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Time for a paradigm shift? Small carnivores' sensitivity highlights the importance of monitoring mid-rank predators in future global change studies.

Emmanuel Do Linh San
Published in: The Journal of animal ecology (2024)
Research Highlight: Jachowski, D. S., Marneweck, C. J., Olfenbuttel, C., & Harris, S. N. (2024). Support for the size-mediated sensitivity hypothesis within a diverse carnivore community. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13916. A current paradigm in ecological research suggests that top predators are suitable sentinel species to identify ecosystem dysfunctions and monitor the effects of climate change. However, the adequacy of top predators to systematically take this function may be mistakenly inferred or unintentionally conflated from the fact that these species are regarded as biodiversity indicators or keystone, umbrella and flagship species in most ecosystems. Regarding terrestrial mammalian carnivores (order Carnivora), some researchers recently suggested that the smaller species likely possess a higher sensitivity to environmental changes than large carnivores because of their biological attributes and their intermediate position in food webs. To test this hypothesis, Jachowski et al. (2024) used camera trapping followed by occupancy and structural equation modelling to explore the dynamics of a diverse carnivore community and the factors that influence them. Their results confirmed that small carnivores are more sensitive to habitat changes and are interconnected by a greater number of significant pathways compared with larger carnivores. This support for the size-mediated sensitivity hypothesis strengthens the proposition that small carnivores (and other mid-rank predators) are ideal sentinel species for monitoring the effects of the wide range of contemporary and future environmental changes. Time will tell whether this new 'middle-out ecology' paradigm will be considered in future global change studies.
Keyphrases
  • climate change
  • human health
  • healthcare
  • mental health
  • genetic diversity
  • current status
  • risk assessment
  • randomized controlled trial
  • machine learning
  • high resolution
  • convolutional neural network
  • deep learning