Abstract
Extreme weather events perturb ecosystems and increasingly threaten biodiversity1. Ecologists emphasize the need to forecast and mitigate the impacts of these events, which requires knowledge of how risk is distributed among species and environments. However, the scale and unpredictability of extreme events complicate risk assessment1,2,3,4—especially for large animals (megafauna), which are ecologically important and disproportionately threatened but are wide-ranging and difficult to monitor5. Traits such as body size, dispersal ability and habitat affiliation are hypothesized to determine the vulnerability of animals to natural hazards1,6,7. Yet it has rarely been possible to test these hypotheses or, more generally, to link the short-term and long-term ecological effects of weather-related disturbance8,9. Here we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to changes in community composition nearly 2 years later. Animals responded behaviourally to rising floodwaters by moving upslope and shifting their diets. Body size and habitat association independently predicted population-level impacts: five of the smallest and most lowland-affiliated herbivore species declined by an average of 28% in the 20 months after landfall, while four of the largest and most upland-affiliated species increased by an average of 26%. We attribute the sensitivity of small-bodied species to their limited mobility and physiological constraints, which restricted their ability to avoid the flood and endure subsequent reductions in the quantity and quality of food. Our results identify general traits that govern animal responses to severe weather, which may help to inform wildlife conservation in a volatile climate.
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Data availability
Data used in this study are available on Dryad: https://doi.org/10.5061/dryad.63tj806; https://doi.org/10.5061/dryad.sxksn02zc and https://doi.org/10.5061/dryad.7wm37pvzv. Source data are provided with this paper.
Code availability
Code used in our analyses is available on Dryad: https://doi.org/10.5061/dryad.7wm37pvzv.
References
IPCC. Climate Change 2022: Impacts, Adaptation and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).
Smith, M. An ecological perspective on extreme climatic events: a synthetic definition and framework to guide future research. J. Ecol. 99, 656–663 (2011).
Ummenhofer, C. C. & Meehl, G. A. Extreme weather and climate events with ecological relevance: a review. Phil. Trans. R. Soc. B 372, 20160135 (2017).
Jentsch, A., Kreyling, J. & Beierkuhnlein, C. A new generation of climate-change experiments: events, not trends. Front. Ecol. Environ. 5, 365–374 (2007).
Pringle, R. M. et al. Impacts of large herbivores on terrestrial ecosystems. Curr. Biol. 33, R584–R610 (2023).
Spiller, D. A., Losos, J. B. & Schoener, T. W. Impact of a catastrophic hurricane on island populations. Science 281, 695–697 (1998).
Schoener, T. W. & Spiller, D. A. Nonsynchronous recovery of community characteristics in island spiders after a catastrophic hurricane. Proc. Natl Acad. Sci. USA 103, 2220–2225 (2006).
Pruitt, J. N., Little, A. G., Majumdar, S. J., Schoener, T. W. & Fisher, D. N. Call-to-Action: a global consortium for tropical cyclone ecology. Trends Ecol. Evol. 34, 588–590 (2019).
Lin, T. C., Hogan, J. A. & Chang, C. T. Tropical cyclone ecology: a scale-link perspective. Trends Ecol. Evol. 35, 594–604 (2020).
IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).
Knutson, T. R. et al. in Critical Issues in Climate Change Science (eds Quéré, C. L. et al.) (ScienceBrief, 2021).
Guzman, O. & Jiang, H. Global increase in tropical cyclone rain rate. Nat. Commun. 12, 5344 (2021).
Wang, G., Wu, L., Mei, W. & Xie, S. P. Ocean currents show global intensification of weak tropical cyclones. Nature 611, 496–500 (2022).
Zeng, H., Chambers, J. O., Negrón-Juárez, R. I. & Powell, M. D. Impacts of tropical cyclones on US forest tree mortality and carbon flux from 1851 to 2000. Proc. Natl Acad. Sci. USA 106, 7888–7892 (2009).
Tanner, E. V., Rodriquez-Sanchez, F., Healey, J. R., Holdway, R. J. & Bellingham, P. J. Long-term hurricane damage effects on tropical forest tree growth and mortality. Ecology 95, 2974–2983 (2014).
Wiley, J. W. & Wunderle, J. M. Jr The effects of hurricanes on birds, with special reference to Caribbean islands. Bird Conserv. Int. 3, 319–349 (1993).
Schoener, T. W., Spiller, D. A. & Losos, J. B. Variable ecological effects of hurricanes: the importance of seasonal timing for survival of lizards on Bahamian islands. Proc. Natl Acad. Sci. USA 101, 177–181 (2004).
Grant, P. R. et al. Evolution caused by extreme events. Proc. R. Soc. B 372, 20160146 (2017).
Donihue, C. M. et al. Hurricane-induced selection on the morphology of an island lizard. Nature 560, 88–91 (2018).
Noonan, M. J. et al. Effects of body size on estimation of mammalian area requirements. Conserv. Biol. 34, 1017–1028 (2020).
Bowman, J., Jaeger, J. A. G. & Fahrig, L. Dispersal distance of mammals is proportional to home range size. Ecology 83, 2049–2055 (2002).
Loe, L. E. et al. Behavioral buffering of extreme weather events in a high-Arctic herbivore. Ecosphere 7, e01374 (2016).
Abernathy, H. N. et al. Deer movement and resource selection during Hurricane Irma: implications for extreme climatic events and wildlife. Proc. R. Soc. B 286, 20192230 (2019).
Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).
Millar, J. S. & Hickling, G. J. Fasting endurance and the evolution of mammalian body size. Funct. Ecol. 4, 5–12 (1990).
Warren, M. Why Cyclone Idai is one of the Southern Hemisphere’s most devastating storms. Nature https://doi.org/10.1038/d41586-019-00981-6 (2019).
Charrua, A. B., Padmanaban, R., Cabral, P., Bandeira, S. & Romeiras, M. M. Impacts of the tropical Cyclone Idai in Mozambique: a multi-temporal Landsat satellite imagery analysis. Remot. Sens. 13, 201 (2021).
Stalmans, M. E., Massad, T. J., Peel, M. J. S., Tarnita, C. E. & Pringle, R. M. War-induced collapse and asymmetric recovery of large-mammal populations in Gorongosa National Park, Mozambique. PLoS ONE 14, e0212864 (2019).
Tinley, K. L. Framework of the Gorongosa Ecosystem. DSc thesis, University of Pretoria (1977).
Böhme, B., Steinbruch, F., Gloaguen, R., Heilmeier, H. & Merkel, B. Geomorphology, hydrology, and ecology of Lake Urema, central Mozambique, with a focus on lake extent changes. Phys. Chem. Earth. 31, 745–752 (2006).
Gaynor, K. M., Daskin, J. H., Rich, L. N. & Brashares, J. S. Post-war wildlife recovery in an African savanna: evaluating patterns and drivers of species occupancy and richness. Anim. Conserv. 24, 510–522 (2021).
Potter, A. B. et al. Mechanisms of dietary resource partitioning in large-herbivore assemblages: a plant-trait-based approach. J. Ecol. 110, 817–832 (2022).
Pansu, J. et al. Generality of cryptic dietary niche differentiation in diverse large-herbivore assemblages. Proc. Natl Acad. Sci. USA 119, e2204400119 (2022).
Daskin, J. H. et al. Allometry of behavior and niche differentiation among congeneric African antelopes. Ecol. Monogr. 93, e1549 (2022).
Veldhuis, M. P. et al. Large herbivore assemblages in a changing climate: incorporating water dependence and thermoregulation. Ecol. Lett. 22, 1536–1546 (2019).
Abraham, J. O., Hempson, G. P. & Staver, A. C. Drought-response strategies of savanna herbivores. Ecol. Evol. 9, 7047–7056 (2019).
Becker, J. A. et al. Ecological and behavioral mechanisms of density-dependent habitat expansion in a recovering African ungulate population. Ecol. Monogr. 91, e01476 (2021).
Loveridge, A. J., Hunt, J. E., Murindagomo, F. & Macdonald, D. W. Influence of drought on predation of elephant (Loxodonta africana) calves by lions (Panthera leo) in an African wooded savannah. J. Zool. 270, 523–530 (2006).
Ferreira, S. M. & Viljoen, P. African large carnivore population changes in response to a drought. Afr. J. Wildl. Res. 52, 1 (2022).
Palmer, M. S. et al. Dynamic landscapes of fear: understanding spatiotemporal risk. Trends Ecol. Evol. 37, 911–925 (2022).
Thibault, K. & Brown, J. Impact of an extreme climatic event on community assembly. Proc. Natl Acad. Sci. USA 105, 3410–3415 (2008).
Perino, A. et al. Rewilding complex ecosystems. Science 364, eaav5570 (2019).
Betts, M. G. et al. Extinction filters mediate the global effects of habitat fragmentation on animals. Science 366, 1236–1239 (2019).
Tucker, M. A. et al. Behavioral responses of terrestrial mammals to COVID-19 lockdowns. Science 380, 1059–1064 (2023).
Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).
Meagher, M. Evaluation of boundary control for bison of Yellowstone National Park. Wildl. Soc. Bull. 17, 15–19 (1989).
Laubscher, L. L. et al. Non-chemical techniques used for the capture and relocation of wildlife in South Africa. Afr. J. Wildl. Res. 45, 275–286 (2015).
Walker, B. H., Emslie, R. H., Owen-Smith, N. & Scholes, R. J. To cull or not to cull: lessons from a southern African drought. J. Appl. Ecol. 24, 381–401 (1987).
Milner, J. M., van Beest, F. M., 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, 1322–1334 (2014).
Joint Research Centre. Tropical Cyclone IDAI in Mozambique (2019-03-15). http://data.europa.eu/89h/4f8c752b-3440-4e61-a48d-4d1d9311abfa (European Commission, 2019).
Guyton, J. A. et al. Trophic rewilding revives biotic resistance to shrub invasion. Nat. Ecol. Evol. 4, 712–724 (2020).
Stalmans, M. & Beilfuss, R. Landscapes of the Gorongosa National Park (Gorongosa National Park, 2008).
Pansu, J. et al. Trophic ecology of large herbivores in a reassembling African ecosystem. J. Ecol. 109, 1355–1376 (2019).
Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).
Bouley, P., Poulos, M., Branco, R. & Carter, N. H. Post-war recovery of the African lion in response to large-scale ecosystem restoration. Biol. Conserv. 227, 233–242 (2018).
Bouley, P., Paulo, A., Angela, M., Du Plessis, C. & Marneweck, D. G. The successful reintroduction of African wild dogs (Lycaon pictus) to Gorongosa National Park, Mozambique. PLoS ONE 16, e0249860 (2021).
Cabral, P. et al. Assessing Mozambique’s exposure to coastal climate hazards and erosion. Int. J. Disaster Risk Reduct. 23, 45–52 (2017).
Kolstad, K. W. Predictions and precursors of Idai and 38 other tropical cyclones and storms in the Mozambique Channel. Q. J. R. Meteorol. Soc. 147, 45–57 (2021).
Nhundu, K., Sibada, M. & Chaminuka, P. in Cyclones in Southern Africa (Springer, 2021).
Kingdon, J. The Kingdon Field Guide to African Mammals (Bloomsbury, 1997).
R Core Team, R: A Language and Environment for Statistical Computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2020).
Pebesma, E. Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30, 683–691 (2004).
UNOSAT. Cumulative Satellite Detected Waters Extent Overview Between 13 & 26 March 2019 over Sofala province, Mozambique. https://data.humdata.org/dataset/cumulative-satellite-detected-waters-extent-13-26-march-2019-over-sofala-province-mozambique (UNITAR, 2019).
Western, D., Mose, V. N., Worden, J. & Maltumo, D. Predicting extreme droughts in savannah Africa: a comparison of proxy and direct measures in detecting biomass fluctuations, trends, and their causes. PLoS ONE 10, e0136516 (2015).
Busetto, L. & Ranghetti, L. MODIStsp: an R package for automatic preprocessing of MODIS Land Products time series. Comput. Gosci. 97, 40–48 (2016).
Kilsch, A. & Atzberger, C. Operational drought monitoring in Kenya using MODIS NDVI time series. Remote Sens. 8, 267 (2016).
Mamugy, F. Does Predation or Competition Shape the Home Range and Resources Selection by Sable Antelope (Hippotragus niger) in the Gorongosa National Park, Mozambique? MSc thesis, University of the Witwatersrand (2016).
Arumoogum, N. Spatiotemporal Niche Dynamics of a Reassembling Herbivore Ensemble in Southern Africa. PhD thesis, University of the Witwatersrand (2022).
Branco, P. S. et al. Determinants of elephant foraging behavior in a coupled human–natural system: is brown the new green? J. Anim. Ecol. 88, 780–792 (2019).
Animal Care and Use Committee of the American Society of Mammalogists. 2016 Guidelines of the American Society of Mammalogists for the use of wild animals in research and education. J. Mammal. 97, 633–688 (2016).
Avgar, T., Potts, J. R., Lewis, M. A. & Boyce, M. S. Integrated step selection analysis: bridging the gap between resource selection and animal movement. Methods Ecol. Evol. 7, 619–630 (2016).
Muff, S., Signer, J. & Fieberg, J. Accounting for individual-specific variation in habitat-selection studies: efficient estimation of mixed-effects models using Bayesian or frequentist computation. J. Anim. Ecol. 89, 80–92 (2019).
Tarnita, C. et al. A theoretical foundation for multi-scale regular vegetation patterns. Nature 541, 398–401 (2017).
Fortin, D. et al. Wolves influence elk movements: behavior shapes a trophic cascade in Yellowstone National Park. Ecology 86, 1320–1330 (2005).
Therneau, T. survival: A package for survival analysis in R. R package version 3.5-5. https://cran.r-project.org/web/packages/survival/index.html (2020).
Flemming, C. H. et al. Estimating where and how animals travel: an optimal framework for path reconstruction form autocorrelated tracking data. Ecology 97, 576–582 (2016).
Mueller, T., Olson, K. A., Leimgruber, P. & Calabrese, J. M. Rigorous home-range estimation with movement data: a new autocorrelated kernel-density estimator. Ecology 96, 1182–1188 (2015).
Singer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.3.2.0. https://cran.r-project.org/web/packages/DHARMa/vignettes/DHARMa.html (2022).
Giguet-Covex, C. et al. Long livestock farming history and human landscape shaping revealed by lake sediment DNA. Nat. Comm. 5, 3211 (2014).
Boyer, F. et al. OBITOOLS: a UNIX-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).
Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-6. https://cran.r-project.org/web/packages/vegan/index.html (2020).
Walker, R. H. et al. Mechanisms of individual variation in large herbivore diets: roles of spatial heterogeneity and state-dependent foraging. Ecology 104, e3921 (2023).
Parker, K. L., Barboza, P. S. & Gillingham, M. P. Nutrition integrates environmental responses of ungulates. Funct. Ecol. 23, 57–69 (2009).
Cook, R. C. et al. Revisions of rump fat and body scoring indices for deer, elk, and moose. J. Wildl. Manag. 74, 880–896 (2010).
Stalmans, M. E. & Peel, M. Aerial wildlife count of the Gorongosa National Park, Mozambique, November 2020. Parque Nacional da Gorongosa https://gorongosa.org/wp-content/uploads/2023/10/GorongosaAerialWildlifeCount2020.pdf (2020).
Acknowledgements
We acknowledge the countless lives lost to and affected by Cyclone Idai. We thank the Republic of Mozambique and Gorongosa National Park for permission to conduct this study. We thank L. Van Wyk, M. Pingo, R. Branco and all park staff for logistical support. G. Vecchi provided comments on our summary of global tropical cyclone trends. We acknowledge support from the following: US National Science Foundation grants IOS-1656527 (R.M.P.), DEB-2225088 (R.M.P.), IOS-1656642 (R.A.L.), and PRFB-1810586 (M.S.P.); National Research Foundation of South Africa grant 116304 (F.P.); the Greg Carr and Cameron Schrier Foundations (R.M.P.); HHMI BioInteractive (K.M.G. and M.S.P.); the Yale Institute for Biospheric Studies (J.H.D.); the Grand Challenges Program of the High Meadows Environmental Institute at Princeton University (R.M.P.); and the National Geographic Society 000039685 (M.C.H.).
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R.H.W., J.A.B., M.C.H., J.H.D., M.E.S., R.M.P. and R.A.L. conceived and designed the study. R.H.W., J.A.B., M.C.H., J.H.D., K.M.G., M.S.P. and R.M.P. performed statistical analyses. J.H.D. and J.D. provided flood depth data. R.H.W., J.A.B., R.M.P., and R.A.L. provided antelope movement data. D.D.G. and R.A.L. provided elephant movement data. N.A., F.P. and J.P.M. provided sable movement data. A.B.P., M.C.H., and R.M.P. provided diet data. K.M.G. and M.S.P. provided camera-trap data. M.A., A.P. and P.B. provided carnivore data. M.E.S. provided aerial survey data. R.H.W. created all data visualizations. R.M.P. and R.A.L. supervised the research. R.H.W., M.C.H., R.M.P. and R.A.L. wrote the manuscript. All authors provided comments and edits.
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Extended data figures and tables
Extended Data Fig. 1 Herbivore body size and habitat affiliation in Gorongosa.
(a) The 13 herbivore species in this study spanned a broad spectrum of body sizes and habitat affiliations. Body mass estimates used in this study are the average of sex-specific adult values from ref. 60. Floodplain affiliation is the mean proportion of individuals of each species occurring in floodplain-grassland habitat (b,c) during aerial wildlife counts (2014–2018). Body mass and floodplain affiliation were uncorrelated (r = −0.34, df = 11, P = 0.26). (b) Map of study area, showing habitat types and geographic features. c–e, Representative photos of floodplain (c, 8–20 m above sea level); floodplain-savanna transition (d, 20–25 m above sea level); and savanna woodland (e, >25 m above sea level).
Extended Data Fig. 2 Rainfall from Cyclone Idai caused extreme and unseasonal flooding.
(a) Bold yellow line shows monthly rainfall in the year of Cyclone Idai (2019); thin purple lines show monthly rainfall in other years (2011–2018, 2020); bold purple line shows mean monthly rainfall across those years. March of 2019 was roughly fivefold wetter (688 mm) than a typical March (130 mm), and 2019 had roughly twice as much rain (1874 mm) as the long-term annual average (850 mm). (b) Floodwaters extended farther (visualized in relation to the road network, red lines) and persisted longer (color scale) in 2019 (top maps) than in 2020 (also a wetter than average year, 1037 mm; bottom maps). Stars mark the location of Lion House, a local landmark. (c) Lion House before Idai (early March 2019) when the seasonal flood waters had largely receded in the surrounding floodplain. This photo was taken via drone by the Gorongosa Restoration Project. (d) Lion House was submerged by flood waters following Idai, with only the roof remaining above water (photo date: 22 March 2019). This picture was taken from a helicopter during post-Idai humanitarian aid efforts by the Gorongosa Restoration Project. Over two days (15–17 March 2019; e-h), flooding induced by Cyclone Idai rose by more than 3 m adjacent to Lion House and persisted for 2 months (i,j). Photos courtesy of Piotr Naskrecki.
Extended Data Fig. 3 Body sizes and movement behaviors of bushbuck that died (n = 3) and survived (n = 5) Cyclone Idai.
a–b, The three bushbuck that died in the flood were smaller than the survivors (a) and included the two smallest of five adult females and the single smallest of three adult males (b). Boxplots show median and interquartile range; whiskers show minimum and maximum. (c) Coefficients ± 95% confidence intervals (CIs) from step-selection functions (SSFs) that quantified selection for elevation, termite mounds, and distance to floodwaters in the week before (purple) and after (yellow) Idai. Positive coefficients indicate selection and negative coefficients indicate avoidance; CIs not overlapping zero indicate significant selection or avoidance; CIs not overlapping each other indicate significant differences before vs. after landfall. Although bushbuck killed by Cyclone Idai significantly increased their selection for higher elevations and mounds (non-overlapping CIs for all coefficients before vs. after landfall), they were unable to avoid the flood edge (indicated by positive coefficients) and died in water >1.5-m deep.
Extended Data Fig. 4 Herbivore traits predicted degree of displacement after Idai.
(a) This analysis parallels and complements Fig. 2b, which shows displacement from ranges based on overlap of utilization distributions via 95% autocorrelated kernel density estimation. Thin lines show individual movements; thick lines show the mean across individuals (purple, pre-cyclone; yellow, post-cyclone). Within a week of landfall, bushbuck (2019: n = 8) moved many-fold farther from their prior week-long range centroids than expected based on pre-cyclone behavior (Welch’s two sample t-test: \({\bar{X}}_{{cyclone}}=0.95\), \({\bar{X}}_{{no\; cyclone}}=0.08\), t = 3.01, P = 0.02); this effect intensified over the next week (\({\bar{X}}_{{cyclone}}=2.67\), \({\bar{X}}_{{no\; cyclone}}=0.08\), t = 5.62, P < 0.001) and then persisted over the next month. Some individuals of other herbivore species exhibited similarly anomalous displacement from their prior week-long range centroids after the cyclone (compare thin lines), but these trends were not pronounced at the population level (all P > 0.05). (b) Affiliation with low-elevation floodplain habitat (quantified at the species level; see Extended Data Fig. 1) strongly predicted the magnitude of individuals’ displacement in the week after Idai (i.e., lower overlap with prior home ranges, estimated as utilization distributions via autocorrelated kernel density estimation; mixed-effects model with beta error distribution, fixed effects of floodplain affiliation and log-transformed body mass, and per-species random intercepts: βfloodplain = −5.44 ± 1.65 s.e., P = 0.001). Small size (measured or estimated at the individual level for all species except elephant; see Methods) was also associated with greater displacement after accounting for effects of habitat affiliation (βlog(mass) = 0.53 ± 0.25 s.e., P = 0.03). Model-predicted effects (black line with shaded 95% CI) show strength and direction of each relationship.
Extended Data Fig. 5 Herbivore diet composition differed after Cyclone Idai (2019) relative to non-cyclone years (2018, 2016).
a–c, Nonmetric multidimensional scaling (NMDS) ordinations of Bray-Curtis dietary dissimilarity based on fecal DNA metabarcoding for each species in each season. Each point corresponds to an individual fecal sample, and distance between points reflects degree of dissimilarity; ellipses show 95% CI derived from the multivariate t-distribution and represent diet composition and breadth for each species in the late-wet season (a), early-dry season (b), and late-dry season (c). Sample sizes are in Supplementary Table 1. 2016 data were available only for the early-dry season (b), and bushbuck and hartebeest are omitted from the wet season plots (a) owing to insufficient sample sizes. P-values above each ordination plot are from pairwise permutational analyses of variance (perMANOVA) between cyclone and non-cyclone periods for each species in each season (for the early-dry season, 2016 and 2018 data are plotted separately but lumped as one ‘non-cyclone’ group for perMANOVA). The perMANOVA for buffalo in the early-dry season failed to converge; all but 5 of the remaining 36 tests indicated statistically significant (P ≤ 0.05) dietary differences between cyclone and non-cyclone periods; the only exceptions were kudu in the late-wet and early-dry seasons, waterbuck in the early- and late-dry seasons, and reedbuck in the late-dry season.
Extended Data Fig. 6 Herbivore diet metrics after Idai (2019) relative to non-cyclone years (2016, 2018).
Points and error bars show mean ± s.e.m; sample sizes are in Supplementary Table 1. Asterisks show statistical significance level, as per legend key at the bottom. (a) Relative read abundance (RRA) of grasses in the diet of each species (from left to right in decreasing order of grass consumption). Grass RRA was significantly lower in all seasons after Cyclone Idai than in 2018 (a wetter than average year), but not relative to the 2016 early-dry season (a drier than average year) (beta generalized linear mixed-effects models, with fixed effect of year and per-species random intercepts: wet, β2018 = 0.73 ± 0.12 s.e., P < 0.001; early-dry, β2016 = 0.12 ± 0.21, P = 0.56 and β2018 = 0.57 ± 0.23, P = 0.01; late-dry, β2018 = 0.46 ± 0.19, P = 0.02). (b) Family-level dietary richness was greater in all seasons after Idai than in previous years (Poisson mixed-effects model, per-species random intercepts: late-wet, β2018 = −0.29 ± 0.04 s.e., P < 0.001; early-dry, β2016 = −0.09 ± 0.04, P = 0.02 and β2018 = −0.06 ± 0.04, P = 0.09; late-dry, β2018 = −0.17 ± 0.04, P < 0.001). c–e, We fit separate linear mixed-effects models with per-species random intercepts for measures of diet quality in each season. (c) Digestibility was non-significantly lower after Idai in the late-wet (β2018 = 0.01 ± 0.02, P = 0.49) and early-dry seasons (β2016 = 0.03 ± 0.02 s.e., P = 0.14 and β2018 = 0.03 ± 0.02 s.e., P = 0.15); although digestibility was higher on average in the late-dry season (β2018 = −0.05, SE = 0.02, P = 0.05), this trend conceals strong interactions between the cyclone and herbivore traits, with small-bodied and floodplain-affiliated species having less digestible diets after Idai than in 2018 (see Extended Data Table 2). (d) Lignin content was elevated throughout the year after Idai (late-wet, β2018 = −0.10 ± 0.04, P = 0.01; early-dry, β2016 = −0.05 ± 0.03, P = 0.15 and β2018 = −0.12 ± 0.03, P < 0.001; late-dry, β2018 = −0.14 ± 0.04, P < 0.001). (e) Phosphorus content was lower after Idai in all seasons (late-wet, β2018 = 0.07 ± 0.03, P = 0.005; early-dry, β2016 = 0.04 ± 0.03, P = 0.12 and β2018 = 0.16 ± 0.03, P < 0.001; late-dry, β2018 = 0.07 ± 0.03, P = 0.02). (f) Sodium content was lower after Idai in the late-wet (β2018 = 0.25 ± 0.06, P < 0.001) and early-dry seasons (β2016 = 0.21 ± 0.06, P = 0.001 and β2018 = 0.34 ± 0.06, P < 0.001) but rebounded by the late-dry season (β2018 = −0.11 ± 0.07, P = 0.10).
Extended Data Fig. 7 Large-mammal abundance in the years before and after Idai.
Data are from biennial helicopter-based wildlife surveys, except for wild dog for which we used monitoring data from Gorongosa’s Conservation Program, and represent systematic total counts covering a standardized 193,500-ha area in the core of the park during the late dry season, when canopy cover is lowest. Even the most meticulous counts do not detect all individuals, so data should be interpreted as minimum numbers known alive, but we are otherwise confident in the accuracy of data for these herbivore populations. Lions are particularly difficult to count from the air, and these data substantially underestimate total abundance inferred from ground-based monitoring55, but we consider them a qualitatively reliable index of relative abundance across years.
Extended Data Fig. 8 Carnivore behavior before and after Cyclone Idai.
(a) Coefficients ± 95% CIs from step-selection functions (SSFs) fit to GPS telemetry data from African wild dog (the only pack present at the time of this study) and lion, showing selection for elevation, termite mounds, and distance to flood waters in the two weeks before (purple) and after (yellow) Idai (cf. Fig. 2a). (b) Both species exhibited moderate displacement from their ranges, moving away from Lake Urema in the weeks after landfall (yellow lines compare the week prior to 15 Mar. 2019 to weekly bins thereafter; thin, individuals; bold, population) relative to periods immediately before the cyclone (purple lines compare the week prior to 1 Feb. 2019 to weekly bins thereafter; cf. Fig. 2b). c–e, Whereas no shift in lion diet was detected, the proportion of waterbuck among confirmed wild dog kills increased after the cyclone (c; sample sizes for each time period at top). This shift was associated with a greater overall difference in wild dog diet composition before versus immediately after the cyclone as quantified using DNA metabarcoding and visualized here by nonmetric multidimensional scaling (NMDS) ordination of Bray Curtis dissimilarity values (d; markers correspond to individual fecal samples). Relative read abundance (RRA) of prey DNA in wild dog scats independently the general pattern observed in the kill data (e; means ± s.e.m.).
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Supplementary Information
Additional description of DNA metabarcoding methods. Also includes Supplementary Tables 1–4, sample sizes for herbivore diet analyses, model selection table used to identify the best predictors of changes in herbivore abundance, and results from principle component analysis used to develop a nutritional condition index.
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Walker, R.H., Hutchinson, M.C., Becker, J.A. et al. Trait-based sensitivity of large mammals to a catastrophic tropical cyclone. Nature 623, 757–764 (2023). https://doi.org/10.1038/s41586-023-06722-0
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DOI: https://doi.org/10.1038/s41586-023-06722-0
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