast project: providing open access to seasonal forecast ensembles from centers around the globe. Bull. Am. Meteorol. Soc. 98, 2293\u20132301 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vitart, F. et al. The WWRP\/WCRP S2S project and its achievements. Bull. Am. Meteorol. Soc. 106, E791\u2013E808 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Krishnamurti, T. N. et al. Multimodel ensemble forecasts for weather and seasonal climate. J. Clim. 13, 4196\u20134216 (2000).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Antonelli, A., Dhanjal-Adams, K. L. & Silvestro, D. Integrating machine learning, remote sensing and citizen science to create an early warning system for biodiversity. Plants People Planet https:\/\/doi.org\/10.1002\/ppp3.10337<\/a> (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pozo-Montuy, G. et al. Howler monkey die-off in southern Mexico. Am. J. Primatol. 86, e23684 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496\u2013501 (2020).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sober\u00f3n, J. & Nakamura, M. Niches and distributional areas: concepts, methods, and assumptions. Proc. Natl Acad. Sci. USA 106, 19644\u201319650 (2009).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Peterson, A. T. et al. Ecological Niches and Geographic Distributions (MPB-49), Vol. 49 (Princeton Univ. Press, 2011).<\/p>\n

Franklin, J. Mapping Species Distributions: Spatial Inference and Prediction (Cambridge Univ. Press, 2010).<\/p>\n

Le\u00f3n, G. M.-D. & Thakur, M. P. Ecological debts induced by heat extremes. Trends Ecol. Evol. 39, 1024\u20131034 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Briscoe, N. J. et al. Mechanistic forecasts of species responses to climate change: the promise of biophysical ecology. Glob. Change Biol. 29, 1451\u20131470 (2023).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zurell, D. et al. Benchmarking novel approaches for modelling species range dynamics. Glob. Change Biol. 22, 2651\u20132664 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kemppinen, J. et al. Microclimate, an important part of ecology and biogeography. Glob. Ecol. Biogeogr. 33, e13834 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475\u2013482 (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Urban, M. C. Climate change extinctions. Science 386, 1123\u20131128 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Gonzalez, A. et al. A global biodiversity observing system to unite monitoring and guide action. Nat. Ecol. Evol. 7, 1947\u20131952 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Borovikov, A. et al. GEOS-5 seasonal forecast system. Clim. Dyn. 53, 7335\u20137361 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kirtman, B. P. et al. The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bull. Am. Meteorol. Soc. 95, 585\u2013601 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lin, H., Muncaster, R., Derome, J., Merryfield, W. J. & Diro, G. Skillful long-lead seasonal predictions in the summertime Northern Hemisphere midlatitudes. J. Clim. 37, 4915\u20134929 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic climate projections. Philos. Trans. R. Soc. A 365, 2053\u20132075 (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Becker, E. & Dool, H. van den. Probabilistic seasonal forecasts in the North American multimodel ensemble: a baseline skill assessment. J. Clim. 29, 3015\u20133026 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Meehl, G. A. et al. Initialized Earth System prediction from subseasonal to decadal timescales. Nat. Rev. Earth Environ. 2, 340\u2013357 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Infanti, J. M. & Kirtman, B. P. North American rainfall and temperature prediction response to the diversity of ENSO. Clim. Dyn. 46, 3007\u20133023 (2016).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. Atmos. 108, 4407 (2003).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hackert, E. et al. Satellite sea surface salinity observations impact on El Ni\u00f1o\/Southern Oscillation predictions: case studies from the NASA GEOS seasonal forecast system. J. Geophys. Res. Oceans 125, e2019JC015788 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Vitart, F. Madden\u2014Julian Oscillation prediction and teleconnections in the S2S database. Q. J. R. Meteorolog. Soc. 143, 2210\u20132220 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jim\u00e9nez-Esteve, B. & Domeisen, D. I. V. Nonlinearity in the North Pacific atmospheric response to a linear ENSO forcing. Geophys. Res. Lett. 46, 2271\u20132281 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Weigel, A. P., Liniger, M. A. & Appenzeller, C. The discrete Brier and ranked probability skill scores. Mon. Weather Rev. 135, 118\u2013124 (2007).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Weigel, A. P., Baggenstos, D., Liniger, M. A., Vitart, F. & Appenzeller, C. Probabilistic verification of monthly temperature forecasts. Mon. Weather Rev. 136, 5162\u20135182 (2008).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pigot, A. L., Merow, C., Wilson, A. & Trisos, C. H. Abrupt expansion of climate change risks for species globally. Nat. Ecol. Evol. 7, 1060\u20131071 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

BirdLife International and Handbook of the Birds of the World. Bird species distribution maps of the world. Version 2023.1. BirdLife International http:\/\/datazone.birdlife.org\/species\/requestdis<\/a> (2023).<\/p>\n

Hijmans, R. J. Terra: spatial data analysis. R package version 1.7 (2024).<\/p>\n

Lenhard, A., Lenhard, W. & Gary, S. cNORM: continuous norming. R package version 3.5.3 (2018).<\/p>\n

Merow, C. et al. Unprecedented heat threatened 1 in 10 vertebrate species in 2023. Preprint at https:\/\/doi.org\/10.21203\/rs.3.rs-4914374\/v1<\/a> (2025).<\/p>\n

Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorolog. Soc. 146, 1999\u20132049 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hamner, B. & Frasco, M. Metrics: evaluation metrics for machine learning. R package version 0.1.4 (2018).<\/p>\n

Hwang, C.-L., Yoon, K., Hwang, C.-L. & Yoon, K. in Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey 58\u2013191 (Springer, 1981).<\/p>\n

Opricovic, S. & Tzeng, G.-H. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 156, 445\u2013455 (2004).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Brauers, W. K. M. & Zavadskas, E. K. Project management by MULTIMOORA as an instrument for transition economies. Technol. Econ. Dev. Econ. 16, 5\u201324 (2010).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zavadskas, E. K., Turskis, Z., Antucheviciene, J. & Zakarevicius, A. Optimization of weighted aggregated sum product assessment. Elektron. Elektrotechn. 122, 3\u20136 (2012).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Martin, B. A. C. MCDM: multi-criteria decision making methods for crisp data. R package version 1.2 (2016).<\/p>\n

Lawler, J. J. et al. The theory behind, and the challenges of, conserving nature\u2019s stage in a time of rapid change: conserving nature\u2019s stage in a time of rapid change. Conserv. Biol. 29, 618\u2013629 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zou, Z.-H., Yi, Y. & Sun, J.-N. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. J. Environ. Sci. 18, 1020\u20131023 (2006).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhu, Y., Tian, D. & Yan, F. Effectiveness of entropy weight method in decision-making. Math. Probl. Eng. 2020, 3564835 (2020).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

pepbioalerts. pepbioalerts\/s2sExposure_ms: ncc (v1.0.0). Zenodo https:\/\/doi.org\/10.5281\/zenodo.19253737<\/a> (2026).<\/p>\n

India heat wave hits wildlife as thirsty monkeys drown in well. Phys.org https:\/\/phys.org\/news\/2024-06-india-wildlife-thirsty-monkeys.html<\/a> (4 June 2024).<\/p>\n

Heat wave claims 3 lives in Jharkhand, Daltonganj hottest city at 47.4 degree Celsius. The Avenue Mail https:\/\/avenuemail.in\/heat-wave-claims-3-lives-in-jharkhand-daltonganj-hottest-city-in-jharkhand-at-47-4-degree-celsius<\/a> (30 May 2024).<\/p>\n

Dogra, B. & Yadav, R. Impact of heat wave on birds and animals. Countercurrents https:\/\/countercurrents.org\/2024\/06\/impact-of-heat-wave-on-birds-and-animals<\/a> (11 June 2024).<\/p>\n

Euronews Green. Mexico is so hot that monkeys, birds and bats are falling dead from trees. Euronews https:\/\/www.euronews.com\/green\/2024\/05\/28\/mexico-is-so-hot-that-monkeys-birds-and-bats-are-falling-dead-from-trees<\/a> (28 May 2024).<\/p>\n

Bunch, A. Birds drop dead in WA heatwave. The New Daily https:\/\/www.thenewdaily.com.au\/news\/weather\/2024\/01\/12\/birds-drop-dead-wa<\/a> (12 January 2024).<\/p>\n

Bozdar, A. H. Birds and heatwaves. Pakistan Today https:\/\/www.pakistantoday.com.pk\/2024\/06\/09\/birds-and-heatwaves<\/a> (9 June 2024).<\/p>\n","protected":false},"excerpt":{"rendered":"IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Hans-Otto P\u00f6rtner, H.-O. & Roberts, D. C.) (Cambridge Univ.…\n","protected":false},"author":2,"featured_media":493057,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22],"tags":[851,2885,4226,10836,246,4398,1437,61,60,9852,82],"class_list":["post-493056","post","type-post","status-publish","format-standard","has-post-thumbnail","category-environment","tag-biodiversity","tag-climate-change","tag-climate-change-climate-change-impacts","tag-climate-change-ecology","tag-environment","tag-environmental-law-policy-ecojustice","tag-general","tag-ie","tag-ireland","tag-projection-and-prediction","tag-science"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/493056","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=493056"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/493056\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/493057"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=493056"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=493056"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=493056"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}