Merow, C

Pigot, AL

Trisos, CH

English

UCL Discovery

Christopher H. Trisos1,2,3, Cory Merow4 & Alex L. Pigot5 1  2 1African Climate and Development Initiative, University of Cape Town, Cape Town, 3 South Africa.  4 2National Socio-Environmental Synthesis Center (SESYNC), Annapolis, MD, USA.  5 3Centre for Statistics in Ecology, the Environment, and Conservation, University of Cape 6 Town, Cape Town, South Africa.  7 4Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA. 8 5Centre for Biodiversity and Environment Research, Department of Genetics, Evolution 9 and Environment, University College London, London, UK. 10  11 e-mail: a.pigot@ucl.ac.uk 12  13 Understanding how climate risks to biodiversity will change over the coming decades is 14 a major challenge and we therefore welcome Colwell’s critique. We agree that the 15 mechanisms discussed by Colwell—evolution, range shifts, and localized climate 16 refugia—may enhance species persistence under climate warming, and that these 17 mechanisms will be more likely to operate within larger spatial grains and mountainous 18 regions, which is why we discussed each in our paper. However, Colwell does not 19 provide any quantitative evidence to support his claim that our analysis overestimates 20 the risk of abrupt climate exposure and presents a highly selective set of factors that are 21 unlikely to have directionally biased our results. Here we explain why our conclusions 22 are robust to the oversimplified subset of mechanisms discussed by Colwell and 23 highlight why we believe the species exposure models (SEM) we introduced are an 24 important step forward in ecological forecasting.  25  26 Abrupt exposure is a general pattern and not an artefact of homogenizing 27 mountain regions. 28 Colwell asserts that risks of abrupt climate exposure are overestimated in our analysis 29 because some 100km grid cells contain substantial spatial climatic heterogeneity, 30 particularly in mountainous regions. However, while mountains do undoubtedly 31 provide more opportunities for local climate refugia, as we already demonstrated in our 32 paper (see Extended Figure 101), it is the relatively flat regions with little spatial 33 climatic heterogeneity, such as the Amazon Basin, that are projected to experience the 34 most abrupt exposure. Of the cells on land that under RCP8.5 are projected to be at risk 35 of abrupt ecological disruption by 2100, only 17% of these span ≥1000m elevation 36 (Figure 1). Thus, our conclusion of abrupt exposure is not driven by the topographically 37 diverse regions that Colwell suggests will be safe havens for biodiversity, but is instead 38 a general pattern across assemblages and is especially strong in those areas where finer 39 scale climate heterogeneity is relatively small. 40  41 Species persistence at large spatial grains does not imply lack of ecological 42 disruption.  43 Colwell suggests that the risk of ecological disruption is overestimated in our analysis 44 because species may be able to persist within 100km grid cells by shifting their 45 distribution to local refugia within the grid cell, either up mountain slopes or, in the 46 oceans, to greater depths. The possibility that species may persist despite exposure to 47 climate conditions beyond their historical limits is a point with which we entirely agree 48 and discussed in our paper. However, Colwell’s interpretation that because species may 49 persist, risks of ecological disruption are overestimated, misses the crucial point. Even if 50 species are able to persist by retreating up mountains or to greater depths, the 51 population contractions associated with these responses would still portend potentially 52 major disruption to the ecological systems these species leave behind3. For instance, 53 few coral species on the Great Barrier Reef may yet have been driven extinct at the scale 54 of 100km grid cells, but this is clearly an unsuitable benchmark for assessing the 55 massive ecological devastation caused by back-to-back mass bleaching and mortality of 56 corals already impacting this and other regions as a result of thermal exposure4. Thus, 57 just as exposure should not be conflated with extinction, the chance that a species may 58 persist somewhere should not be conflated with a low risk of ecological disruption. 59  60 Abrupt exposure implies an elevated risk of ecological disruption regardless of 61 spatial grain.  62 Colwell suggests that we should not define assemblages as the set of species that occur 63 in a 100km grid cell, because these species may not interact at finer resolutions. This 64 critique could be levelled at any spatial grain, and there is no single ideal grain size for 65 describing a spatially diffuse assemblage of species5. While choosing a finer grain could 66 better characterize local climate, it would lead to many false presences for each species, 67 which could also lead to biased niche estimates6. More fundamentally, the decline or 68 loss of a species will cause ecological disruption wherever it occurs in geographic space 69 and our projections do not assume that species interact. Had we considered such 70 interactions while studying exposure at finer grains we would likely project a greater 71 risk of ecological disruption due to collapsing interdependencies among species. As with 72 perhaps every pattern in biogeography, a critical study of scale dependence is 73 warranted. A major challenge for future work is being able to model global or regional 74 patterns of species exposure to future climates at both fine spatial (e.g., 1km) and 75 temporal scales (e.g., monthly), as opposed to fine-scale modelling on only one of these 76 dimensions7. 77  78 Uncertainty in estimates of exposure and ecological disruption cut both ways. 79 While Colwell discusses mechanisms that make our risk projections pessimistic, he 80 ignores other factors that could lead to exposure being underestimated. First, we 81 defined exposure as the time when the mean annual or maximum mean monthly 82 temperature consistently exceeds the realised historical limits of a species for a run of at 83 least five consecutive years. However, species may be at risk from much briefer periods 84 of exposure—such as a single extreme year, month or even day8—leading to more 85 immediate risks of ecological disruption than we projected910. Second, our range-wide 86 estimates did not account for the possibility that populations may be locally adapted11 87 or that species niches are determined by dependencies between multiple climate 88 variables12, both of which would increase the risk of exposure. Third, species may be 89 sensitive to climate-driven disruption at temperatures below their realised thermal 90 limits because they are impacted by the temperature-driven loss of essential habitat, 91 such as sea ice fragmentation for polar bears13 or mass mortality of habitat forming 92 corals for marine animals4, as well as by altered biotic interactions141516—all factors not 93 considered in our models.  94 For these reasons some may disagree with Colwell and think that our study has 95 underestimated climate risks to biodiversity. Indeed, while our projections show that 96 climate risks are likely to rapidly escalate over the coming decades, increases in sea 97 surface temperatures over the last half century have already caused wide-spread die-98 offs of fish17, seagrass18, macro-algae19 and coral species20, with these events often 99 occurring abruptly and impacting multiple species, as in the case of mass coral 100 bleaching4. Similarly, on land, climate driven population declines and local extinctions of 101 both ectothermic and endothermic species are already underway21–24. 102  103 A research agenda for understanding dynamic climate risks to biodiversity.  104 We think that a primary value of the SEM framework will be to provide a conceptual 105 and methodological foundation for addressing how various mechanisms balance out to 106 either amplify or temper the risk of abrupt climate exposure and ecological disruption 107 thereby advancing understanding of how climate risks to biodiversity will unfold over 108 time. From a conceptual perspective, we emphasized in our paper that we project the 109 risk of exposure to conditions beyond the known realised limits of a species, not the 110 outcome of exposure (which may include evolution, dispersal, and local extinction). This 111 distinction is important as it helps separate the sources of uncertainty inherent in 112 biodiversity projections: (i) uncertainty in estimates of the timing of exposure due to 113 limitations in species occurrence or climate data and (ii) uncertainty in the ecological 114 consequences of exposure. From a methodological perspective, SEMs—which are based 115 on fine temporal resolution climate data at monthly or annual scales rather than the 116 mean conditions for a remote period of decades in the future—can help resolve these 117 uncertainties. For example, estimating the future timing of exposure of local 118 populations to unprecedented conditions can help understand the potential for 119 evolutionary rescue from changing climates. Identifying those species and regions at 120 immediate risk of exposure provides both a pragmatic early warning system for climate 121 risks to biodiversity and the opportunity to continuously update and refine projections 122 as climate change unfolds and ecological responses are observed. 123 Our global analysis of exposure dynamics across terrestrial and marine systems 124 remains a starting point. Much work is now needed to improve and refine estimates of 125 the timing of exposure and to understand its ecological consequences. But, as was the 126 case with ecological responses to environmental upheavals in the past25, our analysis 127 suggests that in many places future changes in biodiversity due to anthropogenic 128 climate warming are unlikely to be gradual and we should be prepared for that. 129  130 References 131 1. Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological 132 disruption from climate change. Nature vol. 580 496–501 (2020). 133 2. NOAA National Geophysical Data Center. 2009: ETOPO1 1 Arc-Minute Global Relief 134 Model. NOAA National Centers for Environmental Information. Accessed 10.05.2021 135 3. Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. 136 Nat Ecol Evol 4, 109–114 (2020). 137 4. Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–138 496 (2018). 139 5. Ricklefs, R. E. Disintegration of the Ecological Community. The American Naturalist vol. 140 172 741–750 (2008). 141 6. Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range 142 maps in ecology and conservation. Proc. Natl. Acad. Sci. U. S. A. 104, 13384–13389 143 (2007). 144 7. Nadeau, C. P., Urban, M. C. & Bridle, J. R. Coarse climate change projections for species 145 living in a fine-scaled world. Global Change Biology vol. 23 12–24 (2017). 146 8. Stewart, S. B. et al. Climate extreme variables generated using monthly time-series data 147 improve predicted distributions of plant species. Ecography vol. 44 626–639 (2021). 148 9. Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and 14  extreme events. Nature Climate Change vol. 8 579–587 (2018). 150 10. McKechnie, A. E. & Wolf, B. O. The Physiology of Heat Tolerance in Small Endotherms. 15  Physiology 34, 302–313 (2019). 152 11. Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts 153 of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014). 154 12. Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural 155 variability in a warming climate. Nature Communications vol. 9 (2018). 156 13. Molnár, P. K., Derocher, A. E., Thiemann, G. W. & Lewis, M. A. Predicting survival, 157 reproduction and abundance of polar bears under climate change. Biological 158 Conservation vol. 143 1612–1622 (2010). 159 14. Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a 160 rainforest food web. Proc. Natl. Acad. Sci. U. S. A. 115, E10397–E10406 (2018). 161 15. Vergés, A. et al. Long-term empirical evidence of ocean warming leading to 162 tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl. 163 Acad. Sci. U. S. A. 113, 13791–13796 (2016). 164 16. Genin, A., Levy, L., Sharon, G., Raitsos, D. E. & Diamant, A. Rapid onsets of warming 165 events trigger mass mortality of coral reef fish. Proc. Natl. Acad. Sci. U. S. A. 117, 25378–166 25385 (2020). 167 17. Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nature Climate 168 Change vol. 3 919–925 (2013). 169 18. Ruthrof, K. X. et al. Subcontinental heat wave triggers terrestrial and marine, multi-taxa 170 responses. Sci. Rep. 8, 13094 (2018). 171 19. Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. 172 Science 353, 169–172 (2016). 173 20. Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the 174 Anthropocene. Science 359, 80–83 (2018). 175 21. Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with 176 population decline among terrestrial birds and mammals globally. Glob. Chang. Biol. 24, 177 4521–4531 (2018). 178 22. Wiens, J. J. Climate-Related Local Extinctions Are Already Widespread among Plant and 179 Animal Species. PLoS Biol. 14, e2001104 (2016). 180 23. Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal 181 niches. Science 328, 894–899 (2010). 182 24. Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines 183 among bumble bees across continents. Science 367, 685–688 (2020). 184 25.  Williams, J.W., Ordonez, A. & Svenning, JC. A unifying framework for studying and 185 managing climate-driven rates of ecological change. Nat Ecol Evol 5, 17–26 (2021) 186  187 188 Figure 1 Abrupt exposure is a general pattern and not an artefact of 189 homogenizing mountain regions. Of those assemblages (100km grid cells, n = 6105) 190 on land projected to be at risk of abrupt ecological disruption this century (≥20% of all 191 species in an assemblages exposed in a single decade (see Figure 41)) most encompass a 192 relatively narrow range of elevations (metres (m), calculated at 1 arc-minute 193 resolution2) and thus have relatively small spatial climatic heterogeneity. Risk is 194 calculated based on 22 General Circulation and Earth System Models developed for the 195 Coupled Model Intercomparison Project 5 (CMIP5) under RCP 8.5, a high emissions 196 scenario. 197  198 Contributions 199 A.L.P., C.H.T. and C.M. contributed to writing the manuscript.  200  201 Data availability 202 Elevation data is publicly available from https://www.ngdc.noaa.gov/mgg/global/. 203 Code and data to reproduce Figure 1 is available at Figshare 204 (https://doi.org/10.6084/m9.figshare.14730501). 205  206 Competing interests 207 The authors declare no competing interests. 208 0 1000 2000 3000 4000 50000100200300400500600700Range in elevation (m)Number of assemblages at riskof abrupt ecological disruption

Spatial scale and the synchrony of ecological disruption

https://discovery.ucl.ac.uk/id/eprint/10141429/1/376571_2_merged_1623065219.pdf

Spatial scale and the synchrony of ecological disruption

Abstract

Similar works

Full text

Available Versions

UCL Discovery