Philippa Kaur

Welch and colleagues analysed 3.7 billion AIS messages recorded between 2017 and 2019 in the global Fishing Watch AIS dataset, identifying more than 55,000 suspected intentional disabling events in waters more than 50 nautical miles from shore, amounting to 6% ( >4.9 million hours) of obscured vessel activity. Hotspots of disabling activity were located near several regions of IUU concern and transshipment hotspots, including in the exclusive economic zones of Argentina and West African nations and in the Northwest Pacific. Using individual boosted regression tree models for the four dominant gear types (squid jiggers, trawlers, tuna purse seines and drifting longlines) and a full model that included all suspected disabling events (that is, the four gear types listed above and additional gears such as gillnet and troll), Welch and colleagues found that loitering by transshipment vessels (a proxy for potential transshipment events) was the most important driver in the full model and squid jigger model and more than half of the disabling events by squid jiggers were close enough to undertake transshipment to refrigerated cargo vessels. More

Cézilly, F. & Perrot-Minnot, M. J. Interpreting multidimensionality in parasite-induced phenotypic alterations: Panselectionism versus parsimony. Oikos 119, 1224–1229 (2010).Article 

Google Scholar 
Moore, J. Parasites and the Behavior of Animals. (Oxford University Press on Demand, 2002).
Google Scholar 
Thomas, F. et al. Do hairworms (Nematomorpha) manipulate the water seeking behaviour of their terrestrial hosts?. J. Evol. Biol. 15, 356–361 (2002).Article 

Google Scholar 
Weinersmith, K. L. What’s gotten into you? A review of recent research on parasitoid manipulation of host behavior. Curr. Opin. Insect Sci. 33, 37–42 (2019).Article 

Google Scholar 
Hughes, D. P. et al. Behavioral mechanisms and morphological symptoms of zombie ants dying from fungal infection. BMC. Ecol. 11, (2011).Lagrue, C., Kaldonski, N., Perrot-Minnot, M. J., Motreuil, S. & Bollache, L. Modification of hosts’ behavior by a parasite: Field evidence for adaptive manipulation. Ecology 88, 2839–2847 (2007).Article 

Google Scholar 
Berdoy, M., Webster, J. P. & Mcdonald, D. W. Fatal attraction in rats infected with Toxoplasma gondii. Proc. R. Soc. B Biol. Sci. 267, 1591–1594 (2000).Article 
CAS 

Google Scholar 
Perrot-Minnot, M. J., Kaldonski, N. & Cézilly, F. Increased susceptibility to predation and altered anti-predator behaviour in an acanthocephalan-infected amphipod. Int. J. Parasitol. 37, 645–651 (2007).Article 

Google Scholar 
Cézilly, F. & Perrot-Minnot, M. J. Studying adaptive changes in the behaviour of infected hosts: A long and winding road. Behav. Proc. 68, 223–228 (2005).Article 

Google Scholar 
Seppälä, O. & Jokela, J. Host manipulation as a parasite transmission strategy when manipulation is exploited by non-host predators. Biol. Lett. 4, 663–666 (2008).Article 

Google Scholar 
Dianne, L. et al. Protection first then facilitation: A manipulative parasite modulates the vulnerability to predation of its intermediate host according to its own developmental stage. Evolution 65, 2692–2698 (2011).Article 

Google Scholar 
Iritani, R. & Sato, T. Host-manipulation by trophically transmitted parasites: The switcher-paradigm. Trends Parasitol. 34, 934–944 (2018).Article 

Google Scholar 
Poulin, R. & Maure, F. Host manipulation by parasites: A look back before moving forward. Trends Parasitol. 31, 563–570 (2015).Article 

Google Scholar 
Herbison, R., Lagrue, C. & Poulin, R. The missing link in parasite manipulation of host behaviour. Parasite Vectors 11, 1–6 (2018).Article 

Google Scholar 
Perrot-Minnot, M. J. & Cézilly, F. Investigating candidate neuromodulatory systems underlying parasitic manipulation: Concepts, limitations and prospects. J. Exp. Biol. 216, 134–141 (2013).Article 

Google Scholar 
Adamo, S. A. Parasites: Evolution’s neurobiologists. J. Exp. Biol. 216, 3–10 (2013).Article 
CAS 

Google Scholar 
Kaushik, M., Lamberton, P. H. L. & Webster, J. P. The role of parasites and pathogens in influencing generalised anxiety and predation-related fear in the mammalian central nervous system. Horm. Behav. 62, 191–201 (2012).Article 

Google Scholar 
Grupe, D. W. & Nitschke, J. B. Uncertainty and anticipation in anxiety: An integrated neurobiological and psychological perspective. Nat. Rev. Neurosci. 14, 488–501 (2013).Article 
CAS 

Google Scholar 
Perry, C. J. & Baciadonna, L. Studying emotion in invertebrates: What has been done, what can be measured and what they can provide. J. Exp. Biol. 220, 3856–3868 (2017).Article 

Google Scholar 
Adamec, R. E., Burton, P., Shallow, T. & Budgell, J. NMDA receptors mediate lasting increases in anxiety-like behavior produced by the stress of predator exposure—Implications for anxiety associated with posttraumatic stress disorder. Physiol. Behav. 65, 723–737 (1998).Article 

Google Scholar 
Bacqué-Cazenave, J. et al. Serotonin in animal cognition and behavior. Int. J. Mol. Sci. 21, 1–23 (2020).Article 

Google Scholar 
Hamilton, T. J., Kwan, G. T., Gallup, J. & Tresguerres, M. Acute fluoxetine exposure alters crab anxiety-like behaviour, but not aggressiveness. Sci. Rep. 6, 4–9 (2016).Article 

Google Scholar 
de Bekker, C. et al. Gene expression during zombie ant biting behavior reflects the complexity underlying fungal parasitic behavioral manipulation. BMC Genom. 16, 1–23 (2015).Article 

Google Scholar 
Shaw, J. C. et al. Parasite manipulation of brain monoamines in California killifish (Fundulus parvipinnis) by the trematode Euhaplorchis californiensis. Proc. R. Soc. B Biol. Sci. 276, 1137–1146 (2009).Article 
CAS 

Google Scholar 
Fayard, M., Dechaume-Moncharmont, F. X., Wattier, R. & Perrot-Minnot, M. J. Magnitude and direction of parasite-induced phenotypic alterations: A meta-analysis in acanthocephalans. Biol. Rev. 95, 1233–1251 (2020).Article 

Google Scholar 
Tain, L., Perrot-Minnot, M. J. & Cézilly, F. Altered host behaviour and brain serotonergic activity caused by acanthocephalans: Evidence for specificity. Proc. R. Soc. B Biol. Sci. 273, 3039–3045 (2006).Article 
CAS 

Google Scholar 
Perrot-Minnot, M. J., Maddaleno, M., Balourdet, A. & Cézilly, F. Host manipulation revisited: No evidence for a causal link between altered photophobia and increased trophic transmission of amphipods infected with acanthocephalans. Funct. Ecol. 26, 1007–1014 (2012).Article 

Google Scholar 
Perrot-Minnot, M. J., Sanchez-Thirion, K. & Cézilly, F. Multidimensionality in host manipulation mimicked by serotonin injection. Proc. R. Soc. B Biol. Sci. 281, (2014).Perrot-Minnot, M. J., Banchetry, L. & Cézilly, F. Anxiety-like behaviour increases safety from fish predation in an amphipod crustacea. R. Soc. Open Sci. 4, (2017).Perrot-Minnot, M. J., Balourdet, A. & Musset, O. Optimization of anesthetic procedure in crustaceans: Evidence for sedative and analgesic-like effect of MS-222 using a semi-automated device for exposure to noxious stimulus. Aquat. Toxicol. 240, 105981 (2021).Article 
CAS 

Google Scholar 
Barr, S., Laming, P. R., Dick, J. T. A. & Elwood, R. W. Nociception or pain in a decapod crustacean?. Anim. Behav. 75, 745–751 (2008).Article 

Google Scholar 
Fossat, P., Bacqué-Cazenave, J., de Deurwaerdère, P., Delbecque, J. P. & Cattaert, D. Anxiety-like behavior in crayfish is controlled by serotonin. Science 1979(344), 1293–1297 (2014).Article 
ADS 

Google Scholar 
Magee, B. & Elwood, R. W. Shock avoidance by discrimination learning in the shore crab (Carcinus maenas) is consistent with a key criterion for pain. J. Exp. Biol. 216, 353–358 (2013).Article 

Google Scholar 
Rakitin, A., Tomsic, D. & Maldonado, H. Habituation and sensitization to an electrical shock in the crab Chasmagnathus. Effect of background illumination. Physiol. Behav. 50, 477–487 (1991).Article 
CAS 

Google Scholar 
Koolhaas, J. M. et al. Stress revisited: A critical evaluation of the stress concept. Neurosci. Biobehav. Rev. 35, 1291–1301 (2011).Article 
CAS 

Google Scholar 
Yuan, T. F. & Hou, G. The effects of stress on glutamatergic transmission in the brain. Mol. Neurobiol. 51, 1139–1143 (2015).Article 
CAS 

Google Scholar 
Fossat, P., Bacqué-Cazenave, J., de Deurwaerdère, P., Cattaert, D. & Delbecque, J. P. Serotonin, but not dopamine, controls the stress response and anxiety-like behavior in the crayfish Procambarus clarkii. J. Exp. Biol. 218, 2745–2752 (2015).
Google Scholar 
Benesh, D. P., Valtonen, E. T. & Seppälä, O. Multidimensionality and intra-individual variation in host manipulation by an acanthocephalan. Parasitology 135, 617–626 (2008).Article 
CAS 

Google Scholar 
Kaldonski, N., Perrot-Minnot, M. J. & Cézilly, F. Differential influence of two acanthocephalan parasites on the antipredator behaviour of their common intermediate host. Anim. Behav. 74, 1311–1317 (2007).Article 

Google Scholar 
Kaldonski, N., Perrot-Minnot, M. J., Motreuil, S. & Cézilly, F. Infection with acanthocephalans increases the vulnerability of Gammarus pulex (Crustacea, Amphipoda) to non-host invertebrate predators. Parasitology 135, 627–632 (2008).Article 
CAS 

Google Scholar 
Parker, G. A., Ball, M. A., Chubb, J. C., Hammerschmidt, K. & Milinski, M. When should a trophically transmitted parasite manipulate its host?. Evolution 63, 448–458 (2009).Article 

Google Scholar 
Paul, E. S. & Mendl, M. T. Animal emotion: Descriptive and prescriptive definitions and their implications for a comparative perspective. Appl. Anim. Behav. Sci. 205, 202–209 (2018).Article 

Google Scholar 
Anderson, D. J. & Adolphs, R. A framework for studying emotions across species. Cell 157, 187–200 (2014).Article 
CAS 

Google Scholar 
Weinberger, J. & Klaper, R. Environmental concentrations of the selective serotonin reuptake inhibitor fluoxetine impact specific behaviors involved in reproduction, feeding and predator avoidance in the fish Pimephales promelas (fathead minnow). Aquat. Toxicol. 151, 77–83 (2014).Article 
CAS 

Google Scholar 
Curran, K. P. & Chalasani, S. H. Serotonin circuits and anxiety: What can invertebrates teach us?. Invertebr. Neurosci. 12, 81–92 (2012).CAS 

Google Scholar 
Mohammad, F. et al. Ancient anxiety pathways influence Drosophila defense behaviors. Curr. Biol. 26, 981–986 (2016).Article 
CAS 

Google Scholar 
Kavaliers, M. & Colwell, D. D. Decreased predator avoidance in parasitized mice: neuromodulatory correlates. Parasitology 111, 257–263 (1995).Article 

Google Scholar 
Chivers, D. P., Wisenden, B. D. & Smith, R. J. F. Damselfly larvae learn to recognize predators from chemical cues in the predator’s diet. Anim. Behav. 52, 315–320 (1996).Article 

Google Scholar 
Hazlett, B. A., Acquistapace, P. & Gherardi, F. Differences in memory capabilities in invasive and native crayfish. J. Crustac. Biol. 22, 439–448 (2002).Article 

Google Scholar 
R Core Team. R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. R Foundation for Statistical Computing (2014). More

Siegel, J. M. Do all animals sleep? Trends Neurosci. 31, 208–213 (2008).Article 
CAS 

Google Scholar 
Lima, S. L., Rattenborg, N. C., Lesku, J. A. & Amlaner, C. J. Sleeping under the risk of predation. Anim. Behav. 70, 723–736 (2005).Article 

Google Scholar 
Tougeron, K. & Abram, P. K. An Ecological Perspective on Sleep Disruption. Am. Nat. 190, 55–66 (2017).Article 

Google Scholar 
Lesku, J. A., Aulsebrook, A. E., Kelly, M. L. & Tisdale, R. K. Evolution of Sleep and Adaptive Sleeplessness. Handbook of Behavioral Neuroscience vol. 30 (Elsevier B.V., 2019).Smeltzer, E. A. et al. Social sleepers: The effects of social status on sleep in terrestrial mammals. Horm. Behav. 143, 105181 (2022).Article 
CAS 

Google Scholar 
Chu, H. S., Oh, J. & Lee, K. The Relationship between Living Arrangements and Sleep Quality in Older Adults: Gender Differences. Int. J. Environ. Res. Public Health 19, 3893 (2022).Karamihalev, S., Flachskamm, C., Eren, N., Kimura, M. & Chen, A. Social context and dominance status contribute to sleep patterns and quality in groups of freely-moving mice. Sci. Rep. 9, 1–9 (2019).Article 
CAS 

Google Scholar 
Capellini, I., Barton, R. A., McNamara, P., Preston, B. T. & Nunn, C. L. Phylogenetic analysis of the ecology and evolution of mammalian sleep. Evolution 62, 1764–1776 (2008).Article 

Google Scholar 
Ogawa, H., Idani, G., Moore, J., Pintea, L. & Hernandez-Aguilar, A. Sleeping Parties and nest distribution of chimpanzees in the Savanna woodland, Ugalla, Tanzania. Int. J. Primatol. 28, 1397–1412 (2007).Article 

Google Scholar 
Mulavwa, M. N. et al. Nest groups of wild bonobos at Wamba: Selection of vegetation and tree species and relationships between nest group size and party size. Am. J. Primatol. 72, 575–586 (2010).
Google Scholar 
Matsuda, I., Tuuga, A. & Higashi, S. Effects of water level on sleeping-site selection and inter-group association in proboscis monkeys: Why do they sleep alone inland on flooded days? Ecol. Res. 25, 475–482 (2010).Article 

Google Scholar 
Schreier, A. L. & Swedell, L. Ecology and sociality in a multilevel society: Ecological determinants of spatial cohesion in hamadryas baboons. Am. J. Phys. Anthropol. 148, 580–588 (2012).Article 

Google Scholar 
Kummer, H. & Kurt, F. Social units of free-living population of hamadryas baboons. Folia Primotol. 1, 4–19 (1963).Ogawa, H. & Takahashi, H. Triadic positions of Tibetan macaques huddling at a sleeping site. Int. J. Primatol. 24, 591–606 (2002).Article 

Google Scholar 
Snyder-Mackler, N., Beehner, J. C. & Bergman, T. J. Defining Higher Levels in the Multilevel Societies of Geladas (Theropithecus gelada). Int. J. Primatol. 33, 1054–1068 (2012).Article 

Google Scholar 
Mochida, K. & Nishikawa, M. Sleep duration is affected by social relationships among sleeping partners in wild Japanese macaques. Behav. Process. 103, 102–104 (2014).Article 

Google Scholar 
Di Bitetti, M. S., Vidal, E. M. L., Baldovino, M. C. & Benesovsky, V. Sleeping site preferences in tufted capuchin monkeys (Cebus apella nigritus). Am. J. Primatol. 50, 257 (2000).Article 

Google Scholar 
Takahashi, H. Huddling relationships in night sleeping groups among wild Japanese macaques in Kinkazan Island during winter. Primates 38, 57–68 (1997).Article 

Google Scholar 
Park, O., Barden, A. & Williams, E. Studies in Nocturnal Ecology, IX. Further Analysis of Activity of Panama Rain Forest Animals. Ecology 21, 122 (1940).Article 

Google Scholar 
Gaston, K. J. Nighttime ecology: The “nocturnal problem” revisited. Am. Nat. 193, 481–502 (2019).Article 

Google Scholar 
Börger, L. et al. Biologging Special Feature. J. Anim. Ecol. 89, 6–15 (2020).Article 

Google Scholar 
Krause, J. et al. Reality mining of animal social systems. Trends Ecol. Evol. 28, 541–551 (2013).Article 

Google Scholar 
Zeus, V. M., Puechmaille, S. J. & Kerth, G. Conspecific and heterospecific social groups affect each other’s resource use: a study on roost sharing among bat colonies. Anim. Behav. 123, 329–338 (2017).Article 

Google Scholar 
Wey, T. W., Burger, J. R., Ebensperger, L. A. & Hayes, L. D. Reproductive correlates of social network variation in plurally breeding degus (Octodon degus). Anim. Behav. 85, 1407–1414 (2013).Article 

Google Scholar 
Hirsch, B. T., Prange, S., Hauver, S. A. & Gehrt, S. D. Genetic relatedness does not predict racoon social network structure. Anim. Behav. 85, 463–470 (2013).Article 

Google Scholar 
Robitaille, A. L., Webber, Q. M. R., Turner, J. W. & Wal Eric, V. The problem and promise of scale in multilayer animal social networks. Curr. Zool. 67, 113–123 (2021).Article 

Google Scholar 
Smith, J. E. et al. Split between two worlds: Automated sensing reveals links between above- and belowground social networks in a free-living mammal. Philos. Trans. R. Soc. B Biol. Sci. 373, 20170249 (2018).Silk, M. J. et al. Seasonal variation in daily patterns of social contacts in the European badger Meles meles. Ecol. Evol. 7, 9006–9015 (2017).Article 

Google Scholar 
Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).Article 
CAS 

Google Scholar 
Barry, R. E. & Mundy, P. J. Seasonal variation in the degree of heterospecific association of two syntopic hyraxes (Heterohyrax brucei and Procavia capensis) exhibiting synchronous parturition. Behav. Ecol. Sociobiol. 52, 177–181 (2002).Article 

Google Scholar 
Barocas, A., Ilany, A., Koren, L., Kam, M. & Geffen, E. Variance in centrality within rock hyrax social networks predicts adult longevity. PLoS ONE 6, 1–8 (2011).Article 

Google Scholar 
Ilany, A., Barocas, A., Koren, L., Kam, M. & Geffen, E. Structural balance in the social networks of a wild mammal. Anim. Behav. 85, 1397–1405 (2013).Article 

Google Scholar 
Gravett, N., Bhagwandin, A., Lyamin, O. I., Siegel, M. & Manger, P. R. Sleep in the Rock Hyrax, Procavia capensis. Brain Behav. Evol. 79, 155–169 (2012).Coe, M. J. Notes on the habits of the mount kenya hyrax (Procavia johnstoni mackinderi thomas). Proc. Zool. Soc. Lond. 138, 638–644 (1961).
Google Scholar 
Viblanc, V. A., Pasquaretta, C., Sueur, C., Boonstra, R. & Dobson, F. S. Aggression in Columbian ground squirrels: relationships with age, kinship, energy allocation, and fitness. Behav. Ecol. 27, arw098 (2016).Article 

Google Scholar 
Wolf, J. B. W., Mawdsley, D., Trillmich, F. & James, R. Social structure in a colonial mammal: unravelling hidden structural layers and their foundations by network analysis. Anim. Behav. 74, 1293–1302 (2007).Article 

Google Scholar 
Podgórski, T., Lusseau, D., Scandura, M., Sönnichsen, L. & Jȩdrzejewska, B. Long-lasting, kin-directed female interactions in a spatially structured wild boar social network. PLoS ONE 9, 1–11 (2014).Article 

Google Scholar 
Druce, D. J. et al. Scale-dependent foraging costs: Habitat use by rock hyraxes (Procavia capensis) determined using giving-up densities. Oikos 115, 513–525 (2006).Article 

Google Scholar 
Goll, Y. et al. Sex-associated and context-dependent leadership in the rock hyrax. iScience 104063 https://doi.org/10.1016/j.isci.2022.104063 (2022).Kelley, J. L., Morrell, L. J., Inskip, C., Krause, J. & Croft, D. P. Predation risk shapes social networks in fission-fusion populations. PLoS One 6, e24280 (2011).Article 
CAS 

Google Scholar 
Brown, K. J. Seasonal variation in the thermal biology of the rock hyrax (Procavia capensis) (Document N° 10413/10124) [Master Dissertation, University of KwaZulu-Natal]. ResearchSpace Digital Library for UKZN scholarly research. http://hdl.handle.net/10413/10124.Bar Ziv, E. et al. Individual, social, and sexual niche traits affect copulation success in a polygynandrous mating system. Behav. Ecol. Sociobiol. 70, 901–912 (2016).Article 

Google Scholar 
McDonald, G. C., Spurgin, L. G., Fairfield, E. A., Richardson, D. S. & Pizzari, T. Differential female sociality is linked with the fine-scale structure of sexual interactions in replicate groups of red junglefowl, Gallus gallus. Proc. R. Soc. B Biol. Sci. 286, 20191734 (2019).Stanley, C. R., Liddiard Williams, H. & Preziosi, R. F. Female clustering in cockroach aggregations—A case of social niche construction? Ethology 124, 706–718 (2018).Article 

Google Scholar 
Pilastro, A., Benetton, S. & Bisazza, A. Female aggregation and male competition reduce costs of sexual harassment in the mosquitofish Gambusia holbrooki. Anim. Behav. 65, 1161–1167 (2003).Article 

Google Scholar 
Schoepf, I. & Schradin, C. Better off alone! Reproductive competition and ecological constraints determine sociality in the African striped mouse (Rhabdomys pumilio). J. Anim. Ecol. 81, 649–656 (2012).Article 

Google Scholar 
Brent, L. J. N., MacLarnon, A., Platt, M. L. & Semple, S. Seasonal changes in the structure of rhesus macaque social networks. Behav. Ecol. Sociobiol. 67, 349–359 (2013).Article 

Google Scholar 
Sundaresan, S. R., Fischhoff, I. R., Dushoff, J. & Rubenstein, D. I. Network metrics reveal differences in social organization between two fission-fusion species, Grevy’s zebra and onager. Oecologia 151, 140–149 (2007).Article 

Google Scholar 
Hasenjager, M. J. & Dugatkin, L. A. Fear of predation shapes social network structure and the acquisition of foraging information in guppy shoals. Proc. R. Soc. B Biol. Sci. 284, 20172020 (2017).Heathcote, R. J. P., Darden, S. K., Franks, D. W., Ramnarine, I. W. & Croft, D. P. Fear of predation drives stable and differentiated social relationships in guppies. Sci. Rep. 7, 1–10 (2017).Article 

Google Scholar 
Dunbar, R. I. M. Social structure as a strategy to mitigate the costs of group living: a comparison of gelada and guereza monkeys. Anim. Behav. 136, 53–64 (2018).Article 
CAS 

Google Scholar 
Sutcliffe, A., Dunbar, R., Binder, J. & Arrow, H. Relationships and the social brain: Integrating psychological and evolutionary perspectives. Br. J. Psychol. 103, 149–168 (2012).Article 

Google Scholar 
Brown, M. R. Comparing the Fission-Fusion Dynamics of Spider Monkeys (Ateles geoffroyi) From Day to Night. https://doi.org/10.11575/PRISM/25371 (2014).Fanson, K. V., Fanson, B. G. & Brown, J. S. Using path analysis to explore vigilance behavior in the rock hyrax (Procavia capensis). J. Mammal. 92, 78–85 (2011).Article 

Google Scholar 
Santema, P. & Clutton-Brock, T. Meerkat helpers increase sentinel behaviour and bipedal vigilance in the presence of pups. Anim. Behav. 85, 655–661 (2013).Article 

Google Scholar 
Wright, J., Berg, E., De Kort, S. R., Khazin, V. & Maklakov, A. A. Cooperative sentinel behaviour in the Arabian babbler. Anim. Behav. 62, 973–979 (2001).Article 

Google Scholar 
Moscovice, L. R., Sueur, C. & Aureli, F. How socio-ecological factors influence the differentiation of social relationships: An integrated conceptual framework. Biol. Lett. 16, 20200384 (2020).Kotler, B. P., Brown, J. S. & Knight, M. H. Habitat and patch use by hyraxes: There’s no place like home? Ecol. Lett. 2, 82–88 (1999).Article 

Google Scholar 
Margolis, E. Dietary composition of the wolf Canis lupus in the Ein Gedi area according to analysis of their droppings (in Hebrew). In Proceedings of 45th Meeting of the Israel Zoological Society, Isr. J. Ecol. Evol. 55, 157–180 (2008).Firth, J. A. & Sheldon, B. C. Social carry-over effects underpin trans-seasonally linked structure in a wild bird population. Ecol. Lett. 19, 1324–1332 (2016).Article 

Google Scholar 
Olds, N. & Shoshani, J. Procavia capensis. Mammalian Species 171, 1–7 (2016).Fourie, L. J. & Perrin, M. R. Social behaviour and spatial relationships of the rock hyrax. South 17, 91–98 (1987).Montiglio, P.-O., Ferrari, C. & Réale, D. Social niche specialization under constraints: Personality, social interactions and environmental heterogeneity. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120343 (2013).Article 

Google Scholar 
Dunbar, R. I. M. Time: a hidden constraint on the behavioural ecology of baboons. Behav. Ecol. Sociobiol. 31, 35–49 (1992).Article 

Google Scholar 
Dunbar, R. I. M., Korstjens, A. H. & Lehmann, J. Time as an ecological constraint. Biol. Rev. 84, 413–429 (2009).Article 
CAS 

Google Scholar 
Zahavi, A. Arabian babbler. In Cooperative Breeding in Birds (eds. Staceyp, B. & Koenigw, D.) 103-130 (Cambridge University Press, 1990).Smith, J. E. et al. Greetings promote cooperation and reinforce social bonds among spotted hyenas. Anim. Behav. 81, 401–415 (2011).Article 

Google Scholar 
Aureli, F. & Schaffner, C. M. Aggression and conflict management at fusion in spider monkeys. Biol. Lett. 3, 147–149 (2007).Article 

Google Scholar 
Deag, J. M. The diurnal patterns of behaviour of the wild Barbary macaque Macaca sylvanus. J. Zool. 206, 403–413 (1985).Article 

Google Scholar 
Canteloup, C., Cera, M. B., Barrett, B. J. & van de Waal, E. Processing of novel food reveals payoff and rank-biased social learning in a wild primate. Sci. Rep. 11, 1–13 (2021).Article 

Google Scholar 
Dragić, N., Keynan, O. & Ilany, A. Multilayer social networks reveal the social complexity of a cooperatively breeding bird. iScience 24, 103336 (2021).Kulahci, I. G., Ghazanfar, A. A. & Rubenstein, D. I. Knowledgeable Lemurs Become More Central in Social Networks. Curr. Biol. 28, 1306–1310.e2 (2018).Article 
CAS 

Google Scholar 
Schino, G. Grooming and agonistic support: A meta-analysis of primate reciprocal altruism. Behav. Ecol. 18, 115–120 (2007).Article 

Google Scholar 
Kutsukake, N. & Clutton-Brock, T. H. Social functions of allogrooming in cooperatively breeding meerkats. Anim. Behav. 72, 1059–1068 (2006).Article 

Google Scholar 
Schweinfurth, M. K., Stieger, B. & Taborsky, M. Experimental evidence for reciprocity in allogrooming among wild-type Norway rats. Sci. Rep. 7, 1–8 (2017).Article 
CAS 

Google Scholar 
Nandini, S., Keerthipriya, P. & Vidya, T. N. C. Group size differences may mask underlying similarities in social structure: A comparison of female elephant societies. Behav. Ecol. 29, 145–159 (2018).Article 

Google Scholar 
Hamede, R. K., Bashford, J., McCallum, H. & Jones, M. Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease. Ecol. Lett. 12, 1147–1157 (2009).Article 

Google Scholar 
Henkel, S., Heistermann, M. & Fischer, J. Infants as costly social tools in male Barbary macaque networks. Anim. Behav. 79, 1199–1204 (2010).Article 

Google Scholar 
Prehn, S. G. et al. Seasonal variation and stability across years in a social network of wild giraffe. Anim. Behav. 157, 95–104 (2019).Article 

Google Scholar 
Borgeaud, C., Sosa, S., Sueur, C. & Bshary, R. The influence of demographic variation on social network stability in wild vervet monkeys. Anim. Behav. 134, 155–165 (2017).Article 

Google Scholar 
Kerth, G., Perony, N. & Schweitzer, F. Bats are able to maintain long-term social relationships despite the high fission-fusion dynamics of their groups. Proc. R. Soc. B 278, 2761–2767 (2011).Article 

Google Scholar 
Silk, J. B. et al. The benefits of social capital: Close social bonds among female baboons enhance offspring survival. Proc. R. Soc. B Biol. Sci. 276, 3099–3104 (2009).Article 

Google Scholar 
Riehl, C. & Strong, M. J. Stable social relationships between unrelated females increase individual fitness in a cooperative bird. Proc. R. Soc. B Biol. Sci. 285, 20180130 (2018).Shizuka, D. & Johnson, A. E. How demographic processes shape animal social networks. Behav. Ecol. 31, 1–11 (2020).Article 

Google Scholar 
Sick, C. et al. Evidence for varying social strategies across the day in chacma baboons. Biol. Lett. 10, 3–6 (2014).Article 

Google Scholar 
Barrett, L., Peter Henzi, S. & Lusseau, D. Taking sociality seriously: The structure of multi-dimensional social networks as a source of information for individuals. Philos. Trans. R. Soc. B Biol. Sci. 367, 2108–2118 (2012).Article 

Google Scholar 
Henzi, S. P., Lusseau, D., Weingrill, T., Van Schaik, C. P. & Barrett, L. Cyclicity in the structure of female baboon social networks. Behav. Ecol. Sociobiol. 63, 1015–1021 (2009).Article 

Google Scholar 
Ripperger, S. P. & Carter, G. G. Social foraging in vampire bats is predicted by long-term cooperative relationships. PLoS Biol. 19, 1–17 (2021).Article 

Google Scholar 
Wittemyer, G., Douglas-Hamilton, I. & Getz, W. M. The socioecology of elephants: analysis of the processes creating multitiered social structures. Anim. Behav. 69, 1357–1371 (2005).Article 

Google Scholar 
Wittemyer, G., Getz, W. M., Vollrath, F. & Douglas-Hamilton, I. Social dominance, seasonal movements, and spatial segregation in African elephants: A contribution to conservation behavior. Behav. Ecol. Sociobiol. 61, 1919–1931 (2007).Article 

Google Scholar 
Gelardi, V., Fagot, J., Barrat, A. & Claidière, N. Detecting social (in)stability in primates from their temporal co-presence network. Anim. Behav. 157, 239–254 (2019).Article 

Google Scholar 
Hobson, E. A., Ferdinand, V., Kolchinsky, A. & Garland, J. Rethinking animal social complexity measures with the help of complex systems concepts. Anim. Behav. 155, 287–296 (2019).Article 

Google Scholar 
Kappeler, P. M. A framework for studying social complexity. Behav. Ecol. Sociobiol. 73, 13 (2019).Ballerini, M. et al. Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proc. Natl Acad. Sci. USA 105, 1232–1237 (2008).Article 
CAS 

Google Scholar 
Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., Aron, S. & Camazine, S. Self-organization in social insects. Trends Ecol. Evol. 12, 188–193 (1997).Article 
CAS 

Google Scholar 
Wickramasinghe, A. & Muthukumarana, S. Assessing the impact of the density and sparsity of the network on community detection using a Gaussian mixture random partition graph generator. Int. J. Inf. Technol. 14, 607–618 (2022).
Google Scholar 
Motalebi, N., Stevens, N. T. & Steiner, S. H. Hurdle Blockmodels for Sparse Network Modeling. Am. Stat. 75, 383–393 (2021).Article 

Google Scholar 
Gokcekus, S., Cole, E. F., Sheldon, B. C. & Firth, J. A. Exploring the causes and consequences of cooperative behaviour in wild animal populations using a social network approach. Biol. Rev. 96, 2355–2372 (2021).Article 

Google Scholar 
Koren, L., Mokady, O. & Geffen, E. Social status and cortisol levels in singing rock hyraxes. Horm. Behav. 54, 212–216 (2008).Article 
CAS 

Google Scholar 
Boyland, N. K., James, R., Mlynski, D. T., Madden, J. R. & Croft, D. P. Spatial proximity loggers for recording animal social networks: Consequences of inter-logger variation in performance. Behav. Ecol. Sociobiol. 67, 1877–1890 (2013).Article 

Google Scholar 
Drewe, J. A. et al. Performance of proximity loggers in recording Intra- and Inter-species interactions: A laboratory and field-based validation study. PLoS ONE 7, e39068 (2012).Hoppitt, W. & Farine, D. Association Indices For Quantifying Social Relationships: How To Deal With Missing Observations Of Individuals Or Groups. Anim. Behav. 136, 227–238 (2018).Article 

Google Scholar 
Farine, D. R. Animal social network inference and permutations for ecologists in R using asnipe. Methods Ecol. Evol. 4, 1187–1194 (2013).Bejder, L., Fletcher, D. & Bräger, S. A method for testing association patterns of social animals. Anim. Behav. 56, 719–725 (1998).Article 
CAS 

Google Scholar 
Kalinka, A. T. & Tomancak, P. linkcomm: An R package for the generation, visualization, and analysis of link communities in networks of arbitrary size and type. Bioinformatics 27, 2011–2012 (2011).Article 
CAS 

Google Scholar 
R Core Team, R. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2020).Wild, F. lsa: Latent Semantic Analysis. R package version 0.73.2. https://CRAN.R-project.org/package=lsa (2020).Han, J., Kamber, M. & Pei, J. Getting to Know Your Data. An R Companion Third Ed. Fundam. Polit. Sci. Res. https://doi.org/10.1016/B978-0-12-381479-1.00002-2 (2021).Benjamini, Y. Controlling the false discovery rate – A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Google Scholar 
Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Syst. 1695, 1–9 (2006).Dai, H., Leeder, J. S. & Cui, Y. A modified generalized fisher method for combining probabilities from dependent tests. Front. Genet. 20, 2–7 (2014).
Google Scholar  More

Trichoderma species collectionEighty strains were obtained from 100 soil samples collected from Zoige alpine wetland ecological regions in China. Details of the strains isolated from soil samples are given in Table 1. All strains were subsequently used for morphological identification, while fifty-seven were used for phylogenetic analysis.Table 1 Details of 80 Trichoderma isolates from the Zoige alpine wetland in this study.Full size tablePhylogenetic analysisThe ITS region used preliminarily as a species identification criterion was applied to TrichOKey at www.ISTH.info70. However, the ITS region has a low number of variable sites and long insertions in certain species; thus, it is unsuitable for a phylogenetic reconstruction of this group41. Our study successfully amplified most fragments of the genes tef1, rpb2, and acl1. We also designed a pair of new primers based on the full-length tef1 gene, 5′-GAGAAGTTCGAGAAGGTGAGC-3′ and 5′-ATGTCACGGACGGCGAAAC-3′, with which a 1.4-kb fragment was amplified for most isolates.All samples analyzed in our study were divided into 4 primary clades based on the gpd gene region, including 49 strains from the T. harzianum complex, 3 T. rossicum strains, 1 T. polysporum strain and one unknown species (4 Trichoderma sp. strains) (Fig. 1). Maximum parsimony analysis was conducted among 101 strains, with Protocrea farinosa (CPK 2472) and P. pallida (CBS 299.78) used as outgroup (Table 2). The dataset for the rpb2, tef1 and acl1 genes contained 3403 characteristics, among which 1152 were parsimony-informative, 988 were variable and parsimony-uninformative, and 1263 were constant. The most parsimonious trees are shown in Fig. 2 (tree length = 5054, consistency index = 0.6005, homoplasy index = 0.3995, retention index = 0.8105, rescaled consistency index = 0.4867).Figure 1Neighbor-joining tree based on partial gpd gene sequences from 57 Trichoderma isolates. Parsimony bootstrap values of more than 50% are shown at nodes.Full size imageTable 2 Trichoderma strain included in the multi-gene sequence analysis, with details of clade, strain number, location, and GenBank accessions of the sequences generated.Full size tableFigure 2Maximum parsimony tree of Trichoderma species inferred from the combined rpb2, tef1 and acl1 partial sequences. Maximum parsimony bootstrap values above 50% are shown at nodes. The tree was rooted with Protocrea farinose and P. pallida Isolates from this study are shown in red (new species in bold).Full size imageThe phylogram showed that 57 stains belonged to the following four clades: Harzianum, Polysporum, Stromaticum, and Longibrachiatum. The strains of the first three clades with neighboring named species were well supported by bootstrap values greater than 90%. The Harzianum clade contained T. alni, T. atrobrunneum, T. harzianum and T. pyramidale of the Trichoderma species complex. The Polysporum clade contained only T. polysporum, and the Stromaticum clade contained T. rossicum. The Longibrachiatum clade contained four strains of Trichoderma sp., T25, T43, T44 and T48, which were separated from any other known taxa of this clade showed a low bootstrap value (MPBP = 62%) with T. citrinoviride and T. saturnisporum. We thus regarded it as a new species and named it Trichoderma zoigense, as described in the next section.Growth ratesAs shown in Fig. 3, the genus Trichoderma from Zoige alpine wetland ecological regions was able to grow in a range from 15 to 35 °C, and the suitable growth temperature for most species ranged from 20 to 30 °C. All seven species identified had normal viability at relatively low temperature (15 °C), and they rarely grew well over 35 °C except for T. zoigense. For T. atrobrunneum, T. harzianum and T. pyramidale, the optimum growth temperature on CMD was 25 to 30 °C. T. alni and T. rossicum preferred a cool growth environment, with an optimum temperature of 25 °C, whereas T. zoigense was more partial to a hot environment, with an optimum temperature of 30 °C, and it even grew well up to 35 °C. T. polysporum was the only slow-growing species that grew with less than 6.0 mm/day between 15 and 30 °C and did not survive at 35 °C. The above results showed that all species had different growth rates but were not completely differentiated from each other on CMD. These species were roughly divided into four groups based on their optimum growth temperature.Figure 3Growth rates of 7 species of Trichoderma on CMD given as mm per day at five temperatures. The values were the means of 3–5 experiments, with 1–3 representative isolates per species.Full size imageRelationship with ecological factorsOur results revealed a substantial disparity in the number and distribution of Trichoderma species among Zoige alpine wetland ecological regions (Tables 3, 4). Table 3 showed that T. harzianum was found in all four soil types, but most isolates of this species were obtained from peat soil. T. rossicum, T. alni and T. zoigense were also present in meadow soil and subalpine meadow soil, whereas T. atrobrunneum was found in aeolian sandy soil and peat soil. T. polysporum was found only in peat soil.Table 3 Isolation frequency of Trichoderma species in different soil types (%).Full size tableTable 4 Isolation frequency of Trichoderma species in different soil layers (%) species.Full size tableIn regard to the different soil layers shown in Table 4, T. harzianum was widely distributed in the five soil layers at depths of 0–100 cm. T. rossicum, T. alni and T. zoigense were isolated mainly from the soil layers at depths of 0–50 cm. Both T. atrobrunneum and T. pyramidale were isolated from depths of 0–10 cm, and T. polysporum was found only in the soil layers at depths of 50–100 cm.Regarding isolation frequency, T. harzianum was the most common of the seven species with a 23% isolation frequency, and it was therefore the dominant species in the zone, while the rare species T. polysporum and T. pyramidale had the lowest isolation frequencies at 1%.TaxonomyNew speciesTrichoderma zoigense G.S. Gong & G.T. Tang, sp. nov. (Fig. 4).Figure 4Cultures and asexual morph of Trichoderma zoigense. (a–d). Cultures at 20 °C [(a) on CMD, 7 days; (b) on MEA, 4 days; (c) on PDA, 4 days; and (d) on SNA, 7 days]. (e) Conidiation tuft (CMD, 4 days). (f–k) Conidiophores and phialides (CMD, 5–7 days). (l) Chlamydospores (PDA, 8 days). (m) Conidia (CMD, 5 days). Scale bars: (e) = 2 mm; (f–m) = 10 μm.Full size imageMycoBank: MB 82114.Typification: CHINA. SICHUAN PROVINCE: Zoige Alpine Wetland, on soil, 29 June 2013, G.S. Gong T44 (holotype CGMCC3.20145). GenBank: ITS = KX632531; TEF = KX632588; RPB2 = KX632645; ACL1 = KX632702; GPD = KX632759.Etymology: zoigense (Latin), the specific epithet about the place where the type was found.Description: Cultures and anamorph: optimal growth at 25 °C on all four media. On CMD after 72 h, growth is 25–28 mm at 20 °C and 28–31 mm at 25 °C. Colony is dense and has a wavy to crenate margin. Surface becomes distinctly zonate and white to grayish-green but celadon to atrovirens later, and it is granular in the center and distinctly radially downy outside and shows whitish surface hyphae and reverse-diffusing croci to pale brown pigment (Fig. 4a). Aerial hyphae are numerous to punctate and long, forming radial strands, with white mycelial patches appearing in aged cultures (Fig. 4e). Autolytic excretions are rare, with no coilings observed. Conidiation was noted after 3–4 d at 25 °C, a yellow or greenish color appears after 7 days, conidiation is effuse, and in intense tufts, erect conidiophores occur around the plug and on aerial hyphae. They are mainly concentrated along the colony center, show a white color that turns green, and then finally degenerate, with conidia often adhering in chains. Conidiophores are short and simple with asymmetric branches. Branches produce phialides directly. Phialides are generally solitary along main axes and side branches and sometimes paired in the terminal position of the main axes, sometimes in whorls of 2–3. Phialides are 4.5–10.5 × 2–5 μm ((overline{x }) = 7.5 ± 1.5 × 3 ± 0.5, n = 50) and 1.5–2.5 μm ((overline{x }) = 2 ± 0.2) wide at the base, lageniform or ampulliform, mostly uncinate or slightly curved, less straight, and often distinctly widened in the middle (Fig. 4f–k). Conidia are 3–4.5 × 2.3–4 μm ((overline{x }) = 3.5 ± 0.3 × 3 ± 0.3, n = 50) and initially hyaline, and they turn green and are oblong or ellipsoidal, almost with constricted sides, and smooth, eguttulate or with minute guttules, with indistinct scars (Fig. 4m).On PDA, after 72 h, growth is 35–41 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5 days at 25 °C. Colonies are dense with wavy to crenate margins; and mycelia are conspicuously differentiated in width of the primary and secondary hyphae. Surface becomes distinctly zonate, yellowish-green to prasinous in color and celadon to atrovirens later, and it is farinose to granular in the center, distinctly radially downy outside, with whitish of surface hyphae and reverse-diffusing brilliant yellow to fruit-green pigment (Fig. 4c). Aerial hyphae are numerous, long and ascend several millimeters, forming radial strands, with white mycelial patches appearing in aged cultures. Autolytic excretions are rare; and no coilings are observed. Odor is indistinct or fragrant. Chlamydospores examined after 7 days at 4.5–9 × 4.5–7.5 μm ((overline{x }) = 6 ± 1.1 × 6 ± 0.7, n = 50), and they are terminal, intercalary, globose or ellipsoidal, and smooth (Fig. 4l). Conidiation is noted after 3–4 days and yellow or greenish after 7 days. Conidiophores are short and simple with asymmetric branches; conidia are greenish, ellipsoidal, and smooth.On SNA, after 72 h, growth is 13–15 mm at 20 °C and, 16–21 mm at 25 °C; and mycelium covers the plate after 12–13 days at 25 °C. Colony is similar to that on CMD, with a little wave margin, although mycelia are looser and slower on the agar surface. Aerial hyphae are relatively inconspicuous and long along the colony margin. Autolytic activity and coiling are absent or inconspicuous. No diffusing pigment or distinct odor are produced (Fig. 4d). Conidiation was noted after 3–4 days at 25 °C, and many amorphous, loose white or aqua cottony tufts occur, mostly median from the plug outwards, and they are confluent to masses up and white but then turn green. After 4–5 days, conidiation becomes dense within the tufts, which are loose at their white margins with long, straight, or slightly sinuous sterile ends in the periphery. Tufts consisting of a loose reticulum with branches often at right angles, give rise to several main axes. Main axes are regular and tree-like, with few or many paired or unpaired side branches. Branches are flexuous, and phialides are solitary along the main axes and side branches, and they are sometimes paired in the terminal position of the main axes, sometimes in whorls of 2–3 that are often cruciform or in pseudo-whorls up to 4. Phialides and conidia are similar to that on CMD.New records for ChinaTrichoderma atrobrunneum F. B. Rocha et al., Mycologia 107: 571, 2015 (Fig. 5).Figure 5Cultures and asexual morph of Trichoderma atrobrunneum. (a–d) Cultures at 25 °C [(a) on CMD, 7 days; (b) on MEA, 4 days; (c) on PDA, 15 days; and (d) on SNA, 7 days]. (e) Conidiation tuft (SNA, 7 days). (f–i,k,l) Conidiophores and phialides (CMD, 5–7 days). (j) Conidia (CMD, 6 days). (m) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–m) = 10 μm.Full size imageSpecimen examined: CHINA. SICHUAN PROVINCE: Zoige Alpine Wetland, on soil, 29 June 2013, G.S. Gong T42 (holotype CGMCC.20167). GenBank: ITS = KX632514; TEF = KX632571; RPB2 = KX632628; ACL1 = KX632685; GPD = KX632742.Description: Cultures and anamorph: optimal growth at 25 °C on all media. On CMD, after 72 h, growth is 35–37 mm at 20 °C and 46–53 mm at 25 °C; mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are loose and thin; hyphae are narrow, sinuous and often form strands on the margin (Fig. 5a). Aerial hyphae are slight, forming a thin white to green downy fluffy or floccose mat. The light brown or brown pigment is observed, with no distinct odor noted. Conidiophores are pyramidal, often with opposing and somewhat widely spaced branches, with the main axis and each branch terminating in a cruciate, sometimes verticillate, whorl of up to four phialides. Phialides are ampulliform to lageniform and 4.9–7.6 × 2.2–3.0 μm ((overline{x }) = 6 ± 0.7 × 2.5 ± 0.2, n = 50) and 1.5–2.5 μm ((overline{x }) = 1.5 ± 0.3) wide at the base (Fig. 5f–i,k,l). Conidia are 2.5–4 × 2.5–3.5 μm ((overline{x }) = 3 ± 0.3 × 3 ± 0.2, n = 50), yellow to green, smooth, and circular to ellipsoidal (Fig. 5j).On PDA, after 72 h, growth is 41–43 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show indistinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide, sinuous and often form strands on the margin (Fig. 5c). Margin is thick and defined. Aerial hyphae are abundant and form a thick green downy mat. Conidiation forms abundantly within 4 days in broad concentric rings. Chlamydospores examined after 7 days are 5–9 × 5.5–8.5 μm ((overline{x }) = 6.5 ± 0.9 × 6.5 ± 0.9, n = 30), globose when terminal, smooth, and intercalary (Fig. 5m).On SNA, after 72 h, growth is 33–35 mm at 20 °C and 38–40 mm at 25 °C; and mycelium covers the plate after 7–8 days at 25 °C. Colonies show distinct zonation. Mycelia are thin and yellow to green; hyphae are wide and sinuous, with indistinct strands on the margin (Fig. 5d). Margin is thin and ill-defined. Aerial hyphae are slight, forming a thin green downy fluff appearing in the colony (Fig. 5e). Diffusing pigment was observed in a ring, and no distinct odor was noted. Conidiation is similar to CMD.Accepted species previously reported in ChinaTrichoderma alni Jaklitsch, Mycologia 100: 799. 2008 (Fig. 6).Figure 6Cultures and asexual morph of Trichoderma alni. (a–d). Cultures after 7 days at 25 °C [(a) on CMD; (b) on MEA; (c) on PDA; and (d) on SNA]. € Coilings of aerial hyphae (PDA, 6 days). (f–j,l). Conidiophores and phialides (CMD, 5–7 days). (k) Conidiation tuft (PDA, 7 days). (m) Conidia (CMD, 6 days). (n,o) Chlamydospores (PDA, 7 days). Scale bars: (e–j,l–o) = 10 μm; (k) = 2 mm.Full size imageDescription: Cultures and anamorph: Optimum growth at 25 °C on all media; no growth at 35 °C. On CMD, after 72 h, growth of 34–36 mm at 20 °C and 50–51 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are loose and thin; hyphae are narrow and sinuous and often form strands on the margin (Fig. 6a). Aerial hyphae are slight and form a thin white to green downy, fluffy or floccose mat. No diffusing pigment or distinct odor is noted. Conidiophores are hyaline and thick, with side branches on several levels at the base of the elongations that are mostly paired and in right angles with phialides in whorls of 3–5. Phialides are 5.5–11.5 × 2–3.5 μm ((overline{x }) = 8 ± 1.4 × 2.5 ± 0.4, n = 50) and 1.5–2.5 μm ((overline{x }) = 2 ± 0.4) wide at the base, often short and wide, and ampulliform (Fig. 6f–j,l). Conidia are 3–4 × 2.5–3.5 μm ((overline{x }) = 3.5 ± 0.2 × 3 ± 0.2, n = 50), dark green, smooth, and ellipsoidal (Fig. 6m).On PDA, after 72 h, growth is 33–35 mm at 20 °C and 41–43 mm at 25 °C; and mycelium covers the plate after 6–7 days at 25 °C. Colonies show indistinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide, sinuous and often form strands on the margin (Fig. 6c). Margin is thin and ill defined. Aerial hyphae are slight, coiled (Fig. 6e), forming a thin white to green downy, fluffy or floccose mat (Fig. 6k). Chlamydospores examined after 7 days are 6–9.5 × 5–8 μm ((overline{x }) = 7.5 ± 0.9 × 7 ± 0.9, n = 30), globose to oval when terminal, and smooth, and few are intercalary (Fig. 6n,o).On SNA, after 72 h, growth is 18–19 mm at 20 °C and 28–32 mm at 25 °C; and mycelium covers the plate after 6–7 days at 25 °C. Colonies show distinct zonation. Mycelia are thin and yellow to green; hyphae are wide and sinuous and show indistinct strands on the margin (Fig. 6d). Margin is thin and ill-defined. Aerial hyphae are slight and form a thin white downy, fluffy, or floccose mat appearing in distal parts of the colony. No diffusing pigment or distinct odor was noted. Conidiation is similar to CMD.Trichoderma harzianum Rifai, Mycol. Pap. 116: 38, 1969 (Fig. 7).Figure 7Cultures and asexual morph of Trichoderma harzianum. (a–d) Cultures after 7 days at 20 °C [(a) on CMD; (b) on MEA; (c) on PDA; and (d) on SNA]. (e) Conidiation tuft (CMD, 7 days). (f–j) Conidiophores and phialides (CMD, 5–7 days). (k) Conidia (CMD, 5 days). (l,m) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–m) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 25 °C on all media. On CMD, after 72 h, growth is 34–38 mm at 20 °C and 46–53 mm at 25 °C; mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are loose and thin; hyphae are narrow, sinuous, and often form strands on the margin (Fig. 7a). Aerial hyphae are abundant and radiating and form thick green downy, fluffy, or floccose mats (Fig. 7e). No diffusing pigment, but fragrant odor noted. Conidiophores are pyramidal with opposing branches, with each branch terminating in a cruciate whorl of up to four or five phialides. Phialides are frequently solitary or in a whorl of three or four. Phialides are ampulliform to lageniform and often constricted below the tip to form a narrow neck of 4.5–8 × 2–3.5 μm ((overline{x }) = 6 ± 0.8 × 2.5 ± 0.3, n = 50) and 1–2.5 μm ((overline{x }) = 2 ± 0.3) wide at the base (Fig. 7f–j). Conidia are subglobose to ovoid, 3–4.5 × 2.5–3.3 μm ((overline{x }) = 3.5 ± 0.3 × 3 ± 0.2, n = 50), laurel-green to bright green, smooth, and ellipsoidal (Fig. 7k).On PDA, after 72 h, growth is 41–43 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide and sinuous and often form strands on the margin (Fig. 7c). Margin is thick and ill defined. Aerial hyphae are abundant and radiating and form thick green downy, fluffy or floccose mats. Chlamydospores examined after 7 days are 5.5–9 × 5.5–9.0 μm ((overline{mathrm{x} }) = 7 ± 0.8 × 7 ± 0.8, n = 30), globose to oval when terminal and smooth, showing an almost unobserved intercalary (Fig. 7l,m).On SNA, after 72 h, growth is 33–35 mm at 20 °C and 38–40 mm at 25 °C; and mycelium covers the plate after 7–8 days at 25 °C. Colonies show distinct zonation. Mycelia are thin and green; hyphae are narrow and sinuous and show indistinct strands on the margin (Fig. 7d). Margin is thin and ill defined. Aerial hyphae are slight and form a thick downy, fluffy, or floccose mat appearing in the colony. No diffusing pigment or distinct fragrant odor was noted. Conidiation was similar to CMD.Trichoderma polysporum Rifai, Mycol. Pap. 116: 18, 1969 (Fig. 8).Figure 8Cultures and asexual morph of Trichoderma polysporum. (a–d) Cultures at 20 °C [(a) on CMD, 7 days; (b) on MEA, 15 days; (c) on PDA, 15 days; and (d) on SNA, 15 days]. (i) Conidiation tuft (PDA, 15 days). (e–h,j) Conidiophores and phialides (CMD, 5–7 days). (k) Chlamydospores (CMD, 7 days). (l) Conidia (PDA, 6 days). Scale bars: (i) = 2 mm; (e–h,j) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 20 °C on all media, no growth at 35 °C. On CMD, after 72 h, growth is 14–16 mm at 20 °C and 9–12 mm at 25 °C; and mycelium covers the plate after 9–10 days at 20 °C. A colony is hyaline, thin and loose, with little mycelium on the agar surface, and it is indistinctly zonate but becomes zonate by conidiation in white tufts after 4–5 d and grass green to green after 6 days (Fig. 8a). Aerial hyphae are long and dense and forming little greenish aggregates that are granular to pulvinate. No pigment or odor. Conidiation noted after 4–5 days, and it is white to greenish, with sterile smooth to rough helical elongations in the distal zones from pustules. Conidiophores are hyaline and thick with side branches on several levels at the base of the elongations that are mostly paired and at right angles with phialides in whorls of 2–5. Phialides are 5–10.5 × 2.5–4 μm ((overline{x }) = 7 ± 1.9 × 3.5 ± 0.4, n = 50) and 2–4 μm ((overline{x }) = 3 ± 0.5) wide at the base, often short and wide and ampulliform (Fig. 8e–h,j). Conidia are 2.5–4 × 2–3 μm ((overline{x }) = 3.5 ± 0.4 × 2.5 ± 0.2, n = 50), hyaline, smooth, and ellipsoidal (Fig. 10l).On PDA, after 72 h, growth is 24–26 mm at 20 °C and 13–16 mm at 25 °C; and mycelium covers the plate after 8–9 days at 20 °C. A colony is densest, distinctly zonate, and grass green to spearmint green; mycelia are conspicuously dense; and surface hyphae form radial strands (Fig. 8c). Aerial hyphae are long and dense and form greenish aggregates that are granular to pulvinate (Fig. 8i). No diffusing pigment and odor. Chlamydospores examined after 7 days are 5.5–9 × 5–7.5 μm ((overline{x }) = 7 ± 0.9 × 6 ± 0.6, n = 30), globose to oval when terminal, and smooth, with an almost unobserved intercalary (Fig. 8k).On SNA, growth is approximately 7 mm/day at 20 °C and 5 mm/day at 25 °C; and mycelium covers the plate after 10 days at 20 °C. A colony is hyaline, thin, and loose, with little mycelium on the agar surface, not or indistinctly zonate, but becomes zonate by conidiation in white tufts after 4–5 days; and the margin is downy by long aerial hyphae, which degenerating/dissolving soon (Fig. 8d).Trichoderma pyramidale W. Jaklitsch & P. Chaverri, Mycologia 107: 581, 2015 (Fig. 9).Figure 9Cultures and asexual morph of Trichoderma pyramidale. (a–d) Cultures at 25 °C [(a) on CMD, 7 days; (b) on MEA, 4 days; (c) on PDA, 4 days; and (d) on SNA, 4 days]. (e) Conidiation tuft (PDA, 7 days). (f–j) Conidiophores and phialides (CMD, 5–7 days). (k) Conidia (CMD, 6 days). (l) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–l) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 25 °C on all media, with little growth at 35 °C. On CMD, after 72 h, growth is 29–32 mm at 20 °C and 48–53 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show distinct zonation. Mycelium is loose and thin; hyphae are narrow, sinuous, and often form strands on the margin (Fig. 9a). Aerial hyphae are slight, forming a thin white to green downy, fluffy or floccose mat. Brown pigment is shown, but no distinct odor noted. Conidiophores are hyaline and thick with side branches on several levels at the base of the elongations that are mostly paired and at right angles with phialides in whorls of 3–5. Phialides are 5–9.5 × 2.5–3 μm ((overline{x }) = 7 ± 1.1 × 3 ± 0.3, n = 50) and 1–2.5 μm ((overline{x }) = 1.5 ± 0.3) wide at the base and often short, wide, and ampulliform (Fig. 9f–j). Conidia are 2.5–4 × 2.5–3.5 μm ((overline{x }) = 3.5 ± 0.3 × 3 ± 0.2, n = 50), green, smooth, and ellipsoidal (Fig. 9k).On PDA, after 72 h, growth is 41–43 mm at 20 °C and 50–55 mm at 25 °C; and mycelium covers the plate after 5–6 days at 25 °C. Colonies show indistinct zonation. Mycelia are dense, opaque, and thick; hyphae are wide, sinuous and often form strands on the margin (Fig. 9c). Margin is thin and ill defined. Aerial hyphae are slight and form a thin white to green downy, fluffy or floccose mat (Fig. 9e). Chlamydospores examined after 7 days are 5.5–10 × 5.5–10 μm ((overline{x }) = 7 ± 0.9 × 7 ± 0.9, n = 30), globose to oval when terminal or intercalary, and smooth (Fig. 9l).On SNA, after 72 h, growth is 33–35 mm at 20 °C and 38–40 mm at 25 °C; and mycelium covers the plate after 7–8 days at 25 °C. Colonies show distinct zonation. Mycelium is thin, yellow to green; hyphae are wide, sinuous, with indistinct strands on the margin (Fig. 9d). Margin is thin and ill defined. Aerial hyphae are slight and form a thin white downy, fluffy or floccose mat in distal parts of the colony. No diffusing pigment or distinct odor noted. Conidiation similar to CMD.Trichoderma rossicum Bissett et al., Canad. J. Bot. 81: 578, 2003 (Fig. 10).Figure 10Cultures and asexual morph of Trichoderma rossicum. (a–d) Cultures after 7 days at 25 °C [(a) on CMD; (b) on MEA; (c) on PDA; and (d) on SNA]. € Conidiation tuft (PDA, 7 days). (f–h,j,k) Conidiophores and phialides (CMD, 5–7 days). (i) Elongations (CMD, 6 days). (l,n) Conidia (CMD, 6 days). (m) Chlamydospores (PDA, 7 days). Scale bars: (e) = 2 mm; (f–n) = 10 μm.Full size imageDescription: Cultures and anamorph: optimal growth at 25 °C on all media. On CMD, growth of 10–11 mm/day at 20 °C and 15–17 mm/day at 25 °C; and mycelium covers the plate after 6–7 days at 20 °C. Colony is dense with a wavy margin, and the surface becomes distinctly zonate (Fig. 10a). Aerial hyphae are numerous, long, elongate, and villiform in the plate (Fig. 10i). No diffusing pigment or odor. Autolytic activity is variable, and coilings are scarce or inconspicuous. Conidiation noted after 3–4 days at 20 °C. Conidiation is effuse and in intense tufts that are hemispherical or irregular, and they show wide wheel grain banding that is gray green to deep green. Conidiophores radiate from the reticulum and are broad, straight, sinuous or helically twisted, show distally slightly pointed elongations, taper from the main axes to top branches, and present primary branches arranged in pairs or in whorls of 2–3, with secondary branches to solitary. Phialides are 4.5–14 × 2.5–4 μm ((overline{x }) = 7 ± 1.5 × 3.5 ± 0.3, n = 50) and 2–3.5 μm ((overline{x }) = 3 ± 0.4) wide at the base, ampulliform, and in whorls of 3–6 (Fig. 10f–h,j,k). Conidia are 3.5–5.5 × 2.5–4 μm ((overline{x }) = 4.5 ± 0.5 × 3 ± 0.2, n = 50), short cylindrical, and a gray color when single and pea green to yellow green in a group (Fig. 10l,n).On PDA, growth is 12–15 mm/day at 20 °C, 12–16 mm/day at 25 °C; and mycelium covers the plate after 4–5 days at 25 °C. Colony is denser with a wavy margin than that on CMD, and the surface is distinctly zonate (Fig. 10c). Aerial hyphae are numerous, long, and villiform to pulvinate in the plate. No diffusing pigment and odor (Fig. 10e). Autolytic activity is variable, coilings are scarce or inconspicuous. Chlamydospores examined after 7 days are 6.5–9.5 × 6–9 μm ((overline{x }) = 7 ± 1.0 × 7 ± 0.9, n = 30), terminal and intercalary, globose or ellipsoidal, and smooth (Fig. 10m).On SNA, growth is 8–13 mm/day at 20 °C and 8–12 mm/day at 25 °C; and mycelium covers the plate after 6–7 day at 25 °C. Colony is hyaline, thin and dense; and mycelium degenerate rapidly (Fig. 10d). Aerial hyphae are inconspicuous, autolytic activity is scant, and coilings are distinct. Conidiation noted after approximately 4 days and starts in white fluffy tufts spreading from the center to form concentric zones, and they compact to pustules with a white to greenish color. More

Valiela, I., Bowen, J. L. & York, J. K. Mangrove Forests: One of the World’s Threatened Major Tropical Environments: At least 35% of the area of mangrove forests has been lost in the past two decades, losses that exceed those for tropical rain forests and coral reefs, two other well-known threatened environments. Bioscience 51, 807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2 (2001).Article 

Google Scholar 
Kuenzer, C., Bluemel, A., Gebhardt, S., Quoc, T. V. & Dech, S. Remote sensing of mangrove ecosystems: A review. Remote Sens. 3, 1. https://doi.org/10.3390/rs3050878 (2011).Article 

Google Scholar 
Turschwell, M. P. et al. Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally. Biol. Cons. 247, 108637. https://doi.org/10.1016/j.biocon.2020.108637 (2020).Article 

Google Scholar 
Millennium Ecosystem Assessment. Ecosystems and Human Well-being: Synthesis. (2005).Nagelkerken, I. et al. The habitat function of mangroves for terrestrial and marine fauna: A review. Aquat. Bot. 89, 155–185. https://doi.org/10.1016/j.aquabot.2007.12.007 (2008).Article 

Google Scholar 
Hamilton, S. E. & Casey, D. Creation of a high spatio-temporal resolution global database of continuous mangrove forest cover for the 21st century (CGMFC-21). Glob. Ecol. Biogeogr. 25, 729–738. https://doi.org/10.1111/geb.12449 (2016).Article 

Google Scholar 
Friess, D. A. et al. The state of the world’s Mangrove forests: Past, present, and future. Annu. Rev. Environ. Resour. 44, 89–115. https://doi.org/10.1146/annurev-environ-101718-033302 (2019).Article 

Google Scholar 
Zeng, Y., Friess, D. A., Sarira, T. V., Siman, K. & Koh, L. P. Global potential and limits of mangrove blue carbon for climate change mitigation. Curr. Biol. 31, 1737-1743.e1733. https://doi.org/10.1016/j.cub.2021.01.070 (2021).Article 
CAS 

Google Scholar 
zu Ermgassen, P. S. E. et al. Fishers who rely on mangroves: Modelling and mapping the global intensity of mangrove-associated fisheries. Estuar. Coast. Shelf Sci. 247, 106975. https://doi.org/10.1016/j.ecss.2020.106975 (2020).Article 

Google Scholar 
Walters, A. D. et al. Do hotspots fall within protected areas? A geographic approach to planning analysis of regional freshwater biodiversity. Freshw. Biol. 64, 2046–2056. https://doi.org/10.1111/fwb.13394 (2019).Article 

Google Scholar 
Blasco, F., Saenger, P. & Janodet, E. Mangroves as indicators of coastal change. CATENA 27, 167–178. https://doi.org/10.1016/0341-8162(96)00013-6 (1996).Article 

Google Scholar 
Gilman, E. L., Ellison, J., Duke, N. C. & Field, C. Threats to mangroves from climate change and adaptation options: A review. Aquat. Bot. 89, 237–250. https://doi.org/10.1016/j.aquabot.2007.12.009 (2008).Article 

Google Scholar 
Hamilton, S. Assessing the role of commercial aquaculture in displacing mangrove forest. Bull. Mar. Sci. 89, 585–601 (2013).Article 

Google Scholar 
Lovelock, C. E. et al. The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526, 559–563. https://doi.org/10.1038/nature15538 (2015).Article 
ADS 
CAS 

Google Scholar 
Richards Daniel, R. & Friess Daniel, A. Rates and drivers of mangrove deforestation in Southeast Asia, 2000–2012. Proc. Natl. Acad. Sci. 113, 344–349. https://doi.org/10.1073/pnas.1510272113 (2016).Article 
ADS 
CAS 

Google Scholar 
Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202. https://doi.org/10.1016/j.cub.2012.09.036 (2012).Article 
CAS 

Google Scholar 
Ward, R. D., Friess, D. A., Day, R. H. & MacKenzie, R. A. Impacts of climate change on mangrove ecosystems: A region by region overview. Ecosyst. Health Sustain. 2, e01211. https://doi.org/10.1002/ehs2.1211 (2016).Article 

Google Scholar 
Van der Stocken, T., Vanschoenwinkel, B., Carroll, D., Cavanaugh, K. C. & Koedam, N. Mangrove dispersal disrupted by projected changes in global seawater density. Nat. Clim. Chang. 12, 685–691. https://doi.org/10.1038/s41558-022-01391-9 (2022).Article 
ADS 

Google Scholar 
Alongi, D. M. The impact of climate change on Mangrove forests. Curr. Clim. Change Rep. 1, 30–39. https://doi.org/10.1007/s40641-015-0002-x (2015).Article 

Google Scholar 
Giri, C. et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob. Ecol. Biogeogr. 20, 154–159. https://doi.org/10.1111/j.1466-8238.2010.00584.x (2011).Article 

Google Scholar 
Kristensen, E. Mangrove crabs as ecosystem engineers; with emphasis on sediment processes. J. Sea Res. 59, 30–43. https://doi.org/10.1016/j.seares.2007.05.004 (2008).Article 
ADS 

Google Scholar 
Penha-Lopes, G. et al. Are fiddler crabs potentially useful ecosystem engineers in mangrove wastewater wetlands?. Mar. Pollut. Bull. 58, 1694–1703. https://doi.org/10.1016/j.marpolbul.2009.06.015 (2009).Article 
CAS 

Google Scholar 
Sharifian, S., Kamrani, E. & Saeedi, H. Global biodiversity and biogeography of mangrove crabs: Temperature, the key driver of latitudinal gradients of species richness. J. Therm. Biol 92, 102692. https://doi.org/10.1016/j.jtherbio.2020.102692 (2020).Article 
CAS 

Google Scholar 
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar 
Guisan, A. & Zimmermann, N. E. Predictive habitat distribution models in ecology. Ecol. Model. 135, 147–186. https://doi.org/10.1016/S0304-3800(00)00354-9 (2000).Article 

Google Scholar 
Guisan, A. et al. Predicting species distributions for conservation decisions. Ecol. Lett. 16, 1424–1435. https://doi.org/10.1111/ele.12189 (2013).Article 

Google Scholar 
Guisan, A., Thuiller, W. & Zimmermann, N. E. Habitat Suitability and Distribution Models: With Applications in R. (Cambridge University Press, 2017).Luan, J., Zhang, C., Xu, B., Xue, Y. & Ren, Y. Modelling the spatial distribution of three Portunidae crabs in Haizhou Bay, China. PLoS ONE 13, e0207457. https://doi.org/10.1371/journal.pone.0207457 (2018).Article 
CAS 

Google Scholar 
Kafash, A. et al. The Gray Toad-headed Agama, Phrynocephalus scutellatus, on the Iranian Plateau: The degree of niche overlap depends on the phylogenetic distance. Zool. Middle East 64, 47–54. https://doi.org/10.1080/09397140.2017.1401309 (2018).Article 

Google Scholar 
Yousefi, M., Shabani, A. A. & Azarnivand, H. Reconstructing distribution of the Eastern Rock Nuthatch during the Last Glacial Maximum and Last Interglacial. Avian Biol. Res. 13, 3–9. https://doi.org/10.1177/1758155919874537 (2019).Article 

Google Scholar 
De Rock, P. et al. Predicting large-scale habitat suitability for cetaceans off Namibia using MinxEnt. Mar. Ecol. Prog. Ser. 619, 149–167 (2019).Article 
ADS 

Google Scholar 
Saeedi, H., Basher, Z. & Costello, M. J. Modelling present and future global distributions of razor clams (Bivalvia: Solenidae). Helgol. Mar. Res. 70, 23. https://doi.org/10.1186/s10152-016-0477-4 (2016).Article 

Google Scholar 
Bosso, L. et al. The rise and fall of an alien: why the successful colonizer Littorina saxatilis failed to invade the Mediterranean Sea. Biol. Invas. 24, 3169–3187. https://doi.org/10.1007/s10530-022-02838-y (2022).Article 

Google Scholar 
Moradmand, M. & Yousefi, M. Ecological niche modelling and climate change in two species groups of huntsman spider genus Eusparassus in the Western Palearctic. Sci. Rep. 12, 4138. https://doi.org/10.1038/s41598-022-08145-9 (2022).Article 
ADS 
CAS 

Google Scholar 
Compton, T. J., Leathwick, J. R. & Inglis, G. J. Thermogeography predicts the potential global range of the invasive European green crab (Carcinus maenas). Divers. Distrib. 16, 243–255. https://doi.org/10.1111/j.1472-4642.2010.00644.x (2010).Article 

Google Scholar 
Kafash, A., Ashrafi, S. & Yousefi, M. Modeling habitat suitability of bats to identify high priority areas for field monitoring and conservation. Environ. Sci. Pollut. Res. 29, 25881–25891. https://doi.org/10.1007/s11356-021-17412-7 (2022).Article 

Google Scholar 
Leathwick, J. et al. Novel methods for the design and evaluation of marine protected areas in offshore waters. Conserv. Lett. 1, 91–102. https://doi.org/10.1111/j.1755-263X.2008.00012.x (2008).Article 

Google Scholar 
Charrua, A. B., Bandeira, S. O., Catarino, S., Cabral, P. & Romeiras, M. M. Assessment of the vulnerability of coastal mangrove ecosystems in Mozambique. Ocean Coast. Manag. 189, 105145. https://doi.org/10.1016/j.ocecoaman.2020.105145 (2020).Article 

Google Scholar 
Khajoei Nasab, F., Mehrabian, A. & Mostafavi, H. Mapping the current and future distributions of Onosma species endemic to Iran. J. Arid Land 12, 1031–1045. https://doi.org/10.1007/s40333-020-0080-z (2020).Article 

Google Scholar 
Allyn, A. J. et al. Comparing and synthesizing quantitative distribution models and qualitative vulnerability assessments to project marine species distributions under climate change. PLoS ONE 15, e0231595. https://doi.org/10.1371/journal.pone.0231595 (2020).Article 
CAS 

Google Scholar 
Makki, T., Mostafavi, H., Matkan, A. & Aghighi, H. Modelling Climate-Change Impact on the Spatial Distribution of Garra Rufa (Heckel, 1843) (Teleostei: Cyprinidae). Iran. J. Sci. Technol. Trans. A: Sci. 45, 795–804. https://doi.org/10.1007/s40995-021-01088-2 (2021).Article 

Google Scholar 
Bolon, I. et al. What is the impact of snakebite envenoming on domestic animals? A nation-wide community-based study in Nepal and Cameroon. Toxicon: X 9–10, 100068. https://doi.org/10.1016/j.toxcx.2021.100068 (2021).Sharma, A., Dubey, V. K., Johnson, J. A., Rawal, Y. K. & Sivakumar, K. Is there always space at the top? Ensemble modeling reveals climate-driven high-altitude squeeze for the vulnerable snow trout Schizothorax richardsonii in Himalaya. Ecol. Ind. 120, 106900. https://doi.org/10.1016/j.ecolind.2020.106900 (2021).Article 

Google Scholar 
Yousefi, M., Naderloo, R. & Keikhosravi, A. Freshwater crabs of the Near East: Increased extinction risk from climate change and underrepresented within protected areas. Glob. Ecol. Conserv. 38, e02266. https://doi.org/10.1016/j.gecco.2022.e02266 (2022).Article 

Google Scholar 
Sheykhi Ilanloo, S. et al. Applying opportunistic observations to model current and future suitability of the Kopet Dagh Mountains for a Near Threatened avian scavenger. Avian Biol. Res. 14, 18–26. https://doi.org/10.1177/1758155920962750 (2020).Article 

Google Scholar 
Naderloo, R. Grapsoid crabs (Decapoda: Brachyura: Thoracotremata) of the Persian Gulf and the Gulf of Oman. Zootaxa 3048(1), 1. https://doi.org/10.11646/zootaxa.3048.1.1 (2011).Article 

Google Scholar 
Naderloo, R. Atlas of crabs of the Persian Gulf. (2017).Innocenti, G., Schubart, C. D. & Fratini, S. Description of Metopograpsus cannicci, new species, a pseudocryptic crab species from East Africa and the Western Indian Ocean (Decapoda: Brachyura: Grapsidae). Raffles Bull. Zool. (RBZ) 68, 619–628 (2020).
Google Scholar 
Hemmati, M. R., Shojaei, M. G., Taheri Mirghaed, A., Mashhadi Farahani, M. & Weigt, M. Food sources for camptandriid crabs in an arid mangrove ecosystem of the Persian Gulf: a stable isotope approach. Isotop. Environ. Health Stud. 57, 457–469. https://doi.org/10.1080/10256016.2021.1925665 (2021).Article 
CAS 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101. https://doi.org/10.1038/nature09329 (2010).Article 
ADS 
CAS 

Google Scholar 
Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226. https://doi.org/10.1016/j.jembe.2011.02.029 (2011).Article 

Google Scholar 
Hall, S. & Thatje, S. Temperature-driven biogeography of the deep-sea family Lithodidae (Crustacea: Decapoda: Anomura) in the Southern Ocean. Polar Biol. 34, 363–370. https://doi.org/10.1007/s00300-010-0890-0 (2011).Article 

Google Scholar 
Hannah, L. Climate Change Biology. Academic Press (2015).Ali, H. et al. Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS ONE 16, e0260031. https://doi.org/10.1371/journal.pone.0260031 (2022).Article 
CAS 

Google Scholar 
Yousefi, M. et al. Climate change is a major problem for biodiversity conservation: A systematic review of recent studies in Iran. Contemp. Probl. Ecol. 12, 394–403. https://doi.org/10.1134/S1995425519040127 (2019).Article 

Google Scholar 
Doney, S. C. et al. Climate Change Impacts on Marine Ecosystems. Ann. Rev. Mar. Sci. 4, 11–37. https://doi.org/10.1146/annurev-marine-041911-111611 (2011).Article 

Google Scholar 
Worm, B. & Lotze, H. K. in Climate Change (Second Edition) (ed Trevor M. Letcher) 195–212 (Elsevier, 2016).Ramírez, F., Afán, I., Davis, L. S. & Chiaradia, A. Climate impacts on global hot spots of marine biodiversity. Sci. Adv. 3, e1601198. https://doi.org/10.1126/sciadv.1601198 (2017).Article 
ADS 

Google Scholar 
Worm, B. et al. Impacts of Biodiversity Loss on Ocean Ecosystem Services. Science 314, 787–790. https://doi.org/10.1126/science.1132294 (2006).Article 
ADS 
CAS 

Google Scholar 
Lester, S. E. et al. Biological effects within no-take marine reserves: a global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009).Article 
ADS 

Google Scholar 
Daru, B. H. & le Roux, P. C. Marine protected areas are insufficient to conserve global marine plant diversity. Glob. Ecol. Biogeogr. 25, 324–334. https://doi.org/10.1111/geb.12412 (2016).Article 

Google Scholar 
Sala, E. et al. Protecting the global ocean for biodiversity, food and climate. Nature https://doi.org/10.1038/s41586-021-03371-z (2021).Article 

Google Scholar 
Embling, C. B. et al. Using habitat models to identify suitable sites for marine protected areas for harbour porpoises (Phocoena phocoena). Biol. Cons. 143, 267–279. https://doi.org/10.1016/j.biocon.2009.09.005 (2010).Article 

Google Scholar 
Magris, R. A. & Déstro, G. F. G. Predictive modeling of suitable habitats for threatened marine invertebrates and implications for conservation assessment in Brazil. Braz. J. Oceanogr. 58, 57–68 (2010).Article 

Google Scholar 
Welch, H., Pressey, R. L. & Reside, A. E. Using temporally explicit habitat suitability models to assess threats to mobile species and evaluate the effectiveness of marine protected areas. J. Nat. Conserv. 41, 106–115. https://doi.org/10.1016/j.jnc.2017.12.003 (2018).Article 

Google Scholar 
Rhoden, C. M., Peterman, W. E. & Taylor, C. A. Maxent-directed field surveys identify new populations of narrowly endemic habitat specialists. PeerJ 5, e3632–e3632. https://doi.org/10.7717/peerj.3632 (2017).Article 

Google Scholar 
Ancillotto, L., Mori, E., Bosso, L., Agnelli, P. & Russo, D. The Balkan long-eared bat (Plecotus kolombatovici) occurs in Italy—First confirmed record and potential distribution. Mamm. Biol. 96, 61–67. https://doi.org/10.1016/j.mambio.2019.03.014 (2019).
Article 

Google Scholar 
Imtiyaz, B. B., Sweta, P. D., Prakash, K. K. Threats to marine biodiversity. Mar. Biodivers.: Present Status Prospects (2011).Robinson, N. M., Nelson, W. A., Costello, M. J., Sutherland, J. E. & Lundquist, C. J. A systematic review of marine-based species distribution models (SDMs) with recommendations for best practice. Front. Mar. Sci. 4, 421 (2017).Article 

Google Scholar 
Fabri-Ruiz, S., Danis, B., David, B. & Saucède, T. Can we generate robust species distribution models at the scale of the Southern Ocean?. Divers. Distrib. 25, 21–37. https://doi.org/10.1111/ddi.12835 (2019).Article 

Google Scholar 
Maxwell, D. L., Stelzenmüller, V., Eastwood, P. D. & Rogers, S. I. Modelling the spatial distribution of plaice (Pleuronectes platessa), sole (Solea solea) and thornback ray (Raja clavata) in UK waters for marine management and planning. J. Sea Res. 61, 258–267. https://doi.org/10.1016/j.seares.2008.11.008 (2009).Article 
ADS 

Google Scholar 
Marshall, C. E., Glegg, G. A. & Howell, K. L. Species distribution modelling to support marine conservation planning: The next steps. Mar. Policy 45, 330–332. https://doi.org/10.1016/j.marpol.2013.09.003 (2014).Article 

Google Scholar 
GBIF. GBIF Occurrence Download https://doi.org/10.15468/dl.khpu28. GBIF (2021).Spalding, M. D. et al. Marine ecoregions of the world: A bioregionalization of coastal and shelf areas. Bioscience 57, 573–583. https://doi.org/10.1641/B570707 (2007).Article 

Google Scholar 
Basher, Z., Bowden, D. A. & Costello, M. J. Global marine environment datasets (GMED). World Wide Web Electron. Publ. 14, 1 (2018).
Google Scholar 
Barnes, D. Ecology of subtropical hermit crabs in SW Madagascar: short-range migrations. Mar. Biol. 142, 549–557. https://doi.org/10.1007/s00227-002-0968-5 (2003).Article 

Google Scholar 
Naimullah, M. et al. Association of environmental factors in the Taiwan Strait with distributions and habitat characteristics of three swimming crabs. Remote Sens. 12, 1. https://doi.org/10.3390/rs12142231 (2020).Article 

Google Scholar 
Malvé, M. E., Rivadeneira, M. M. & Gordillo, S. Northward range expansion of the European green crab Carcinus maenas in the SW Atlantic: a synthesis after ~20 years of invasion history. bioRxiv, 2020.2011.2004.368761, doi:https://doi.org/10.1101/2020.11.04.368761 (2020).Merow, C., Smith, M. J. & Silander, J. A. Jr. A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x (2013).Article 

Google Scholar 
Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. https://doi.org/10.1111/ecog.01881 (2016).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing (2020).Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49. https://doi.org/10.1017/S0376892997000088 (1997).Article 

Google Scholar 
Swets John, A. Measuring the Accuracy of Diagnostic Systems. Science 240, 1285–1293. https://doi.org/10.1126/science.3287615 (1988).Article 
ADS 
MathSciNet 
MATH 

Google Scholar 
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.3–7 (2020).UNEP-WCMC and IUCN. Protected Planet: The World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM). UNEP-WCMC and IUCN (2021). More

Data sourcesThis study builds on two datasets; 666 livestock anthrax outbreaks collected over 60 years (1957–2017) by the Kenya Directorate of Veterinary Services (KDVS), and 13 reported anthrax outbreaks we investigated between 2017 and 201811,13. These datasets were combined with data from targeted active anthrax surveillance we conducted in 2019–2020 (see below) to define anthrax suitable areas in Kenya, including hotspots, and subsequently assessed effectiveness of livestock vaccination as a control strategy.Targeted active surveillance-collected anthrax data, 2019–2020Active anthrax surveillance was conducted for 12 months between 2019 and 2020 in randomly selected areas to ensure representation of all AEZs of the country. AEZs are land units defined based on the patterns of soil, landforms and climatic characteristics. Kenya has seven AEZs that include agro-alpine, high potential, medium potential, semi-arid, arid, very-arid and desert. In 2013, Kenya devolved governance into 47 semi-autonomous counties that are subdivided into 290 subcounties which are in turn divided into 1450 administrative wards, the smallest administrative units in the country. Using a geographic map that condensed Kenya into five AEZs; agro-alpine, high potential, medium potential, semi-arid, and arid/very arid zones, we randomly selected 4 administrative sub-counties from each AEZ (N = 20). To increase geographic spread of the study and enhance detection of anthrax outbreaks, we surveilled the larger administrative county (consisting of 20 to 45 administrative wards) where the randomly selected sub-counties were located. As shown in Fig. S1, we ultimately carried out the active anthrax surveillance in 18 counties, containing 523 administrative wards, the latter being used for measuring spatial association (see below).We conducted the surveillance between April 2019 and June 2020, through 523 animal health practitioners (AHPs), one in each ward, after intensive training to identify anthrax using a standard case definition, and to collect and electronically transmit the data weekly using telephone-based short messaging system (SMS) to a central server hosted by KDVS. Regarding case definition, any livestock death classified as anthrax through clinical or laboratory diagnosis was considered an anthrax event. Using standard guidelines issued by the KDVS, a clinical diagnosis was made by the AHPs across the country as an acute cattle, sheep or goat disease characterized by sudden death with or without bleeding from natural orifices, accompanied by absence of rigor mortis. Further, if the carcass was accidentally opened, failure of blood to clot and/or the presence of splenomegaly were included. In pigs, symptoms included swelling of the face and neck with oedema. A laboratory confirmed anthrax was diagnosed using Gram and methylene blue stains followed by identification of the capsule and typical rod-shaped B. anthracis in clinical specimens that the AHPs submitted to the central or regional veterinary investigation laboratories in Kenya. One case of anthrax in either species was considered an outbreak.During the surveillance, the programmed server sent prompting texts directly to the AHPs’ cell phones every Friday of each week for the 52 weeks. The AHPs interacted with the platform by responding to prompting questions sent via SMS to their telephones. Data were securely stored in an online encrypted platform which was subsequently downloaded into Ms Excel for analysis. This surveillance detected 119 anthrax outbreaks, whose partial data were used to model effects of climate change on future anthrax distribution in Kenya14. Here, we integrated these active surveillance data with other datasets to conduct detailed ENM and kernel-smoothed density mapping with a goal of refining suitable anthrax areas including crystalizing hotspots in the country.Anthrax outbreak incidence per livestock population by countyWe knew the total number of livestock per county and wards by species for the active surveillance period. The counties represented the level of disease management including vaccine distribution while the wards within counties represented the modeling unit for targeting control. Therefore, we estimated the outbreak incidence as the total number of outbreaks per livestock species per 100,000 head of that species.Ecological niche modeling and validationWe used boosted regression tree (BRT) algorithm as previously published13. In those studies, we estimated the geographic distribution of anthrax in southern Kenya using 69 spatially unique outbreak points (thinned from the 86 outbreaks in the records) and 18 environmental variables resampled to 250 m resolution. In this study, the final experiments were run with a learning rate (lr) = 0.001, bagging fraction (br) = 5, and maximum tree = 2500. We then mapped anthrax suitability as the mean output of the 100 experiments and the lower 2.5% and upper 97.5% mapped as confidence intervals. We determined variable contribution and derived partial dependence as previously described13. As BRTs are a random walk and each experiment randomly resamples training and test data, it was necessary to repeat those outputs along with the map predictions.Here, our goal was to evaluate the BRT models built with records data from 2011 to 2017 data and use the predict function to calculate model accuracy metrics using the 2017–2020 outbreaks as presence points and the sub-counties reporting zero outbreaks during the 2019–2020 active surveillance period as absence points. The model of southern Kenya was projected onto all of Kenya using climate variables clipped to the whole of Kenya. We tested the BRT models in two ways; first, evaluating 2011–2017 data models with holdout data using a random resampling and multi-modeling approach. Here, we report the area under curve (AUC) for each of the original training/testing split into the 69 historical points and the 2017–2020 data serving as independent data, the latter representing true model validation. Second, to determine the total percentage of surveillance data predicted and map areas of anthrax suitability to compare with kernel density estimates (see below), we produced a dichotomized map using the Youden index cutoff17 following Otieno et al.14.Outbreak concentrations from kernel density estimation (KDE)To describe the spatial concentration of reported outbreaks, we calculated descriptive spatial statistics, including the spatial mean, standard distance, and standard deviational ellipse of outbreak locations from the prospective surveillance dataset following Blackburn et al.18 These spatial statistics help to differentiate the geographic focus (spatial mean) and dispersion of outbreak reports from year to year and across the sampling period. We then conducted kernel density estimation (KDE) to visualize the concentration of anthrax outbreaks per square kilometer per year and across the study period18. We used the spatstat package for all KDE analyses using the quadratic kernel function19:$$fleft( x right) = frac{1}{{nh^{2} }} mathop sum limits_{i = 1}^{n} Kleft( {frac{{x – X_{i} }}{h}} right)$$where h is the bandwidth, x-Xi is the distance to each anthrax outbreak i. Finally, K is the quadratic kernel function, defined as:$$Kleft( x right) = frac{3}{4}left( {1 – x^{2} } right), left| x right| le 1$$$$Kleft( x right) = 0,x > 1$$This function was employed to estimate anthrax outbreak concentration across space using each outbreak weighted as one. We calculated the bandwidth (kernel) using hopt that uses the sample size (number of outbreaks) and the standard distance to estimate bandwidth. Finally, we estimated bandwidth for each year and then averaged them to apply the same fixed bandwidth for each year under study in Q-GIS version 3.1.8. The resulting outputs were map surfaces representing the spatial concentrations of outbreaks across the country per 1 km2 for each study year and all study years combined. For this study, we used the cutoff criteria of Nelson and Boots19 to identify outbreak hotspots as areas with density values in the upper 25%, 10%, and 5% of outbreak concentrations. The analyses identified these areas by year (2017–2020) and for all surveillance years combined.Local spatial clustering at the ward levelAnthrax outbreak incidence per livestock speciesThe ENM and KDE-derived maps provide a first estimate of potential risk and outbreak concentration, respectively. We were also interested in estimating anthrax outbreak intensity relative to livestock populations at a local level. For the active surveillance period, we knew the total number of outbreaks per ward (the smallest administrative spatial unit) by livestock species. For this two-year period, we estimated the ward-level outbreak incidence as the total number of outbreaks per livestock species per 10,000 head of that species. To estimate livestock population per ward, we extracted the values in the raster file of the areal weighted gridded livestock of the world data using the zonal statistic routine in Q-GIS version 3.1.8, into the polygon consisting of all pixels per ward as the total population19,20. We calculated outbreak incidence as the number of outbreaks per ward cattle population per 10,000 cattle for each administrative ward. We limited this analysis to those 18 counties participating in the active surveillance study (Fig. S1), as we could appropriately assume any ward with no reports was a ‘true zero’ for the estimation. Given that most reported outbreaks were in domestic cattle (see results below), we here report those results involving cattle alone. Given the overall high number of wards and the high number of wards without outbreaks, we performed the empirical Bayes smoothing and spatial Bayes smoothing routines in GeoDa version 1.12.1.161 to reduce the variance in anthrax incidence estimates20,21. To evaluate smoothing routine performance, we box plotted rates per ward and selected the method with the greatest reduction in outliers21. Smoothed rates were mapped as choropleth map in Q-GIS version 3.1.8 using the four equal area bins.Spatial cluster analysisWe used Local Moran’s I16 to test for spatial cluster of livestock anthrax in cattle using the smoothed outbreak incidence estimates. The Local Moran’s I statistic tests whether individual wards are part of spatial cluster, like incidence estimates surrounded by similar estimate (high-high or low-low) or spatial outliers where wards with significantly high or low estimates are surrounded by dissimilar values (high-low or low–high). The local Moran’s I is written as16:$$I_{i} = Z_{i} sum W_{ij} Z_{j}$$where Ii is the statistic for a ward i, Zi is the difference between the incidence at i and the mean anthrax incidence rate for all of wards in the study, Zj is the difference between anthrax risk at ward j and the mean for all wards. Wij is the weights matrix. In this study, the 1st order queen contiguity was employed. Here, Wij equals 1/n if a ward shared a boundary or vertex and 0 if not. For this study, Local Moran’s I was performed on the wards using 999 permutations and p = 0.05 using GeoDa version 1.12.1.161.Assessing effectiveness of cattle vaccination in burden hotspotsAs a first estimate of how we might scale up livestock anthrax vaccination efforts in Kenya, we slightly adjusted a simple published anthrax outbreak simulation model in a cattle population. For this study we applied an early mathematical approach of Funiss and Hahn22 to simulate anthrax at the ward level. While other recent models are available23,24, these are difficult to parameterize or require time series data we could not derive with the surveillance approach in this study. Like the more recent models, Funiss and Hahn22 assumed anthrax transmission was driven by cattle accessing spore-contaminated environments. Here the proportion of infected cattle each day depended on the population of susceptible animals in the population and probability of getting infected. This probability depends on environmental contamination (“a”), and a fraction of anthrax carcasses in the environment on a day (“f,”). Each day, the newly infected cattle are transferred to an incubation period vector, “d,” waiting to die following a probability “p”. In this model, all infected animals, “n,” die following the incubation periods given by the vector, “p”, in which pi is the probability of a cow dying i days after the infection. Following death, the cattle are transferred to a carcass state, providing a direct infection source to the susceptible cattle via environmental contamination. Environmental contamination “a,” is therefore defined as the number of spores ingested by an animal in a day. This environmental contamination depends on spores from carcasses and an assumed spore decay rate γ22.The complete set of difference equations with a daily time step is given by:$${text{S}}_{(t + 1)} = {text{S}}_{(t)} – {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)$$$${text{I}}_{(t + 1)} = {text{I}}_{(t)} + {text{ S}}_{(t)} *left( {{1} – {text{e}}^{{ – left( {{text{a}}_{{text{t}}} + gamma {text{f}}_{{{text{t}} + {1}}} } right)}} } right)$$where the expression (left( {{1} – {text{e}}^{{ – left( {{text{a}}_{t} + gamma {text{f}}_{{{text{t}} + 1}} } right)}} } right)) denotes the probability of an animal becoming infected and at + γft+1 is the mean number of spores ingested by a cow in a day. The equation for environmental contamination, a, is given by:$${text{a}}_{t + 1} {-}{text{a}}_{{text{t}}} = alpha {text{a}}_{{text{t}}} + beta {text{c}}_{{{text{t}} + {1}}}$$The newly infected animals die after a certain number of days. The distribution of incubation periods is given by the vector, p. On each day, the new cases are placed in a due-to-die vector, d, and when they die, they are subsequently moved down one step to fresh carcasses, ft. The fresh carcasses provide a direct source of infection to the susceptible cattle via the ‘fresh carcass term’, γ. These carcasses decay or are scavenged or disposed by man. The equation expressing the disseminating carcasses, c, is:$${text{C}}_{t + 1} – {text{c}}_{t} = {text{f}}_{t + 1} – delta {text{c}}_{t}$$The model parameters variables are provided in Table 1 and are similar to those used by Funiss and Hahn22 to generate a standard run. We ran the model for one year and extrapolated to cattle population in the identified hotspot wards.Table 1 Model parameters and variables.Full size table More

Human-modified forests, such as selectively logged forests, are often characterized as degraded ecosystems because of their altered structure and low biomass. The concept of ecosystem degradation can be a double-edged sword. It rightly draws attention to the conservation value of old-growth systems and the importance of ecosystem restoration. However, it can also suggest that human-modified ecosystems are of low ecological value and therefore, in some cases, suitable for conversion to agriculture (such as oil palm plantations) and other land uses3,4,5.Selectively logged and other forms of structurally altered forests are becoming the prevailing vegetation cover in much of the tropical forest biome2. Such disturbance frequently leads to a decline in old-growth specialist species1, and also in non-specialist species in some contexts6,7,8. However, species-focused biodiversity metrics are only one measure of ecosystem vitality and functionality, and rarely consider the collective role that suites of species play in maintaining ecological functions9.An alternative approach is to focus on the energetics of key taxonomic groups, and the number and relative dominance of species contributing to each energetic pathway. Energetic approaches to examining ecosystem structure and function have a long history in ecosystem ecology10. Virtually all ecosystems are powered by a cascade of captured sunlight through an array of autotroph tissues and into hierarchical assemblages of herbivores, carnivores and detritivores. Energetic approaches shine light on the relative significance of energy flows among key taxa and provide insight into the processes that shape biodiversity and ecosystem function. The common currency of energy enables diverse guilds and taxa to be compared in a unified and physically meaningful manner: dominant energetic pathways can be identified, and the resilience of each pathway to the loss of individual species can be assessed. Quantitative links can then be made between animal communities and the plant-based ecosystem productivity on which they depend. The magnitude of energetic pathways in particular animal groups can often be indicators of key associated ecosystem processes, such as nutrient cycling, seed dispersal and pollination, or trophic factors such as intensity of predation pressure or availability of resource supply, all unified under the common metric of energy flux11,12.Energetics approaches have rarely been applied in biodiverse tropical ecosystems because of the range of observations they require11,12,13. Such analyses rely on: population density estimates for a very large number of species; understanding of the diet and feeding behaviour of the species; and reliable estimation of net primary productivity (NPP). Here we take advantage of uniquely rich datasets to apply an energetics lens to examine and quantify aspects of the ecological function and vitality of habitats in Sabah, Malaysia, that comprise old-growth forests, logged forest and oil palm plantation (Fig. 1 and Extended Data Fig. 1). Our approach is to calculate the short-term equilibrium production or consumption rates of food energy by specific species, guilds or taxonomic groups. We focus on three taxonomic groups (plants, birds and mammals) that are frequently used indicators of biodiversity and are relatively well understood ecologically.Fig. 1: Maps of the study sites in Sabah, Borneo.a–d, Maps showing locations of NPP plots and biodiversity surveys in old-growth forest, logged forest and oil palm plantations in the Stability of Altered Forest Ecosystems Project landscape (a), Maliau Basin (b), Danum Valley (c) and Sepilok (d). The inset in a shows the location of the four sites in Sabah. The shade of green indicates old-growth (dark green), twice-logged (intermediate green) or heavily logged (light green) forests. The camera and trap grid includes cameras and small mammal traps. White areas indicate oil palm plantations.Full size imageWe are interested in the fraction of primary productivity consumed by birds and mammals, and how it varies along the disturbance gradient, and how and why various food energetic pathways in mammals and birds, and the diversity of species contributing to those pathways, vary along the disturbance gradient. To estimate the density of 104 mammal and 144 bird species in each of the three habitat types, we aggregated data from 882 camera sampling locations (a total of 42,877 camera trap nights), 508 bird point count locations, 1,488 small terrestrial mammal trap locations (34,058 live-trap nights) and 336 bat trap locations (Fig. 1 and Extended Data Fig. 1). We then calculated daily energetic expenditure for each species based on their body mass, assigned each species to a dietary group and calculated total food consumption in energy units. For primary productivity, we relied on 34 plot-years (summation of plots multiplied by the number of years each plot is monitored) of measurements of the key components of NPP (canopy litterfall, woody growth, fine root production) using the protocols of the Global Ecosystem Monitoring Network14,15,16 across old-growth (n = 4), logged (n = 5) and oil palm (n = 1) plots. This dataset encompasses more than 14,000 measurements of litterfall, 20,000 tree diameter measurements and 2,700 fine root samples.Overall bird species diversity is maintained across the disturbance gradient and peaks in the logged forest; for mammals, there is also a slight increase in the logged forest, followed by rapid decline in the oil palm (Fig. 2b,c). Strikingly, both bird and mammal biomass increases substantially (144% and 231%, respectively) in the logged forest compared to the old-growth forest, with mammals contributing about 75% of total (bird plus mammal) biomass in both habitat types (Fig. 2b,c).Fig. 2: Variation of ecosystem energetics along the disturbance gradient from old-growth forest through logged forest to oil palm.a, Total NPP along the gradient (mean of intensive 1-ha plots; n = 4 for old growth (OG), n = 5 for logged and n = 1 for oil palm (OP); error bars are 95% confidence intervals derived from propagated uncertainty in the individually measured NPP components), with individual plot data points overlaid. b,c, Total body mass (bars, left axis) and number of species counted (blue dots and line, right axis) of birds (b) and mammals (c). d,e, Total direct energetic food intake by birds (d) and mammals (e). f,g, Percentage of NPP directly consumed by birds (f) and mammals (g). In b–e, body mass and energetics were estimated for individual bird and mammal species, with the bars showing the sum. Error bars denote 95% confidence intervals derived from 10,000 Monte Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of the different food types, composition of the diet of each species and NPP. In f,g, the grey bars indicate direct consumption of NPP, white bars denote the percentage of NPP indirectly supporting bird and mammal food intake when the mean trophic level of consumed invertebrates is assumed to be 2.5, with the error bars denoting assumed mean trophic levels of 2.4 and 2.6. Note the log scale of the y axis in f,g. Numbers for d,e provided in Supplementary Data Tables 1, 2.Full size imageThe total flow of energy through consumption is amplified across all energetic pathways by a factor of 2.5 (2.2–3.0; all ranges reported are 95% confidence intervals) in logged forest relative to old-growth forest. In all three habitat types, total energy intake by birds is much greater than by mammals (Fig. 2d,e and Extended Data Table 1). Birds account for 67%, 68% and 90% of the total direct consumption by birds and mammals combined in old-growth forests, logged forests and oil palm, respectively. Although mammal biomass is higher than bird biomass in the old-growth and logged forests, the metabolism per unit mass is much higher in birds because of their small body size; hence, in terms of the energetics and consumption rates, the bird community dominates. The total energy intake by birds alone increases by a factor of 2.6 (2.1–3.2) in the logged forest relative to old-growth forest. This is mainly driven by a 2.5-fold (1.7–2.8) increase in foliage-gleaning insectivory (the dominant energetic pathway), and most other feeding guilds also show an even larger increase (Figs. 2d and 3). However, total bird energy intake in the oil palm drops back to levels similar to those in the old-growth forest, with a collapse in multiple guilds. For mammals, there is a similar 2.4-fold (1.9–3.2) increase in total consumption when going from old-growth to logged forest, but this declines sharply in oil palm plantation. Most notable is the 5.7-fold (3.2–10.2) increase in the importance of terrestrial mammal herbivores in the logged relative to old-growth forests. All four individual old-growth forest sites show consistently lower bird and mammal energetics than the logged forests (Extended Data Fig. 5).Fig. 3: Magnitude and species diversity of energetic pathways in old-growth forest, logged forest and oil palm.The size of the circles indicates the magnitude of energy flow, and the colour indicates birds or mammals. S, number of species; E, ESWI, an index of species redundancy and, therefore, resilience (high values indicate high redundancy; see main text). For clarity, guilds with small energetic flows are not shown, but are listed in Supplementary Data 4. Images created by J. Bentley.Full size imageThe fraction of NPP flowing through the bird and mammal communities increases by a factor of 2.1 (1.5–3.0) in logged forest relative to old-growth forest. There is very little increase in NPP in logged relative to old-growth forests (Fig. 2a) because increased NPP in patches of relatively intact logged forest is offset by very low productivity in more structurally degraded areas such as former logging platforms14,15. In oil palm plantations, oil palm fruits account for a large proportion of NPP, although a large fraction of these is harvested and removed from the ecosystem17. As a proportion of NPP, 1.62% (1.35–2.13%) is directly consumed by birds and mammals in the old-growth forest; this rises to 3.36% (2.57–5.07%) in the logged forest but drops to 0.89% (0.57–1.44%) in oil palm (Fig. 2f,g and Extended Data Table 2).If all invertebrates consumed are herbivores or detritivores (that is, at a trophic level of 2.0), and trophic efficiency is 10% (ref. 10), the total amount of NPP supporting the combined bird and mammal food intake would be 9%, 16% and 5% for old-growth forest, logged forest and oil palm, respectively. However, if the mean trophic level of consumed invertebrates is 2.5 (that is, a mix of herbivores and predators), the corresponding proportions would be 27%, 51% and 17% (Fig. 2f,g). As insectivory is the dominant feeding mode for the avian community, these numbers are dominated by bird diets. For birds in the old-growth forests, 0.35% of NPP supports direct herbivory and frugivory, but around 22% of NPP (assumed invertebrate trophic level 2.5) is indirectly required to support insectivory. The equivalent numbers for birds in logged forest are 0.83% and 46%. Hence, birds account for a much larger indirect consumption of NPP. Bird diet studies in old-growth and logged forest in the region suggest that consumed invertebrates have a mean trophic level of 2.5 (ref. 18; K. Sam, personal communication), indicating that the higher-end estimates of indirect NPP consumption (that is, around 50% in logged forests) are plausible.It is interesting to compare such high fractions of NPP to direct estimates of invertebrate herbivory. Scans of tree leaf litter from these forests suggest that just 7.0% of tree canopy leaf area (1–3% of total NPP) is removed by tree leaf herbivory14,16, but such estimates do not include other pathways available to invertebrates, including herbivory of the understorey, aboveground and belowground sap-sucking, leaf-mining, fruit- and wood-feeding, and canopy, litter and ground-layer detritivory. An increase in invertebrate biomass and herbivory in logged forest compared to old-growth forest has previously been reported in fogging studies in this landscape19. Such high levels of consumption of NPP by invertebrates could have implications on ecosystem vegetation biomass production, suggesting, first, that invertebrate herbivory has a substantial influence on recovery from logging and, second, that insectivorous bird densities may exert substantial indirect controls on ecosystem recovery.The distributions of energy flows among feeding guilds are remarkably stable among habitat types (Fig. 3), indicating that the amplified energy flows in the logged forests do not distort the overall trophic structure of vertebrate communities. Overall bird diet energetics are dominated by insectivory, which accounts for a strikingly invariant 66%, 63% and 66% of bird energetic consumption in old-growth forest, logged forest and oil palm, respectively. Foliage-gleaning dominates as a mode of invertebrate consumption in all three habitat types, with frugivory being the second most energetically important feeding mode (26%, 27% and 19%, respectively). Mammal diet is more evenly distributed across feeding guilds, but frugivory (31%, 30%, 30%) and folivory (24%, 38%, 26%) dominate. Small mammal insectivores are probably under-sampled (see Methods) so the contribution of mammal insectivory may be slightly greater than that estimated here. The apparent constancy of relative magnitude of feeding pathways across the intact and disturbed ecosystems is noteworthy and not sensitive to plausible shifts in feeding behaviour between habitat types (see Supplementary Discussion). There is no evidence of a substantial shift in dominant feeding guild: the principal feeding pathways present in the old-growth forest are maintained in the logged forest.When examining change at species level in the logged forests, the largest absolute increases in bird food consumption were in arboreal insectivores and omnivores (Fig. 4a and Extended Data Fig. 2a). In particular, this change was characterized by large increases in the abundance of bulbul species (Pycnonotus spp.). No bird species showed a significant or substantial reduction in overall energy consumption. In the oil palm plantation, total food consumption by birds was less than in logged forests, but similar to that in old-growth forests. However, this was driven by very high abundance of a handful of species, notably a single arboreal omnivore (yellow-vented bulbul Pycnonotus goiavier) and three arboreal insectivores (Mixornis bornensis, Rhipidura javanica, Copsychus saularis), whereas energy flows through most other bird species were greatly reduced (Fig. 4b and Extended Data Fig. 2b).Fig. 4: Changes in energy consumption by species in logged forest and oil palm relative to old-growth forest.a,b, Changes in energy consumption by species in logged forest relative to old-growth forest (a) and in oil palm relative to old-growth forest (b). The 20 species experiencing the largest increase (red) and decrease (blue) in both habitat types are shown. Bird species are shown in a lighter tone and mammal species are shown in a darker tone. The error bars denote 95% confidence intervals, derived from 10,000 Monte Carlo simulation estimates incorporating uncertainty in body mass, population density, the daily energy expenditure equation, assimilation efficiency of the different food types and composition of the diet of each species.Full size imageFor mammals, the increase in consumption in logged forests is dominated by consumption by large terrestrial herbivores increasing by a factor of 5.7 (3.2–10.2), particularly sambar deer (Rusa unicolor) and Asian elephant (Elephas maximus; Fig. 4a and Extended Data Figs. 2b and 3), along with that by small omnivores, predominantly rodents (native spiny rats, non-native black rat; Fig. 4). A few rainforest species show a strong decline (for example, greater mouse-deer Tragulus napu and brown spiny rat Maxomys rajah). In the oil palm, most mammal species collapse (Fig. 4b) and the limited consumption is dominated by a few disturbance-tolerant habitat generalists (for example, red muntjac Muntiacus muntjak, black rat Rattus rattus, civets), albeit these species are at lower densities than observed in old-growth forest (Extended Data Fig. 2).With very few exceptions, the amplified energy flows in logged forest seem to retain the same level of resilience as in old-growth forest. The diversity and dominance of species within any pathway can be a measure of the resilience of that pathway to loss of species. We assessed energetic dominance within individual pathways by defining an energetic Shannon–Wiener index (ESWI) to examine distribution of energy flow across species; low ESWI indicates a pathway with high dependence on a few species and hence potential vulnerability (Fig. 3). The overall ESWI across guilds does not differ between the old-growth and logged forest (t2,34 = −0.363, P = 0.930), but does decline substantially from old-growth forest to oil palm (t2,34 = −3.826, P = 0.0015), and from logged forest to oil palm (t2,34 = −3.639, P = 0.0025; linear mixed-effects models, with habitat type as fixed effect and guild as random effect; for model coefficients see Supplementary Table 3).Hence, for birds, the diversity of species contributing to dominant energetic pathways is maintained in the transition from old-growth to logged forests but declines substantially in oil palm. Mammals generally show lower diversity and ESWI than birds, but six out of ten feeding guilds maintain or increase ESWI in logged forest relative to the old-growth forests but collapse in oil palm (Fig. 3). Terrestrial herbivory is the largest mammal pathway in the logged forest but is dependent on only four species and is probably the most vulnerable of the larger pathways: a few large mammals (especially sambar deer) play a dominant terrestrial herbivory role in the logged forest. In parallel, bearded pigs (Sus barbatus), the only wild suid in Borneo, form an important and functionally unique component of the terrestrial omnivory pathway. These larger animals are particularly sensitive to anthropogenic pressures such as hunting, or associated pathogenic pressures as evidenced by the recent precipitous decline of the bearded pig in Sabah due to an outbreak of Asian swine fever (after our data were collected)20.Vertebrate populations across the tropics are particularly sensitive to hunting pressure21. Our study site has little hunting, but as a sensitivity analysis we explored the energetic consequences of 50% reduction in population density of those species potentially affected by targeted and/or indiscriminate hunting (Extended Data Fig. 4). Targeted hunted species include commercially valuable birds, and gun-hunted mammals (bearded pig, ungulates, banteng and mammals with medicinal value). Indiscriminately hunted species include birds and mammals likely to be trapped with nets and snares. Hunting in the logged forests lowers both bird and mammal energy flows but still leaves them at levels higher than in faunally intact old-growth forests. Such hunting brings bird energetics levels close to (but still above) those of old-growth forests. For mammals, however, even intensively hunted logged forests seem to maintain higher energetic flows than the old-growth forests. Hence, only very heavy hunting is likely to ‘offset’ the amplified energetics in the logged forest.The amplified energetic pathways in our logged forest probably arise as a result of bottom-up trophic factors including increased resource supply, palatability and accessibility. The more open forest structure in logged forest results in more vegetation being near ground level22,23 and hence more accessible to large generalist mammal herbivores, which show the most striking increase of the mammal guilds. The increased prioritization by plants of competition for light and therefore rapid vegetation growth strategies in logged forests results in higher leaf nutrient content and reduced leaf chemical defences against herbivory24,25, along with higher fruiting and flowering rates19 and greater clumping in resource supply9. This increased resource availability and palatability probably supports high invertebrate and vertebrate herbivore densities25. The act of disturbance displaces the ecosystem from a conservative chemically defended state to a more dynamic state with amplified energy and nutrient flow, but not to an extent that causes heavy disruption in animal community composition. Top-down trophic factors might also play a role in amplifying the energy flows in intermediate trophic levels, through mechanisms such as increased protection of ground-dwelling or nesting mammals and birds from aerial predators in the dense vegetation ground layer. This might partially explain the increased abundance of rodents, but there is little evidence of trophic release at this site because of the persisting high density of mammal carnivores26. Overall, the larger number of bottom-up mechanisms and surge in invertebrate consumption suggest that increased resource supply and palatability largely explains the amplification of consumption pathways in the logged forest. An alternative possibility is that the amplified vertebrate energetics do not indicate amplified overall animal energetics but rather a large diversion of energy from unmeasured invertebrate predation pathways (for example, parasitoids); this seems unlikely but warrants further exploration.Oil palm plantations show a large decline in the proportion of NPP consumed by mammals and birds compared to logged forests12. Mammal populations collapse because they are more vulnerable and avoid humans, and there is no suite of mammal generalists that can step in27,28. Birds show a more modest decline, to levels similar to those observed in old-growth forests, as there is a broad suite of generalist species that are able to adapt to and exploit the habitat types across the disturbance gradient, and because their small size and mobility render them less sensitive to human activity29. There is a consistent decline in the oil palm in ESWI for birds and especially for mammals, indicating a substantial increase in ecosystem vulnerability in many pathways.In conclusion, our analysis demonstrates the tremendously dynamic and ecologically vibrant nature of the studied logged forests, even heavily and repeatedly logged forests such as those found across Borneo. It is likely that the patterns, mechanisms and basic ecological energetics we describe are general to most tropical forests; amplification of multiple ecosystem processes after logging has also been reported for logged forests in Kenya9, but similar detailed analyses are needed for a range of tropical forests to elucidate the importance of biogeographic, climatic or other factors. We stress that our findings do not diminish the importance of protecting structurally intact old-growth forests, but rather question the meaning of degradation by shining a new light on the ecological value of logged and other structurally ‘degraded’ forests, reinforcing their significance to the conservation agenda30. We have shown that a wide diversity of species not only persist but thrive in the logged forest environment. Moreover, such ecological vibrancy probably enhances the prospects for ecosystem structural recovery. In terms of faunal intactness, our study landscape is close to a best-case scenario because hunting pressures were low. If logged forests can be protected from heavy defaunation, our analysis demonstrates that they can be vibrant ecosystems, providing many key ecosystem functions at levels much higher than in old-growth forests. Conservation of logged forest landscapes has an essential role to play in the in the protection of global biodiversity and biosphere function. More

Putz, F. E. et al. Conserv. Lett. 5, 296–303 (2012).Article 

Google Scholar 
Blaser, J., Sarre, A., Poore, D. & Johnson, S. Status of Tropical Forest Management 2011. ITTO Tech. Ser. No. 38 (International Tropical Timber Organization, 2011); available at https://go.nature.com/3usq2an
Google Scholar 
Malhi, Y. et al. Nature https://doi.org/10.1038/s41586-022-05523-1 (2022).Article 

Google Scholar 
Zwerts, J. A. et al. Conserv. Sci. Pract. 3, e568 (2021).Article 

Google Scholar 
Wilkie, D. S., Bennett, E. L., Peres, C. A. & Cunningham, A. A. Ann. N. Y. Acad. Sci. 1223, 120–128 (2011).Article 
PubMed 

Google Scholar 
Putz, F. E., Blate, G. M., Redford, K. H., Fimbel, R. & Robinson, J. Conserv. Biol. 15, 7–20 (2001).Article 

Google Scholar  More