Ecology

Börger, L., Dalziel, B. D. & Fryxell, J. M. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecol. Lett. 11, 637–650 (2008).Article 

Google Scholar 
Burt, W. H. Territoriality and home range concepts as applied to mammals. J. Mammal. 24, 346 (1943).Article 

Google Scholar 
Darwin, C. On the Origin of Species by Means of Natural Selection (D. Appleton Co., 1859).Merkle, J., Fortin, D. & Morales, J. M. A memory‐based foraging tactic reveals an adaptive mechanism for restricted space use. Ecol. Lett. 17, 924–931 (2014).Article 
CAS 

Google Scholar 
Bordes, F., Morand, S., Kelt, D. A. & Van Vuren, D. H. Home range and parasite diversity in mammals. Am. Nat. 173, 467–474 (2009).Article 

Google Scholar 
Morales, J. M. et al. Building the bridge between animal movement and population dynamics. Philos. Trans. R. Soc. B: Biol. Sci. 365, 2289–2301 (2010).Article 

Google Scholar 
Lewis, M. A. & Murray, J. D. Modelling territoriality and wolf-deer interactions. Nature 366, 738–740 (1993).Article 

Google Scholar 
Kelt, D. A. & Van Vuren, D. H. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645 (2001).Article 
CAS 

Google Scholar 
Wang, M. & Grimm, V. Home range dynamics and population regulation: an individual-based model of the common shrew Sorex araneus. Ecol. Modell. 205, 397–409 (2007).Article 

Google Scholar 
Moorcroft, P. R., Lewis, M. A. & Crabtree, R. L. Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone. Proc. R. Soc. B: Biol. Sci. 273, 1651–1659 (2006).Article 

Google Scholar 
Powell, R. A. in Research Techniques in Animal Ecology Vol. 65 (eds. Boitani, L. & Fuller, T. K.) 599 (Columbia Univ. Press, 2000).Spencer, W. D. Home ranges and the value of spatial information. J. Mammal. 93, 929–947 (2012).Article 

Google Scholar 
Bracis, C., Gurarie, E., Van Moorter, B. & Goodwin, R. A. Memory effects on movement behavior in animal foraging. PLoS ONE 10, e0136057 (2015).Article 

Google Scholar 
Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).Article 

Google Scholar 
Powell, R. A. & Mitchell, M. S. What is a home range? J. Mammal. 93, 948–958 (2012).Article 

Google Scholar 
Stamps, J. Motor learning and the value of familiar space. Am. Nat. 146, 41–58 (1995).Article 

Google Scholar 
Gautestad, A. O. & Mysterud, I. Spatial memory, habitat auto-facilitation and the emergence of fractal home range patterns. Ecol. Modell. 221, 2741–2750 (2010).Article 

Google Scholar 
Gautestad, A. O. & Mysterud, I. Intrinsic scaling complexity in animal dispersion and abundance. Am. Nat. 165, 44–55 (2005).Article 

Google Scholar 
Merkle, J. A., Potts, J. R. & Fortin, D. Energy benefits and emergent space use patterns of an empirically parameterized model of memory‐based patch selection. Oikos 126, 185–196 (2017).Schlägel, U. E. & Lewis, M. A. Detecting effects of spatial memory and dynamic information on animal movement decisions. Methods Ecol. Evolution 5, 1236–1246 (2014).Article 

Google Scholar 
Van Moorter, B. et al. Memory keeps you at home: a mechanistic model for home range emergence. Oikos 118, 641–652 (2009).Article 

Google Scholar 
Riotte-Lambert, L., Benhamou, S. & Chamaillé-Jammes, S. How memory-based movement leads to nonterritorial spatial segregation. Am. Naturalist 185, E103–E116 (2015).Article 

Google Scholar 
Marchand, P. et al. Combining familiarity and landscape features helps break down the barriers between movements and home ranges in a non‐territorial large herbivore. J. Anim. Ecol. 86, 371–383 (2017).Article 

Google Scholar 
Gautestad, A. O., Loe, L. E. & Mysterud, A. Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models. J. Anim. Ecol. 82, 572–586 (2013).Article 

Google Scholar 
Ranc, N., Cagnacci, F. & Moorcroft, P. R. Memory drives the formation of animal home ranges: evidence from a reintroduction. Ecol. Lett. 25, 716–728 (2022).Article 

Google Scholar 
Ranc, N., Moorcroft, P. R., Ossi, F. & Cagnacci, F. Experimental evidence of memory-based foraging decisions in a large wild mammal. Proc. Natl Acad. Sci. USA 118, e2014856118 (2021).Article 
CAS 

Google Scholar 
Potts, J. R. & Lewis, M. A. A mathematical approach to territorial pattern formation. Am. Math. Monthly 121, 754–770 (2014).Article 

Google Scholar 
Shettleworth, S. J. Cognition, Evolution, and Behavior (Oxford Univ. Press, 2009).van Asselen, M. et al. Brain areas involved in spatial working memory. Neuropsychologia 44, 1185–1194 (2006).Article 

Google Scholar 
Paul, C., Magda, G. & Abel, S. Spatial memory: theoretical basis and comparative review on experimental methods in rodents. Behav. Brain Res. 203, 151–164 (2009).Article 

Google Scholar 
Boratyński, Z. Energetic constraints on mammalian home-range size. Funct. Ecol. 34, 468–474 (2020).Article 

Google Scholar 
Tamburello, N., Côté, I. M. & Dulvy, N. K. Energy and the scaling of animal space use. Am. Naturalist 186, 196–211 (2015).Article 

Google Scholar 
McNab, B. K. Bioenergetics and the determination of home range size. Am. Naturalist 97, 133–140 (1963).Article 

Google Scholar 
McNab, B. K. Food habits, energetics, and the population biology of mammals. Am. Naturalist 116, 106–124 (1980).Article 

Google Scholar 
Fokidis, H. B., Risch, T. S. & Glenn, T. C. Reproductive and resource benefits to large female body size in a mammal with female-biased sexual size dimorphism. Anim. Behav. 73, 479–488 (2007).Article 

Google Scholar 
Saïd, S. et al. What shapes intra-specific variation in home range size? A case study of female roe deer. Oikos 118, 1299–1306 (2009).Article 

Google Scholar 
Schradin, C. et al. Female home range size is regulated by resource distribution and intraspecific competition: a long-term field study. Anim. Behav. 79, 195–203 (2010).Article 

Google Scholar 
Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evolution 1, 1123–1128 (2017).Article 

Google Scholar 
Croston, R., Branch, C., Kozlovsky, D., Dukas, R. & Pravosudov, V. Heritability and the evolution of cognitive traits. Behav. Ecol. 26, 1447–1459 (2015).Article 

Google Scholar 
Ashton, B. J., Ridley, A. R., Edwards, E. K. & Thornton, A. Cognitive performance is linked to group size and affects fitness in Australian magpies. Nature 554, 364–367 (2018).Article 
CAS 

Google Scholar 
Madden, J. R., Langley, E. J. G., Whiteside, M. A., Beardsworth, C. E. & Van Horik, J. O. The quick are the dead: pheasants that are slow to reverse a learned association survive for longer in the wild. Philos. Trans. R. Soc. B. Biol. Sci. https://doi.org/10.1098/rstb.2017.0297 (2018).Sonnenberg, B. R., Branch, C. L., Pitera, A. M., Bridge, E. & Pravosudov, V. V. Natural selection and spatial cognition in wild food-caching mountain chickadees. Curr. Biol. 29, 670–676 (2019).Article 
CAS 

Google Scholar 
Shaw, R. C., MacKinlay, R. D., Clayton, N. S. & Burns, K. C. Memory performance influences male reproductive success in a wild bird. Curr. Biol. 29, 1498–1502.e3 (2019).Article 
CAS 

Google Scholar 
Gehr, B. et al. Stay home, stay safe—site familiarity reduces predation risk in a large herbivore in two contrasting study sites. J. Anim. Ecol. 89, 1329–1339 (2020).Article 

Google Scholar 
Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).Article 
CAS 

Google Scholar 
Willems, E. P. & Hill, R. A. Predator-specific landscapes of fear and resource distribution: effects on spatial range use. Ecology 90, 546–555 (2009).Article 

Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: spatial patterns of risk perception and response. Trends Ecol. Evolution 34, 355–368 (2019).Article 

Google Scholar 
Bose, S. et al. Implications of fidelity and philopatry for the population structure of female black-tailed deer. Behav. Ecol. 28, 983–990 (2017).Article 

Google Scholar 
Forrester, T. D., Casady, D. S. & Wittmer, H. U. Home sweet home: fitness consequences of site familiarity in female black-tailed deer. Behav. Ecol. Sociobiol. 69, 603–612 (2015).Article 

Google Scholar 
Magrath, R. D., Haff, T. M., Fallow, P. M. & Radford, A. N. Eavesdropping on heterospecific alarm calls: from mechanisms to consequences. Biol. Rev. 90, 560–586 (2015).Article 

Google Scholar 
Skelhorn, J. & Rowe, C. Cognition and the evolution of camouflage. Proc. R. Soc. B: Biol. Sci. 283, 20152890 (2016).Article 

Google Scholar 
Dickinson, A. Associative learning and animal cognition. Philos. Trans. R. Soc. B: Biol. Sci. 367, 2733–2742 (2012).Article 

Google Scholar 
Baddeley, A. D. & Lieberman, K. in Exploring Working Memory 206–223 (Routledge, 2017).Olton, D. S. & Samuelson, R. J. Remembrance of places passed: spatial memory in rats. J. Exp. Psychol. Anim. Behav. Process. 2, 97–116 (1976).Article 

Google Scholar 
Lashley, K. S. Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain (Univ. Chicago Press, 1929).O’keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford Univ. Press, 1978).Beardsworth, C. E. et al. Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy? Ecol. Lett. 24, 751–760 (2021).Article 

Google Scholar 
Rowe, C. & Healy, S. D. Measuring variation in cognition. Behav. Ecol. 25, 1287–1292 (2014).Article 

Google Scholar 
Warner, R. E. Use of cover by pheasant broods in east-central Illinois. J. Wildl. Manag. 43, 334 (1979).Article 

Google Scholar 
Toledo, S. et al. Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system. Science 369, 188–193 (2020).Article 
CAS 

Google Scholar 
Weiser, A. W. et al. Characterizing the accuracy of a self-synchronized reverse-GPS wildlife localization system. In Proc. 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 1–12 (IEEE, 2016).Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).Article 
CAS 

Google Scholar 
Beardsworth, C. E. et al. Validating ATLAS: a regional-scale high-throughput tracking system. Methods Ecol. Evolution 13, 1990–2004 (2022).Article 

Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: an r package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evolution 7, 1124–1132 (2016).Article 

Google Scholar 
Clutton‐Brock, T. H. & Harvey, P. H. Primates, brains and ecology. J. Zool. 190, 309–323 (1980).Article 

Google Scholar 
Avgar, T. et al. Space-use behaviour of woodland caribou based on a cognitive movement model. J. Anim. Ecol. 84, 1059–1070 (2015).Article 

Google Scholar 
Laundré, J. W., Hernández, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).Article 

Google Scholar 
Stephens, D. W. & Krebs, J. R. Foraging Theory (Princeton Univ. Press, 2019).Beauchamp, G. Animal Vigilance: Monitoring Predators and Competitors. Animal Vigilance: Monitoring Predators and Competitors (Elsevier, 2015).Langley, E. J. G. et al. Heritability and correlations among learning and inhibitory control traits. Behav. Ecol. 31, 798–806 (2020).Article 

Google Scholar 
Chen, J., Zou, Y., Sun, Y.-H. & Ten Cate, C. Problem-solving males become more attractive to female budgerigars. Science 363, 166–167 (2019).Article 
CAS 

Google Scholar 
Vale, R., Evans, D. A. & Branco, T. Rapid spatial learning controls instinctive defensive behavior in mice. Curr. Biol. 27, 1342–1349 (2017).Article 
CAS 

Google Scholar 
Burt de Perera, T. & Guilford, T. Rapid learning of shelter position in an intertidal fish, the shanny Lipophrys pholis L. J. Fish. Biol. 72, 1386–1392 (2008).Article 

Google Scholar 
Font, E. Rapid learning of a spatial memory task in a lacertid lizard (Podarcis liolepis). Behav. Procs. 169, 103963 (2019).Article 

Google Scholar 
Senar, J. & Pascual, J. Keel and tarsus length may provide a good predictor of avian body size. Ard.-Wageningen 85, 269–274 (1997).
Google Scholar 
Lavielle, M. Detection of multiple changes in a sequence of dependent variables. Stoch. Process. Appl. 83, 79–102 (1999).Article 

Google Scholar 
Calenge, C. The package ‘adehabitat’ for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Millspaugh, J. J. A Manual for Wildlife Radio Tagging Robert E. Kenward. The Auk 118 (Academic Press, 2001).Gupte, P. R. et al. A guide to pre-processing high-throughput animal tracking data. J. Anim. Ecol. 91, 287–307 (2022).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).Grahn, M., Göransson, G. & Von Schantz, T. Territory acquisition and mating success in pheasants, Phasianus colchicus: an experiment. Anim. Behav. 46, 721–730 (1993).Article 

Google Scholar 
Ridley, M. W. & Hill, D. A. Social organization in the pheasant (Phasianus colchicus): harem formation, mate selection and the role of mate guarding. J. Zool. 211, 619–630 (1987).Article 

Google Scholar 
Gompper, M. E. & Gittleman, J. L. Home range scaling: intraspecific and comparative trends. Oecologia 87, 343–348 (1991).Article 

Google Scholar 
Fisher, R. A. in Breakthroughs in Statistics (eds Kotz, S. & Johnson, N. L.) 66–70 (Springer, 1992).Barton, K. MuMIn: Multi-Model Inference (cran.r-project.org, 2022).Nakagawa, S. A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav. Ecol. 15, 1044–1045 (2004).Article 

Google Scholar 
Heathcote, R. Data for ‘Spatial memory predicts home range size and predation risk in pheasants’ nature ecology and evolution. Mendeley Data https://doi.org/10.17632/m89226xg6p.1 (2022). More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Zheng, S., Hu, J., Ma, Z., Lindenmayer, D. & Liu, J. Increases in intraspecific body size variation are common amongst North American mammals and birds between 1880 and 2020. Nat. Ecol. Evol., https://doi.org/10.1038/s41559-022-01967-w (2023). More

Bradshaw, W. E. & Holzapfel, C. M. Evolutionary response to rapid climate change. Science 312, 1477–1478 (2006).Article 
CAS 

Google Scholar 
Sheridan, J. A. & Bickford, D. Shrinking body size as an ecological response to climate change. Nat. Clim. Change 1, 401–406 (2011).Article 

Google Scholar 
Audzijonyte, A. et al. Fish body sizes change with temperature but not all species shrink with warming. Nat. Ecol. Evol. 4, 809–814 (2020).Article 

Google Scholar 
Gardner, J. L., Heinsohn, R. & Joseph, L. Shifting latitudinal clines in avian body size correlate with global warming in Australian passerines. Proc. R. Soc. B 276, 3845–3852 (2009).Article 

Google Scholar 
Bergmann C. Über die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse (Göttinger Studien, 1847).Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
Darimont, C. T. et al. Human predators outpace other agents of trait change in the wild. Proc. Natl Acad. Sci. USA 106, 952–954 (2009).Article 
CAS 

Google Scholar 
van Gils, J. A. et al. Body shrinkage due to Arctic warming reduces red knot fitness in tropical wintering range. Science 352, 819–821 (2016).Article 

Google Scholar 
Ryding, S., Klaassen, M., Tattersall, G. J., Gardner, J. L. & Symonds, M. R. E. Shape-shifting: changing animal morphologies as a response to climatic warming. Trends Ecol. Evol. 36, 1036–1048 (2021).Article 

Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).Article 

Google Scholar 
Enquist, B. J. et al. Scaling from traits to ecosystems: developing a general trait driver theory via integrating trait-based and metabolic scaling theories. Adv. Ecol. Res 52, 249–318 (2015).Article 

Google Scholar 
González-Suárez, M. & Revilla, E. Variability in life-history and ecological traits is a buffer against extinction in mammals. Ecol. Lett. 16, 242–251 (2013).Article 

Google Scholar 
Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).Article 

Google Scholar 
Brady, S. P. et al. Causes of maladaptation. Evol. Appl. 12, 1229–1242 (2019).Article 

Google Scholar 
Scheele, B. C., Foster, C. N., Banks, S. C. & Lindenmayer, D. B. Niche contractions in declining species: mechanisms and consequences. Trends Ecol. Evol. 32, 346–355 (2017).Article 

Google Scholar 
Campbell-Staton, S. C. et al. Ivory poaching and the rapid evolution of tusklessness in African elephants. Science 374, 483–487 (2021).Article 
CAS 

Google Scholar 
Thompson M. J., Capilla-Lasheras P., Dominoni D. M., Réale D. & Charmantier A. Phenotypic variation in urban environments: mechanisms and implications. Trends Ecol. Evol. 37, 171–182 (2022).Starrfelt, J. & Kokko, H. Bet-hedging—a triple trade-off between means, variances and correlations. Biol. Rev. 87, 742–755 (2012).Article 

Google Scholar 
Heino, M., Díaz Pauli, B. & Dieckmann, U. Fisheries-induced evolution. Annu. Rev. Ecol. Evol. Syst. 46, 461–480 (2015).Article 

Google Scholar 
Kindsvater, H. K. & Palkovacs, E. P. Predicting eco-evolutionary impacts of fishing on body size and trophic role of Atlantic cod. Copeia 105, 475–482 (2017).Article 

Google Scholar 
Hantak, M. M., McLean, B. S., Li, D. & Guralnick, R. P. Mammalian body size is determined by interactions between climate, urbanization, and ecological traits. Commun. Biol. 4, 972 (2021).Article 

Google Scholar 
Freckleton, R. P., Harvey, P. H. & Pagel, M. Bergmann’s rule and body size in mammals. Am. Nat. 161, 821–825 (2003).Article 

Google Scholar 
Riddell, E. A., Odom, J. P., Damm, J. D. & Sears, M. W. Plasticity reveals hidden resistance to extinction under climate change in the global hotspot of salamander diversity. Sci. Adv. 4, eaar5471 (2018).Article 

Google Scholar 
Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 2279 (2019).Article 

Google Scholar 
Yang, J. et al. Large underestimation of intraspecific trait variation and its improvements. Front. Plant Sci. 11, 53 (2020).Article 

Google Scholar 
Olsen, E. M. et al. Maturation trends indicative of rapid evolution preceded the collapse of northern cod. Nature 428, 932–935 (2004).Article 
CAS 

Google Scholar 
Antonson, N. D., Rubenstein, D. R., Hauber, M. E. & Botero, C. A. Ecological uncertainty favours the diversification of host use in avian brood parasites. Nat. Commun. 11, 4185 (2020).Article 

Google Scholar 
Rode, K. D., Amstrup, S. C. & Regehr, E. V. Reduced body size and cub recruitment in polar bears associated with sea ice decline. Ecol. Appl. 20, 768–782 (2010).Article 

Google Scholar 
Edeline, E. et al. Harvest-induced disruptive selection increases variance in fitness-related traits. Proc. R. Soc. B 276, 4163–4171 (2009).Article 

Google Scholar 
Hays, G. C. et al. Changes in mean body size in an expanding population of a threatened species. Proc. R Soc. B https://doi.org/10.1098/rspb.2022.0696 (2022).Halfwerk, W. et al. Adaptive changes in sexual signalling in response to urbanization. Nat. Ecol. Evol. 3, 374–380 (2019).Article 

Google Scholar 
Fernández-Chacón, A. et al. Protected areas buffer against harvest selection and rebuild phenotypic complexity. Ecol. Appl. 30, e02108 (2020).Article 

Google Scholar 
Sánchez-Tójar, A., Moran, N. P., O’Dea, R. E., Reinhold, K. & Nakagawa, S. Illustrating the importance of meta-analysing variances alongside means in ecology and evolution. J. Evol. Biol. 33, 1216–1223 (2020).Article 

Google Scholar 
Reed, T. E., Waples, R. S., Schindler, D. E., Hard, J. J. & Kinnison, M. T. Phenotypic plasticity and population viability: the importance of environmental predictability. Proc. R. Soc. B 277, 3391–3400 (2010).Article 

Google Scholar 
Klump, B. C. et al. Innovation and geographic spread of a complex foraging culture in an urban parrot. Science 373, 456–460 (2021).Article 
CAS 

Google Scholar 
Bosse, M. et al. Recent natural selection causes adaptive evolution of an avian polygenic trait. Science 358, 365–368 (2017).Article 
CAS 

Google Scholar 
Singer, M. C. & Parmesan, C. Lethal trap created by adaptive evolutionary response to an exotic resource. Nature 557, 238–241 (2018).Article 
CAS 

Google Scholar 
Usui, R., Sheeran, L. K., Asbury, A. M. & Blackson, M. Impacts of the COVID-19 pandemic on mammals at tourism destinations: a systematic review. Mamm. Rev. 51, 492–507 (2021).Article 

Google Scholar 
Meineke, E. K. & Daru, B. H. Bias assessments to expand research harnessing biological collections. Trends Ecol. Evol. 36, 1071–1082 (2021).Article 

Google Scholar 
The IUCN Red List of Threatened Species. Version 2021-2 (IUCN, accessed November 2021); https://www.iucnredlist.orgBoyd, R. J. et al. ROBITT: a tool for assessing the risk-of-bias in studies of temporal trends in ecology. Methods Ecol. Evol. 13, 1497–1507 (2022).Article 

Google Scholar 
Thornton, P. K., Ericksen, P. J., Herrero, M. & Challinor, A. J. Climate variability and vulnerability to climate change: a review. Glob. Change Biol. 20, 3313–3328 (2014).Article 

Google Scholar 
Botero, C. A., Weissing, F. J., Wright, J. & Rubenstein, D. R. Evolutionary tipping points in the capacity to adapt to environmental change. Proc. Natl Acad. Sci. USA 112, 184–189 (2015).Article 
CAS 

Google Scholar 
Niklas, K. J. The scaling of plant and animal body mass, length, and diameter. Evolution 48, 44–54 (1994).Article 
CAS 

Google Scholar 
Van Valen, L. Morphological variation and width of ecological niche. Am. Nat. 99, 377–390 (1965).Article 

Google Scholar 
Gaillard, J. M. et al. Generation time: a reliable metric to measure life-history variation among mammalian populations. Am. Nat. 166, 119–123 (2005).Article 

Google Scholar 
Postma, E. in Quantitative Genetics in the Wild (eds Charmantier, A. et al.) 16–33 (Oxford Univ. Press, 2014).Jones, K. E. et al. PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648–2648 (2009).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Bates D., Mächler M., Bolker B. & Walker S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Ives, A. R., Dinnage, R., Nell, L. A., Helmus, M. & Li, D. phyr: Model based phylogenetic analysis. R package version 1.1.0 https://CRAN.R-project.org/package=phyr (2020).Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).Article 
CAS 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).Article 
CAS 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Article 

Google Scholar 
Hurlbert, S. H. & Lombardi, C. M. Final collapse of the Neyman–Pearson decision theoretic framework and rise of the neoFisherian. Ann. Zool. Fenn. 46, 311–349 (2009).Article 

Google Scholar  More

Akçakaya, H. R. et al. Quantifying species recovery and conservation success to develop an IUCN Green List of Species. Conserv. Biol. 32, 1128–1138. https://doi.org/10.1111/cobi.13112 (2018).Article 

Google Scholar 
IUCN. The IUCN Red List of Threatened Species. Version 2021-3 (2022).Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341. https://doi.org/10.1016/j.tree.2018.12.012 (2019).Article 

Google Scholar 
Mohan, M. et al. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forestshttps://doi.org/10.3390/f8090340 (2017).Article 

Google Scholar 
Dronova, I., Kislik, C., Dinh, Z. & Kelly, M. A review of unoccupied aerial vehicle use in wetland applications: Emerging opportunities in approach, technology, and data. Droneshttps://doi.org/10.3390/drones5020045 (2021).Article 

Google Scholar 
Farinha, A. & Lima, P. U. A novel underactuated hand suitable for human-oriented domestic environments. In: Proceedings – 2016 International Conference on Autonomous Robot Systems and Competitions, ICARSC 2016 106–111, https://doi.org/10.1109/ICARSC.2016.21 (2016).Hamaza, S., Georgilas, I., Heredia, G., Ollero, A. & Richardson, T. Design, modeling, and control of an aerial manipulator for placement and retrieval of sensors in the environment. J. Field Robotics 37, 1224–1245. https://doi.org/10.1002/rob.21963 (2020).Article 

Google Scholar 
Nakamura, A. et al. Forests and their canopies: Achievements and horizons in canopy science. Trends Ecol. Evol. 32, 438–451. https://doi.org/10.1016/j.tree.2017.02.020 (2017).Article 

Google Scholar 
Hang, K. et al. Perching and resting – A paradigm for UAV maneuvering with modularized landing gears. Sci. Roboticshttps://doi.org/10.1126/scirobotics.aau6637 (2019).Article 

Google Scholar 
Danko, T. W., Kellas, A. & Oh, P. Y. Robotic rotorcraft and perch-and-stare: Sensing landing zones and handling obscurants. In ICAR ’05. Proceedings., 12th International Conference on Advanced Robotics, 2005 296–302, https://doi.org/10.1109/ICAR.2005.1507427 (2005).Pauli, J. N., Zachariah Peery, M., Fountain, E. D. & Karasov, W. H. Arboreal folivores limit their energetic output, all the way to slothfulness. Am. Nat. 188, 196–204, https://doi.org/10.1086/687032 (2016).Olson, R. A., Glenn, Z. D., Cliffe, R. N. & Butcher, M. T. Architectural properties of sloth forelimb muscles (Pilosa: Bradypodidae). J. Mamm. Evol. 25, 573–588. https://doi.org/10.1007/s10914-017-9411-z (2018).Article 

Google Scholar 
Kovač, M., Germann, J., Hürzeler, C., Siegwart, R. Y. & Floreano, D. A perching mechanism for micro aerial vehicles. J. Micro-Nano Mechatron. 5, 77–91. https://doi.org/10.1007/s12213-010-0026-1 (2009).Article 

Google Scholar 
Toon, J. ’SlothBot in the Garden’ Demonstrates Hyper-Efficient Conservation Robot.Thomas, J. et al. Aggressive flight with quadrotors for perching on inclined surfaces. J. Mech. Robot. 8, 51007. https://doi.org/10.1115/1.4032250 (2016).Article 

Google Scholar 
Daler, L., Klaptocz, A., Briod, A., Sitti, M. & Floreano, D. A perching mechanism for flying robots using a fibre-based adhesive. In 2013 IEEE International Conference on Robotics and Automation, 4433–4438 (IEEE, 2013).Kovač, M., Germann, J., Hürzeler, C., Siegwart, R. Y. & Floreano, D. A perching mechanism for micro aerial vehicles. J. Micro-Nano Mechatron. 5, 77–91 (2009).Article 

Google Scholar 
Pope, M. T. et al. A multimodal robot for perching and climbing on vertical outdoor surfaces. IEEE Trans. Rob. 33, 38–48. https://doi.org/10.1109/TRO.2016.2623346 (2017).Article 

Google Scholar 
Lussier Desbiens, A., Asbeck, A. T. & Cutkosky, M. R. Landing, perching and taking off from vertical surfaces. Int. J. Robotics Res. 30, 355–370 (2011).Article 

Google Scholar 
Nguyen, H.-N., Siddall, R., Stephens, B., Navarro-Rubio, A. & Kovač, M. A Passively adaptive microspine grapple for robust, controllable perching. In 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), 80–87 (IEEE, 2019).Braithwaite, A., Al Hinai, T., Haas-Heger, M., McFarlane, E. & Kovač, M. Tensile web construction and perching with nano aerial vehicles. In Robotics Research (eds Bicchi, A. & Burgard, W.) (Springer, Cham, 2018).
Google Scholar 
Zhang, K., Chermprayong, P., Alhinai, T. M., Siddall, R. & Kovac, M. SpiderMAV: Perching and stabilizing micro aerial vehicles with bio-inspired tensile anchoring systems. In International Conference on Intelligent Robots and Systems (2017).Roderick, W. R. T., Jiang, H., Wang, S., Lentink, D. & Cutkosky, M. R. Bioinspired grippers for natural curved surface perching. In Conference on Biomimetic and Biohybrid Systems, 604–610 (Springer, 2017).Thomas, J., Loianno, G., Daniilidis, K. & Kumar, V. Visual servoing of quadrotors for perching by hanging from cylindrical objects. IEEE Robotics Automation Lett.https://doi.org/10.1109/LRA.2015.2506001 (2016).Article 

Google Scholar 
McLaren, A., Fitzgerald, Z., Gao, G. & Liarokapis, M. A passive closing, tendon driven, adaptive robot hand for ultra-fast, aerial grasping and perching. In IEEE International Conference on Intelligent Robots and Systems 5602–5607, https://doi.org/10.1109/IROS40897.2019.8968076 (2019).Zhang, Z., Xie, P. & Ma, O. Bio-inspired trajectory generation for UAV perching. In 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 997–1002 (IEEE, 2013).Doyle, C. E. et al. An avian-inspired passive mechanism for quadrotor perching. IEEE/ASME Trans. Mechatron. 18, 506–517. https://doi.org/10.1109/TMECH.2012.2211081 (2013).Article 

Google Scholar 
Erbil, M. A., Prior, S. D. & Keane, A. J. Design optimisation of a reconfigurable perching element for vertical take-off and landing unmanned aerial vehicles. Int. J. Micro Air Veh. 5, 207–228 (2013).Article 

Google Scholar 
Chi, W., Low, K. H., Hoon, K. H. & Tang, J. An optimized perching mechanism for autonomous perching with a quadrotor. In IEEE International Conference on Robotics and Automation, 3109–3115, (2014). https://doi.org/10.1109/ICRA.2014.6907306Roderick, W. R. T., Cutkosky, M. R. & Lentink, D. Bird-inspired dynamic grasping and perching in arboreal environments. Sci. Roboticshttps://doi.org/10.1126/scirobotics.abj7562 (2021).Article 

Google Scholar 
Garcia-Rubiales, F. J., Ramon-Soria, P., Arrue, B. C., Ollero, A. Magnetic & detaching system for Modular UAVs with perching capabilities in industrial environments.,. International Workshop on Research. Education and Development on Unmanned Aerial Systems, RED-UAS2019(172–176), 2019. https://doi.org/10.1109/REDUAS47371.2019.8999704 (2019).Bai, L. et al. Design and experiment of a deformable bird-inspired UAV perching mechanism. J. Bionic Eng. 18, 1304–1316. https://doi.org/10.1007/s42235-021-00098-5 (2021).Article 

Google Scholar 
Joachimczak, M., Suzuki, R. & Arita, T. Artificial metamorphosis: Evolutionary design of transforming, soft-bodied robots. Artif. Life 22(271–298), 2016. https://doi.org/10.1162/artl_a_00207 (2016).Article 

Google Scholar 
Sims, K. Evolving Virtual Creatures. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’94, 15-22, https://doi.org/10.1145/192161.192167 (Association for Computing Machinery, 1994).Bongard, J. Morphological change in machines accelerates the evolution of robust behavior. Proc. Natl. Acad. Sci. 108, 1234–1239. https://doi.org/10.1073/pnas.1015390108 (2011).Article 
ADS 

Google Scholar 
Truman, J. W. & Riddiford, L. M. The origins of insect metamorphosis. Nature 401, 447–452. https://doi.org/10.1038/46737 (1999).Article 
ADS 
CAS 

Google Scholar 
Campbell, N. A. et al. Biology: A Global Approach (Pearson New Your, NY, 2018).
Google Scholar 
Dai, J. S. & Rees Jones, J. Mobility in metamorphic mechanisms of foldable/erectable kinds. J. Mech. Des. 121, 375. https://doi.org/10.1115/1.2829470 (1999).Article 

Google Scholar 
Mintchev, S. & Floreano, D. Adaptive morphology: A design principle for multimodal and multifunctional robots. IEEE Robot. Autom. Mag. 23, 42–54 (2016).Article 

Google Scholar 
Shah, D. et al. Shape changing robots: Bioinspiration, simulation, and physical realization. Adv. Mater. 33, 2002882 (2021).Article 
CAS 

Google Scholar 
Sareh, S., Siddall, R., Alhinai, T. & Kovac, M. Bio-inspired soft aerial robots: Adaptive morphology for high-performance flight. In Soft Robotics: Trends, Applications and Challenges, 65–74 (Springer, 2017).Derrouaoui, S. H., Bouzid, Y., Guiatni, M. & Dib, I. A comprehensive review on reconfigurable drones: classification characteristics design and control technologies. Unmanned Syst. 10(01), 3–29. https://doi.org/10.1142/S2301385022300013 (2022).Floreano, D. & Wood, R. J. Science, technology and the future of small autonomous drones. Nature 521, 460–466 (2015).Article 
ADS 
CAS 

Google Scholar 
Hwang, D., Barron, E. J., Haque, A. B. & Bartlett, M. D. Shape morphing mechanical metamaterials through reversible plasticity. Sci. Robotics 7, eabg2171. https://doi.org/10.1126/scirobotics.abg2171 (2022).Article 

Google Scholar 
Siddall, R., Ortega Ancel, A. & Kovač, M. Wind and water tunnel testing of a morphing aquatic micro air vehicle. Interface focus 7, 20160085. https://doi.org/10.1098/rsfs.2016.0085 (2017).Article 

Google Scholar 
Chen, Y. et al. A biologically inspired, flapping-wing, hybrid aerial-aquatic microrobot. Sci. Roboticshttps://doi.org/10.1126/scirobotics.aao5619 (2017).Article 

Google Scholar 
Daler, L., Mintchev, S., Stefanini, C. & Floreano, D. A bioinspired multi-modal flying and walking robot. Bioinspiration Biomim.https://doi.org/10.1088/1748-3190/10/1/016005 (2015).Article 

Google Scholar 
Kovač, M., Wassim-Hraiz, Fauria, O., Zufferey, J. C. & Floreano, D. The EPFL jumpglider: A hybrid jumping and gliding robot with rigid or folding wings. In 2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011 1503–1508, https://doi.org/10.1109/ROBIO.2011.6181502 (2011).Riviere, V., Manecy, A. & Viollet, S. Agile robotic fliers: A morphing-based approach. Soft Roboticshttps://doi.org/10.1089/soro.2017.0120 (2018).Article 

Google Scholar 
Bucki, N. & Mueller, M. W. Design and control of a passively morphing quadcopter. In IEEE International Conference on Robotics and Automation, vol. 2019-May, 9116–9122, https://doi.org/10.1109/ICRA.2019.8794373 (2019).Mintchev, S., Daler, L., Eplattenier, G. L., Floreano, D. & Member, S. Foldable and self – deployable pocket sized quadrotor. In Proc. of the IEEE Conference on Robotics and Automation 2190–2195 (2015).Mintchev, S., Shintake, J. & Floreano, D. Bioinspired dual-stiffness origami. Sci. Robotics 0275, 1–8. https://doi.org/10.1126/scirobotics.aau0275 (2018).Article 

Google Scholar 
Zhao, M., Kawasaki, K., Anzai, T., Chen, X. & Noda, S. Transformable multirotor with two-dimensional multilinks : Modeling, control, and whole-body aerial manipulation. Int. J. Robot. Res.https://doi.org/10.1177/0278364918801639 (2018).Article 

Google Scholar 
Bucki, N., Tang, J. & Mueller, M. W. Design and control of a midair-reconfigurable quadcopter using unactuated hinges. IEEE Trans. Rob.https://doi.org/10.1109/TRO.2022.3193792 (2022).Article 

Google Scholar 
Shimoyama, I., Miura, H., Suzuki, K. & Ezura, Y. Insect-like microrobots with external skeletons. IEEE Control Syst. Mag. 13, 37–41. https://doi.org/10.1109/37.184791 (1993).Article 

Google Scholar 
Noh, M., Kim, S.-W., An, S., Koh, J.-S. & Cho, K.-J. Flea-inspired catapult mechanism for miniature jumping robots. IEEE Trans. Rob. 28, 1007–1018. https://doi.org/10.1109/tro.2012.2198510 (2012).Article 
ADS 

Google Scholar 
Miyashita, S., Guitron, S., Ludersdorfer, M., Sung, C. R. & Rus, D. An untethered miniature origami robot that self-folds, walks, swims, and degrades. In Proceedings – IEEE International Conference on Robotics and Automation 2015-June, 1490–1496, https://doi.org/10.1109/ICRA.2015.7139386 (2015).Morgan, J., Magleby, S. P. & Howell, L. L. An approach to designing origami-adapted aerospace mechanisms. J. Mech. Des.https://doi.org/10.1115/1.4032973 (2016).Article 

Google Scholar 
Liang, X. et al. The AmphiHex: A novel amphibious robot with transformable leg-flipper composite propulsion mechanism. In IEEE International Conference on Intelligent Robots and Systems 3667–3672, https://doi.org/10.1109/IROS.2012.6386238 (2012).Polygerinos, P. et al. Soft robotics: Review of fluid-driven intrinsically soft devices; manufacturing, sensing, control, and applications in human-robot interaction. Adv. Eng. Mater.https://doi.org/10.1002/adem.201700016 (2017).Article 

Google Scholar 
Coyle, S., Majidi, C., LeDuc, P. & Hsia, K. J. Bio-inspired soft robotics: Material selection, actuation, and design. Extreme Mech. Lett. 22, 51–59. https://doi.org/10.1016/j.eml.2018.05.003 (2018).Article 

Google Scholar 
Rus, D. & Tolley, M. T. Design, fabrication and control of soft robots. Nature 521, 467–475. https://doi.org/10.1038/nature14543 (2015).Article 
ADS 
CAS 

Google Scholar 
Laschi, C., Mazzolai, B. & Cianchetti, M. Soft robotics: Technologies and systems pushing the boundaries of robot abilities. Sci. Robotics 1, eaah3690. https://doi.org/10.1126/scirobotics.aah3690 (2016).Article 

Google Scholar 
Boyraz, P., Runge, G. & Raatz, A. An overview of novel actuators for soft robotics. High Throughput 7, 1–21. https://doi.org/10.3390/act7030048 (2018).Article 

Google Scholar 
Miriyev, A., Stack, K. & Lipson, H. Soft material for soft actuators. Nat. Commun. 8, 1–8. https://doi.org/10.1038/s41467-017-00685-3 (2017).Article 
CAS 

Google Scholar 
Nguyen, P. H. & Kovač, M. Adopting physical artificial intelligence in soft aerial robots. IOP Conf. Ser.: Mater. Sci. Eng. 1261, 012006. https://doi.org/10.1088/1757-899X/1261/1/012006 (2022).Article 

Google Scholar 
Kim, S.-J., Lee, D.-Y., Jung, G.-P. & Cho, K.-J. An origami-inspired, self-locking robotic arm that can be folded flat. Sci. Robotics 3, eaar2915. https://doi.org/10.1126/scirobotics.aar2915 (2018).Article 

Google Scholar 
Ruiz, F., Arrue, B. C. & Ollero, A. SOPHIE: Soft and flexible aerial vehicle for physical interaction with the environment. IEEE Robotics Automation Lett. 7, 11086–11093. https://doi.org/10.1109/LRA.2022.3196768 (2022).Article 

Google Scholar 
Doshi, N. et al. Model driven design for flexure-based microrobots. In IEEE International Conference on Intelligent Robots and Systems 2015-Decem, 4119–4126, https://doi.org/10.1109/IROS.2015.7353959 (2015).Koh, J.-S., Doshi, N., Wood, R. J., Temel, F. Z. & McClintock, H. The milliDelta: A high-bandwidth, high-precision, millimeter-scale Delta robot. Sci. Robotics 3, eaar3018. https://doi.org/10.1126/scirobotics.aar3018 (2018).Article 

Google Scholar 
Backus, S. B., Sustaita, D., Odhner, L. U. & Dollar, A. M. Mechanical analysis of avian feet: Multiarticular muscles in grasping and perching. R. Soc. Open Sci.https://doi.org/10.1098/rsos.140350 (2015).Article 

Google Scholar 
Paine, C. E. T. et al. Functional explanations for variation in bark thickness in tropical rain forest trees. Funct. Ecol. 24, 1202–1210. https://doi.org/10.1111/j.1365-2435.2010.01736.x (2010).Article 

Google Scholar 
Miriyev, A. & Kovač, M. Skills for physical artificial intelligence. Nat. Mach. Intell. 2, 658–660. https://doi.org/10.1038/s42256-020-00258-y (2020).Article 

Google Scholar 
Felton, S., Tolley, M., Demaine, E., Rus, D. & Wood, R. A method for building self-folding machines. Science 345, 644–646. https://doi.org/10.1126/science.1252610 (2014).Article 
ADS 
CAS 

Google Scholar 
Siddall, R., Byrnes, G., Full, R. J. & Jusufi, A. Tails stabilize landing of gliding geckos crashing head-first into tree trunks. Commun. Biol. 4, 1–12. https://doi.org/10.1038/s42003-021-02378-6 (2021).Article 
CAS 

Google Scholar 
Feduccia, A. Evidence from claw geometry indicating arboreal habits of Archaeopteryx. Science 259, 790–793. https://doi.org/10.1126/science.259.5096.790 (1993).Article 
ADS 
CAS 

Google Scholar  More

Burt, W. H. J. Mamm. 24, 346–352 (1943).Article 

Google Scholar 
Heathcote, R. J. P. et al. Nature Ecol. Evol. https://doi.org/10.1038/s41559-022-01950-5 (2023).Article 

Google Scholar 
Moorcroft, P. R., Lewis, M. A. & Crabtree, R. L. Proc. R. Soc. Lond. B 273, 1651–1659 (2006).
Google Scholar 
Moorcroft, P. R. & Barnett, A. Ecology 89, 1112–1119 (2008).Article 

Google Scholar 
Hattori, A. & Takuro, S. J. Mar. Biol. Assoc. U.K. 93, 2265–2272 (2013).Article 

Google Scholar 
Van Moorter, B. et al. Oikos 118, 641–652 (2009).Article 

Google Scholar 
Merkle, J. A., Potts, J. R. & Fortin, D. Oikos 126, https://doi.org/10.1111/oik.03356 (2017).Bracis, C., Gurarie, E., Van Moorter, B. & Goodwin, R. A. PLoS ONE 10, e0136057 (2015).Article 

Google Scholar 
Ranc, N., Cagnacci, F. & Moorcroft, P. R. Ecol. Lett. 25, 716–728 (2022).Article 

Google Scholar 
Schlägel, U. E. & Lewis, M. A. Methods Ecol. Evol. 5, 1236–1246 (2014).Article 

Google Scholar 
Ranc, N., Moorcroft, P. R., Ossi, F. & Cagnacci, F. Proc. Natl Acad. Sci. USA 118, e2014856118 (2021).Article 
CAS 

Google Scholar 
Ranc, N. et al. Sci. Rep. 10, 11946 (2020).Merkle, J. A., Fortin, D. & Morales, J. M. Ecol. Lett. 17, 924–931 (2014).Article 
CAS 

Google Scholar 
Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Trends Ecol. Evol. 34, 355–368 (2019).Article 

Google Scholar 
Rigoudy, N. L. A. et al. Behav. Ecol. 33, 789–797 (2022).Article 

Google Scholar 
Forrester, T. D., Casady, D. S. & Wittmer, H. U. Behav. Ecol. Sociobiol. 69, 603–612 (2015).Article 

Google Scholar 
Jesmer, B. R. et al. Science 361, 1023–1025 (2018).Article 
CAS 

Google Scholar  More

In a region under consideration, let at any instant (t >0), x and y represent the prey and predator population densities, respectively. The rate of change of each model species density at time t is made on the following assumptions:

1.

Prey population grow logistically in the absence of predator with birth rate r, which is affected by the fear ((f_1)) of predator (when predators are around).

2.

There is a reduction in the rate of prey density change due to three types of death, namely, natural death with the rate (d_1), fear related death5 with the level of fear (f_2) and over crowding death with the rate (d_2).

3.

Also, the rate of change of prey density decreases due to predation of predator population following a predator-dependent functional response describing both predatory and prey schooling behaviors10. Response function is expressed in functional form describing as (zeta (x, y)=frac{cxy}{1+chxy}), where c denotes the rate of consumption and h represents handling time of predator for one prey.

4.

Predator population survive in the system by consuming prey population only. They grow with conversion efficiency (c_1) of prey biomass into predator biomass.

5.

Predator population harvested from the system which reduces its rate of density. We consider a nonlinear harvesting term (Michaelis-Menten type) given by, (H(y)=dfrac{qEy}{p_1E+p_2y}). Here, parameters q and E, respectively, represent the catchability rate and harvesting effort. It is easy to observe that (Hrightarrow frac{q}{p_1}y) as (Erightarrow infty) for a fixed value of y. Also, (Hrightarrow frac{q}{p_2}E) as (yrightarrow infty) for a fixed value of E. Therefore, at higher effort levels, (p_1) is proportional to the stock level-catch rate ratio and at higher levels of stock, (p_2) is proportional to the effort level-catch rate ratio.

6.

Lastly, we assume that the predator population experience natural as well as over crowding related death with the rates (d_3) and (d_4), respectively.

Keeping all these above assumptions in mind, we formulate the following prey–predator model:$$begin{aligned} frac{dx}{dt}= & {} frac{rx}{1+f_1y}-(1+f_2y)d_1x-d_2x^2-frac{cxy^2}{1+chxy}nonumber ,\ frac{dy}{dt}= & {} frac{c_1cxy^2}{1+chxy}-d_3y-d_4y^2-frac{qEy}{p_1E+p_2y}. end{aligned}$$
(1)
System (1) is to be analyzed with the initial conditions (x(0),y(0) >0). All the model parameters are assumed to be positive constants and their hypothetical values that we used for numerical calculations are as follows:$$begin{aligned}{} & {} r=3.1, f_1=1, f_2=0.4, d_1=0.1, d_2=0.08, c=0.11, h=0.1, c_1=0.5, d_3=0.1,nonumber \{} & {} d_4=0.06, q=0.65, E=0.5, p_1=0.5, p_2=0.65. end{aligned}$$
(2)
In Table 1, we have provided system’s equilibria, sufficient conditions of their existence and stability. Mathematically, it is difficult to determine the existence of coexistence (interior) equilibrium point(s) by the given nullclines. So, we visualize it numerically (see Fig. 1). It is apparent from the figure that on increasing the value of E, number of coexistence equilibrium points reduces and after a certain range there is no coexistence equilibrium point.Table 1 Sufficient conditions for the existence and stability of different equilibrium points of system (1).Full size tableFigure 1Nullclines for different values of E. Other parameters are same as in (2).Full size image
Transcritical bifurcationFrom Table 1, it is clear that the equilibrium (E_0) is stable if (rr^{TB}=d_1=0.1)) then (E_0) becomes unstable and the equilibrium point (E_1) exists and becomes stable.Figure 2Transcritical bifurcation with respect to r. Rest of the parameters are same as in (2).Full size imageHopf bifurcationOne of the most common dynamics in interacting population dynamics is oscillating behavior, which implies that there is a Hopf bifurcation. By local changes in equilibrium properties, Hopf bifurcation describes when a periodic solution appears or disappears. In this section, we study the Hopf bifurcation through the coexistence equilibrium (E^*) with respect to the model parameter E. Discussion for the existence of Hopf bifurcation is as follows:As it is easy to follow, we verify Hopf bifurcation numerically. We have considered the parameters value same as in (2) except (c=0.1) and E. At (E=E^{[HB]}=0.1196559641), the trace of the Jacobian matrix at (E^*(2.618402886, 2.352228027)) is zero and determinant, (Det(J_{E^*})=0.4474794791 >0). The value of (dfrac{d(Tr(J_{E^*}))}{dE}Big |_{E=E^{[HB]}}=-0.02965188514ne 0). Therefore, the transversality condition for Hopf bifurcation is also satisfied at (E=E^{[HB]}). Thus, these results confirm that the system (1) experiences a Hopf bifurcation2 around (E^*(2.618402886, 2.352228027)).Moreover, we obtain Lyapunov number (L_1=-0.04728284756pi More

Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).Article 
ADS 
CAS 

Google Scholar 
Schumaker, N. H. Using landscape indices to predict habitat connectivity. Ecology 77, 1210–1225 (1996).Article 

Google Scholar 
Pacifici, M. et al. Assessing species vulnerability to climate change. Nat. Clim. Chang. 5, 215–224 (2015).Article 
ADS 

Google Scholar 
Fahrig, L. Effects of habitat fragmentation on biodiversity. Annu. Rev. Ecol. Evol. Syst. 34, 487–515 (2003).Article 

Google Scholar 
Clavero, M., Brotons, L., Pons, P. & Sol, D. Prominent role of invasive species in avian biodiversity loss. Biol. Conserv. 142, 2043–2049 (2009).Article 

Google Scholar 
Soroye, P., Ahmed, N. & Kerr, J. T. Opportunistic citizen science data transform understanding of species distributions, phenology, and diversity gradients for global change research. Glob. Change Biol. 24, 5281–5291 (2018).Article 
ADS 

Google Scholar 
Gotelli, N. J. & Colwell, R. K. Quantifying biodiversity: Procedures and pitfalls in the measurement and comparison of species richness. Ecol. Lett. 4, 379–391 (2001).Article 

Google Scholar 
Dickinson, J. L., Zuckerberg, B. & Bonter, D. N. Citizen science as an ecological research tool: Challenges and benefits. Annu. Rev. Ecol. Evol. Syst. 41, 149–172 (2010).Article 

Google Scholar 
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179 (2019).Article 

Google Scholar 
Steen, V. A., Elphick, C. S. & Tingley, M. W. An evaluation of stringent filtering to improve species distribution models from citizen science data. Divers. Distrib. 25, 1857–1869 (2019).Article 

Google Scholar 
Crall, A. W. et al. Assessing citizen science data quality: An invasive species case study. Conserv. Lett. 4, 433–442 (2011).Article 

Google Scholar 
Bird, T. J. et al. Statistical solutions for error and bias in global citizen science datasets. Biol. Conserv. 173, 144–154 (2014).Article 

Google Scholar 
MacKenzie, D. I. et al. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248–2255 (2002).Article 

Google Scholar 
Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE 9(10), E111436 (2014).Article 
ADS 

Google Scholar 
Weisshaupt, N., Lehikoinen, A., Mäkinen, T. & Koistinen, J. Challenges and benefits of using unstructured citizen science data to estimate seasonal timing of bird migration across large scales. PLoS ONE 16, e0246572 (2021).Article 
CAS 

Google Scholar 
Kéry, M. & Schmid, H. Estimating species richness: Calibrating a large avian monitoring programme. J. Appl. Ecol. 43, 101–110 (2006).Article 

Google Scholar 
Chao, A. & Chiu, C. H. Species richness: Estimation and comparison 1–26 (Wiley StatsRef: Statistics Reference Online, 2014).
Google Scholar 
Walther, B. A. & Moore, J. L. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28, 815–829 (2005).Article 

Google Scholar 
Chao, A. & Lee, S.-M. Estimating the number of classes via sample coverage. J. Am. Stat. Assoc. 87, 210–217 (1992).Article 
MATH 

Google Scholar 
Walther, B. A. & Morand, S. Comparative performance of species richness estimation methods. Parasitology 116, 395–405 (1998).Article 

Google Scholar 
Walther, B. A. & Martin, J. L. Species richness estimation of bird communities: How to control for sampling effort?. Ibis 143, 413–419 (2001).Article 

Google Scholar 
Walther, B. A., Cotgreave, P., Price, R., Gregory, R. & Clayton, D. H. Sampling effort and parasite species richness. Parasitol. Today 11, 306–310 (1995).Article 
CAS 

Google Scholar 
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 345, 101–118 (1994).Article 
ADS 
CAS 

Google Scholar 
Bean, W. T., Stafford, R. & Brashares, J. S. The effects of small sample size and sample bias on threshold selection and accuracy assessment of species distribution models. Ecography 35, 250–258 (2012).Article 

Google Scholar 
Flather, C. Fitting species–accumulation functions and assessing regional land use impacts on avian diversity. J. Biogeogr. 23, 155–168 (1996).Article 

Google Scholar 
White, P. E. et al. A comparison of the species–time relationship across ecosystems and taxonomic groups. Oikos 112, 185–195 (2006).Article 

Google Scholar 
McGlinn, D. J. & Palmer, M. W. Modeling the sampling effect in the species–time–area relationship. Ecology 90, 836–846 (2009).Article 

Google Scholar 
Isaac, N. J. et al. Statistics for citizen science: Extracting signals of change from noisy ecological data. Method Ecol. Evol. 5, 1052–1060 (2014).Article 

Google Scholar 
Ding, T. et al. The 2020 CWBF checklist of the birds of Taiwan (Chinese Wild Bird Federation, 2020).
Google Scholar 
Lin, M.-M. et al. Bird records database of a Taiwanese non-governmental organization, the Chinese wild bird federation, from 1972 to 2017. TW. J. Biodivers. 21, 83–101 (2019).
Google Scholar 
Dokter, A. M., Desmet, P., Van Hoey, S. (2022) bioRad: Biological analysis and visualization of weather radar data: v0. 6.0Strimas-Mackey, M. et al. (2020) Best practices for using eBird Data. Version 1.0. Cornell Laboratory of Ornithology, Ithaca, New York, 10.5281/zenodo.3620739Robinson, O. J. et al. Using citizen science data in integrated population models to inform conservation. Biol. Conserv. 227, 361–368 (2018).Article 

Google Scholar 
Callaghan, C. T., Martin, J. M., Major, R. E. & Kingsford, R. T. Avian monitoring–comparing structured and unstructured citizen science. Wildl. Res. 45, 176–184 (2018).Article 

Google Scholar 
Robinson, W. D., Hallman, T. A. & Hutchinson, R. A. Benchmark bird surveys help quantify counting accuracy in a citizen-science database. Front. Ecol. Evol. 9, 568278 (2021).Article 

Google Scholar 
Neate-Clegg, M. H., Horns, J. J., Adler, F. R., Aytekin, M. Ç. K. & Şekercioğlu, Ç. H. Monitoring the world’s bird populations with community science data. Biol. Conserv. 248, 108653 (2020).Article 

Google Scholar 
Chao, A. Nonparametric estimation of the number of classes in a population. Scand. J. Stat 1, 265–270 (1984).
Google Scholar 
Hsieh, T., Ma, K. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

Google Scholar 
Team, R. C. (2013).R: A language and environment for statistical computing.James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).Book 
MATH 

Google Scholar 
Magurran, A. E. & McGill, B. J. Biological diversity: Frontiers in measurement and assessment (OUP Oxford, 2010).
Google Scholar 
Spiess, A.-N. (2018) Package ‘propagate’RC Team, C Worldwide. The R stats package (R Foundation for Statistical Computing, 2002).
Google Scholar 
Guralnick, R. & Van Cleve, J. Strengths and weaknesses of museum and national survey data sets for predicting regional species richness: Comparative and combined approaches. Divers. Distrib. 11, 349–359 (2005).Article 

Google Scholar 
Dar, T. A. et al. Bird community structure in Phakot and Pathri Rao watershed areas in Uttarakhand. India. Int. J. Environ. Sci. 34, 193–205 (2008).
Google Scholar 
Azevedo, G. H. et al. Effectiveness of sampling methods and further sampling for accessing spider diversity: A case study in a Brazilian Atlantic rainforest fragment. Insect. Conserv. Divers. 7, 381–391 (2014).Article 

Google Scholar 
Bonter, D. N. & Cooper, C. B. Data validation in citizen science: A case study from project feederwatch. Front. Ecol. Environ. 10, 305–307 (2012).Article 

Google Scholar 
Gómez-Martínez, C. et al. Forest fragmentation modifies the composition of bumblebee communities and modulates their trophic and competitive interactions for pollination. Sci. Rep. 10, 1–15 (2020).Article 

Google Scholar 
Sullivan, B. L. et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).Article 

Google Scholar 
Newson, S. E., Woodburn, R. J., Noble, D. G., Baillie, S. R. & Gregory, R. D. Evaluating the breeding bird survey for producing national population size and density estimates. Bird Study 52, 42–54 (2005).Article 

Google Scholar 
Robbins, C. S. Effect of time of day on bird activity. Stud. Avian Biol. 6, 275–286 (1981).
Google Scholar 
Farmer, R. G., Leonard, M. L. & Horn, A. G. Observer effects and avian-call-count survey quality: Rare-species biases and overconfidence. Auk 129, 76–86 (2012).Article 

Google Scholar 
Gardiner, M. M. et al. Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10, 471–476 (2012).Article 

Google Scholar 
Swanson, A., Kosmala, M., Lintott, C. & Packer, C. A generalized approach for producing, quantifying, and validating citizen science data from wildlife images. Conserv. Biol. 30, 520–531 (2016).Article 

Google Scholar 
Ratnieks, F. L. et al. Data reliability in citizen science: Learning curve and the effects of training method, volunteer background and experience on identification accuracy of insects visiting ivy flowers. Methods Ecol. Evol. 7, 1226–1235 (2016).Article 

Google Scholar 
Lopez, L. C. S., de Aguiar Fracasso, M. P., Mesquita, D. O., Palma, A. R. T. & Riul, P. The relationship between percentage of singletons and sampling effort: A new approach to reduce the bias of richness estimates. Ecol. Indicators 14, 164–169 (2012).Article 

Google Scholar 
Bunge, J. & Fitzpatrick, M. Estimating the number of species: A review. J. Am. Stat. Assoc. 88, 364–373 (1993).
Google Scholar 
SoberónM, J. & LlorenteB, J. The use of species accumulation functions for the prediction of species richness. Conserv. Biol. 7, 480–488 (1993).Article 

Google Scholar 
Magurran, A. E. Species abundance distributions over time. Ecol. Lett. 10, 347–354 (2007).Article 

Google Scholar 
de Caprariis, P., Lindemann, R. & Haimes, R. A relationship between sample size and accuracy of species richness predictions. J. Int. Assoc. Math. Geol. 13, 351–355 (1981).Article 

Google Scholar 
Klemann-Junior, L., Villegas Vallejos, M. A., Scherer-Neto, P. & Vitule, J. R. S. Traditional scientific data vs. uncoordinated citizen science effort: A review of the current status and comparison of data on avifauna in Southern Brazil. PLoS ONE 12, e0188819. https://doi.org/10.1371/journal.pone.0188819 (2017).Article 
CAS 

Google Scholar 
Tulloch, A. I. & Szabo, J. K. A behavioural ecology approach to understand volunteer surveying for citizen science datasets. Emu 112, 313–325 (2012).Article 

Google Scholar 
Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. 8, e1000385 (2010).Article 

Google Scholar 
Kamp, J. et al. Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib. 22, 1024–1035. https://doi.org/10.1111/ddi.12463 (2016).Article 

Google Scholar 
Lin, Y.-P. et al. Uncertainty analysis of crowd-sourced and professionally collected field data used in species distribution models of Taiwanese moths. Biol. Conserv. 181, 102–110 (2015).Article 

Google Scholar 
Fletcher, R. J. Jr. et al. A practical guide for combining data to model species distributions. Ecology 100, e02710 (2019).Article 

Google Scholar  More

The contributions of SMFEs to the local economy and developmentSMFEs are characterized by limited resources, hence their inability to employ more people however, the few being employed to aid in the operations of the businesses contribute to the reduction of the employment gap among the youth in the study areas. The employment opportunities provided by SMFEs supplement the central government’s efforts to offer employment to the people. Subsequently, the people in the area depend on it for their livelihood to improve their living standards. The study found a diverse number of SMFEs in terms of wood and non-wood-related activities (Fig. 3) that people engage in as primary or secondary jobs. Some evidence has proved that SMFE’s contribution to forest employment is above 50% in some countries like Brazil, Uganda, Guyana, and China, and almost 80 to 90% of all forest-based enterprises in most countries7. This may directly impact efforts to reduce poverty by improving the living standards of people who form and operate SMFEs as a livelihood.Zada et al.10 also reported that households who own SMFEs had a wealth index increase from 5.4 to 7.4 whereas those without SMFEs had an index of 4.9. SMFEs do have the potential to improve household income levels which can lead to reinvesting and expansion. This study found that the monthly expenses of SMFEs contribute to 46.2% of their monthly sales. Therefore, if SMFEs can increase significantly, their ability to reinvest while observing the best practices in operating their businesses then they will be able to maximize their turnovers. This can result in expansion and more employment opportunities for others hence, reducing the burden on the government to provide employment.There is a direct positive and significant relationship between SMFEs and local economic development11. SMFEs were reported to positively and significantly mediate the relationships between government support, entrepreneurship knowledge, and local economic development. SMFEs with informal or formal training can ensure government support is efficiently used in tapping into entrepreneurial knowledge to drive their impacts on local economies. This will also allow them to grow into sustainable businesses while also promoting the sustainability of forest resources which they depend on for raw materials.Operational characteristics and impacts of SMFEs on sustainabilityAround the globe and per the laws of Ghana, businesses are required to fulfill certain obligations to enable them to run smoothly12. Failure to undertake these tasks may attract severe penalties, including criminal charges that may carry significant jail terms. An example is failure to pay taxes and adhere to certain regulations. This section looks at certain characteristics of SMFEs in this study that project their impact on sustainable forest management.Firstly, the laws of Ghana make it mandatory for all business categories to pay tax and as such, SMFEs are not left out. However, the major challenge with taxes in Ghana and by extension, the world, is compliance. Mantey13 reported that 59.1% of small business owners did not understand the Ghanaian Tax System. The key lesson drawn from this observation is that, as SMFEs, one of their characteristics is that they generate a smaller income compared to larger companies or corporate bodies. This goes a long way in determining the amount they pay as taxes. In addition, by their nature, they can under-declare the revenue they make to influence the amount they will be taxed. This calls for the development and flawless implementation of mechanisms to monitor and audit these SMFEs to ensure that they comply with tax directives and regulations.Mantey13 further reported that 57.4% of the surveyed business owners are not aware of most tax laws and guidelines on the taxation of incomes for organizations. Some blamed their inability to pay taxes on the business being slow and others were unwilling to give a response to why they were unable to pay their taxes. In this study, majority (77.5%) indicated that they pay taxes. It was also established in this study that payment of taxes has a significantly weak correlation with the educational background of the respondents. Though the majority of the SMFEs paid taxes, it may not be directly linked to all respondents having some form of formal education and vice versa. However, this may be factored in when considering the training and mentoring of SMFEs to contribute to local development by paying their taxes. More SMFEs may endeavor to pay their taxes regularly if they understand what these taxes can do to improve their work environment.Governments in recent times have stepped up revenue mobilization efforts to capture more businesses into the tax bracket of the country. This has seen the revenue authorities recruit and train more revenue officers to reach businesses like SMFEs which are mostly not reachable due to their inability to register their businesses.Secondly, the majority (71.25%) of SMFEs in this study was not registered. This adds to the general belief that most businesses operate without the required licenses or have failed to renew their expired licenses. Some studies also made similar observations and arrived at the lack of enforcement of laws, as a key reason why many businesses in developing countries remained unregistered contrary to the requirements of the law14,15. Further analysis showed SMFEs who belong to associations are likely to register their businesses because it is a requirement to join them. The benefits of belonging to an association include access to loan facilities and other credit programs and therefore some SMFEs do not want to risk missing out through failure to get their business registered16.SMFEs need to get registered for them to be considered legitimate business entities however, this seems to be a challenge in most developing countries. Tomaselli et al.17 found this assertion relevant when investigating SMFEs access to microfinance. Registration of business is a key requirement to access loan facilities and so is belonging to a recognized association. Associations are known to serve as guarantors for members who want loan facilities from banks and other financial institutions to expand their businesses16. Unregistered, unregulated, and unmonitored SMFEs are those whose activities tend to compromise the sustainability of forest resources18. Therefore, registration of SMFEs does that only serve the interest of governments but also the interests of these SMFEs themselves.The third has to do with the sourcing of raw materials. Ghana being a tropical country is blessed abundantly with forest resources but over the decades, the overexploitation of these forests has brought to the brink of extinction, various species of both plant and animal life19. The dependence of SMFEs on the forests cannot be underestimated as literature, citing Osei Tutu et al.20, posits that SMFEs contribute to 95% of the income of some rural households. This study shows that 68.8% of SMFEs get their raw materials directly from the forest. Both woody and non-woody materials are in abundance and can be extracted with minimal cost.In sourcing raw materials from the forests in Ghana, SMFEs are required to obtain permits or licenses from the relevant authorities such as the forestry commission. This permit/license is what allows or gives this SMFEs access to otherwise inaccessible forest reserves to harvest raw materials20. Additionally, these documents can go as far as determining the type and quantity of materials to harvest. It can also determine the type of access granted as these accesses can vary or differ depending on the time or season of harvest18. The issuance of permits and licenses is meant to monitor and regulate resource harvesting with the primary goal of checking the overexploitation of these resources. However, this is not possible due to the high levels of non-compliance by SMFEs21. Evident in this study is the 78.2% of SMFEs who gather raw materials from the forest without permits/licenses.Osei Tutu et al.18 concluded that the neglect of the SMFEs sub-sector is responsible for the loss of state revenue because of their unwillingness to register and pay appropriate taxes and permit fees for their illegal and unsustainable business operations. The report further posits that “despite the numerous support channels (national and international) available to them, the roles played by SMFEs in poverty reduction are significantly unimpactful hence the need to intensify capitalizing on all opportunities to address challenges they present.” The government institutions in charge of these forest resources depend on these permits and license fees to supplement their already insufficient government subventions for the operations. Therefore, losing revenues may undermine their sustainability programs.Driving factors of SMFEsThe ability of a business to thrive highly depends on its ability to overcome certain challenges within its operating environment22. That alone, however, is not enough as certain factors ignite the ambition of a business. These factors decisively influence the success or the failure of the business hence, they are identified as determinants. The study sought to identify some determinants that drive the activities of SMFEs. Responses from the SMFEs concluded that economic and social factors such as resource availability, profits/revenue, employment, and labor are the key determinants that drive the SMFEs.Resource availability was the major driver of their activities cited by 91.3% of SMFEs. This is because, the numerous forests the nation is endowed with provide abundantly, the raw materials needed for them to use. Due to the favorable climatic conditions prevailing in the high forest zones, there is a constant supply of materials needed by SMFEs to produce their products for business23. In addition, availability means less competition for limited resources and therefore it boils down to the ability to process these raw materials into finished goods for market consumption hence, reducing the costs of production24.SMFEs also pointed to profits/revenue, as the factor driving their activities to engage in, and sustain their business. The abundance and readily availability of raw materials are very important to the growth of their business and in turn, help them maximize their returns. This is because the inputs they make to acquire the raw materials are relatively low in comparison to the total revenues they generate. This observation is also reflected in the captured expenditures they make as inputs or investments into their businesses.SMFEs that need technologically advanced mechanisms and equipment are those that are required or inclined to make heavy investments whereas those that need simple tools and equipment invest less. Whichever the case, the nature of SMFEs suggests that a business that requires raw materials with very minimal or no costs involved at all, yet yields very high profits, is how people can improve their living25. Badini et al.26 classified enabling environment of SMFEs into external and internal factors where financial capital, business management, and organizational capacities form internal factors. On the other hand, external factors include regulatory frameworks, forest law enforcement, and natural capital which refers to the stock of natural resources or environmental assets. The success of any SMFE is largely dependent on these factors.Finally, 8.75% of the SMFEs view labor and employment, as the determinants driving their existence. For them, compared to other labor-intensive ventures, their business does not require huge labor to get work done. The few hands needed means most of the revenues do not go to paying workers. They can dictate and bargain to their advantage because there are many people without jobs hence a job turned down, because of less encouraging benefits is gladly accepted by another25. Ultimately, the study finds that labor is cheap in some areas of the SMFEs’ environs primarily, due to unemployment.Sustainability challenges in forest management relative to SMFEs activitiesSince the United Nations Conference on Environment and Development (UNCED) in Rio de Janeiro, Brazil in 1992, key challenges of SFM have broadly covered the sustainability of forest resources through the reduction of deforestation and forest degradation, conservation and protection of biological diversity, genetic resources sustainability and improving forest goods and services valuation27. It is important to note that SMFEs have played an overlooked role in these challenges as it seems its contributions to poverty reduction have taken center stage in international discourses, with its negative impacts on the environment being relegated to the backseat when considering the causes of environmental degradation. Attempts to effectively manage the activities of SMFEs have witnessed the emergence of a lot of challenges that threaten the very sustainability the globe yearns for. Some reasons point to the source of the challenges that have plagued these efforts, some of which are highlighted below.First is the lack of resources to recruit and train the needed personnel to constantly monitor the activities of these SMFEs during the harvesting of raw materials. This makes it easier for them to enter restricted forest areas without the necessary documentation and proceed to harvest more than they are required to at any given time. Secondly, it is difficult to track their activities because many SMFEs currently, do not register their businesses as required by law.A typical example is the use of unapproved trails or routes and the use of inappropriate harvesting techniques such as burning. This leads to the destruction of various lifeforms that are critical to the regenerative capabilities of the forests28. The study also found that the supervision of the activities of SMFEs is very poor as only 12% of SMFEs had their activities supervised on certain occasions. This buttresses the assertion by Acheampong et al.29 who posited that the lack of supervision is a major issue that needs to be vigorously addressed if we need to achieve forest sustainability in developing countries.There is a need to educate SMFEs on the laws and regulations governing the use of forest resources. It was revealed that only 16% of the respondents have some knowledge of the regulations governing the harvest and use of both woody and non-woody forest resources. This knowledge gap is being exploited by SMFEs as an excuse for not doing what is expected of them. However, a study found that 69% of respondents claimed to have good knowledge of the regulations governing their activities14. This can be attributed to self-learning or the action of the supervising authorities who for one reason or another other can perform their mandate of educating the SMFEs. There is a need to properly equip the supervising agencies to carry out this mandate.The research, therefore, cites the non-registering of SMFEs as an underlying cause of the flouting of these regulations and laws. The research also suggests that some form of training can be done at the point of registering even before the certification is done. As observed in the area of training, there is not enough emphasis on the need to train SMFEs in sustainability issues in terms of harvesting raw materials. It was noted that the majority (67%) of SMFEs (Table 8) have no training on how to harvest, process, and adequately market their products to ensure maximum profits while sustaining the resources for future harvests. There is a need to institute training and capacity-building programs for SMFEs that will empower them to succeed and yet aim to ensure sustainable forest management.The role of sustainable forest management in climate change mitigationSustainable forest management (SFM) can play a significant role in climate change mitigation, as forests are an important sink for carbon dioxide and other greenhouse gases. By sequestering carbon in their biomass and soils, forests can help to remove carbon dioxide from the atmosphere, which can help to mitigate the impacts of climate change30.There are a number of ways in which SFM can support climate change mitigation, including through the conservation and expansion of forests, the sustainable management of forests, and the use of forest-based products and practices that reduce greenhouse gas emissions. Policymakers and stakeholders at local, national, and international levels are increasingly recognizing the role of forests in climate change mitigation, and there is growing interest in developing strategies and policies that support the use of forests for this purpose.However, there are challenges that impede the efficient leveraging of SFM for climate change mitigation and one of such challenges is the need to balance economic, social, and environmental considerations31. Forests provide a range of goods and services that are vital for human well-being and economic development, including timber, non-timber forest products, and ecosystem services such as carbon sequestration, water regulation, and habitat for wildlife32. However, these resources can be in high demand, and managing forests sustainably can be difficult, particularly in developing countries where there may be limited access to financial and technical resources33.Another challenge is the impact of external factors such as climate change on the health and productivity of forests34. Rising temperatures and changing weather patterns can affect the growth and survival of forests, and may also increase the risk of forest fires and pests35. Policymakers must consider the role of forests in mitigating and adapting to climate change, as well as the potential impacts on forest-dependent communities32.One way in which SMFEs can contribute to climate change mitigation is through the sustainable management of forests. By practicing sustainable forestry, SMFEs can help to maintain and enhance the carbon sequestration capacity of forests, which can help to remove carbon dioxide from the atmosphere and mitigate the impacts of climate change31. This can involve practices such as planting and reforestation, soil and water conservation, and the use of sustainable harvesting techniques32. However, this study revealed the majority of these SMFEs are unregistered and therefore not monitored. Meaning their activities cannot be regulated to ensure practices that promote climate change mitigation.SMFEs can also contribute to climate change mitigation by using forest-based products and practices that reduce greenhouse gas emissions. For example, the use of wood products as a substitute for fossil fuel-based products can help to reduce emissions, as wood products sequester carbon over their lifetime and do not release it into the atmosphere when they are used34. In addition, the use of biomass energy in place of fossil fuels can help to reduce emissions, provided that the biomass is sourced sustainably and the emissions associated with its transportation and use are accounted for35.Another way in which SMFEs can contribute to climate change mitigation is through the development of innovative solutions and technologies that support sustainable forestry practices and reduce greenhouse gas emissions. This could include the use of precision forestry techniques, which use advanced technology to improve the efficiency and sustainability of forestry operations34. It could also involve the development and commercialization of new forest-based products or practices that have a lower carbon footprint32.Policies can have a significant impact on the way in which forests are managed for climate change mitigation31. For example, policies that promote sustainable forestry practices, such as the use of certification schemes or incentive programs, can help to ensure that forests are managed in a way that meets the needs of current and future generations33. On the other hand, policies that do not adequately consider the needs and interests of all stakeholders, or that do not provide sufficient support for sustainable forestry practices, may have negative impacts on the ability of forests to contribute to climate change mitigation34.Overall, addressing the inter-challenges of SFM for climate change mitigation and the impact of policies is an important part of ensuring the sustainability and long-term viability of forests as a tool for mitigating climate change.Development of SMFEs within the forest-based economy of Ghana through policyDespite the global consensus on the sustainability of forest resources and their utmost importance regarding the sustenance of present and future generations, the situation remains unclear at the field level36. The application of criteria and indicators of sustainability provides support for a small but crucial clarification on achieving sustainable forest management (SFM). A meaningful basis for assessing SFM at operational levels will require clarification together with management prescriptions and performance standards while providing linkage to voluntary timber certification.Currently, many environment-based non-governmental organizations (ENGOs) like Global Footprint Network and Fauna & Flora International who are concerned about natural resource exploitation, are convinced by the international debate on criteria and indicators that timber harvesting and ecosystem services of the forests can be sustained37. Stakeholders of the forestry franchise agree that environmental conservation can be accommodated through a necessary and reasonable modification and adaptation of forest-harvesting practices. Therefore, multi-resource forest management as a new paradigm replaces the indigenous sustained-yield management approach that bases on growth-harvest equilibrium using policy as a vehicle38.Food and Agriculture Organization (FAO) is assisting countries through policy advice, technical assistance, capacity building, workshop, and hands-on training, to overcome the challenges of sustainable forest management39. The assistance is provided through the assessment of forest resources and the elements of SFM, as well as the monitoring of progress toward it. FAO also identifies, tests, and modern scientific SMF approaches and techniques to address climate change mitigation and adaptation challenges such as increasing demand for wood and non-wood forest products and services, pest, and diseases.The views held by the Forestry Commission and National Board for Small Scale Industries (NBSSI) during interviews are in line with the suggestions and actions by the World Bank and FOA that involve training and other support systems for managers of forest resources in tropical countries like Ghana that depends heavily on its natural forests. Despite the availability of some of the avenues needed to execute these strategies, the non-compliance by SMFEs makes it difficult for these targets to be met. The general thought is that, if all relevant authorities and stakeholders perform their roles effectively, the current challenges of maximizing the contributions of SMFEs to development and sustainable forest management can be realized.The impact of forest policies is evident in countries like Gabon, a country rich in forest resources, which regards forests as a critical economic resource. World Bank-supported reforms have helped make concessions awarding procedures more competitive and transparent40. Forest taxation recovery has been bolstered, with tax collection rates increasing from 40 to 80% between 2005 and 2010. Sustainable forest management is presently practiced in around 85% of productive forest areas and as a result of these reforms, the forestry sector’s contribution to Gabon’s GDP increased from 2.5% in 2004 to 4.7% in 200940.Support for small and medium forest-based firms raised actual cash income among forest user groups by 53% in India’s Andhra Pradesh throughout the project duration. Seasonal outmigration decreased by 23%, and the quality of thick forest cover in these places improved40,41.Ghana has made significant progress toward sustainable management of its forest resources via the adoption of different forest regulations like the Forest and Wildlife Policy of 1994, Timber Resources Management Act, of 2002, etc. The problem with most of the country’s forest resource policies is the lack of attention paid to the human component; the emphasis is on sustainable timber extraction, even if it is destructive to the livelihoods of forest-dependent populations. Forest policies have historically been determined by successive administrations’ economic interests, which essentially focused on the exploitation of wood resources for income production. This has been a significant impediment to the creation and development of non-timber forest products which the majority of SMFEs depend on in Ghana. This has allowed the number SMFEs rapidly increase due to the lack of coverage by forest policies42. The policy interventions in Gabon and India have yielded results that can provide the foundation needed for Ghana to formulate its policies for the development of SMFEs in a way that does not threaten sustainable forest management. More