Idea paper: Improving forecasts of community composition with lightweight biodiversity monitoring across ecological and anthropogenic disturbance gradients

Jamie M. Kass, Nao Takashina, Nicholas R. Friedman, Buntarou Kusumoto, Mary E. Blair

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Accurate and up-to-date biodiversity forecasts enable robust planning for environmental management and conservation of landscapes under a wide range of uses. Future predictions of the species composition of ecological communities complement more frequently reported species richness estimates to better characterize the different dimensions of biodiversity. The models that make community composition forecasts are calibrated with data on species' geographic patterns for the present, which may not be good proxies for future patterns. The future establishment of novel communities represents data on species interactions unaccounted for by these models. However, detecting them in a systematic way presents challenges due to the lack of monitoring data for landscapes with high environmental turnover, where such communities are likely to establish. Here, we propose lightweight monitoring over both ecological and anthropogenic disturbance gradients using passive sensors (i.e., those that operate continuously without much human input) to detect novel communities with the aim of updating models that make community composition forecasts. Monitoring over these two gradients should maximize detection of novel communities and improve understanding of relationships between community composition and environmental change. Further, barriers regarding cost and effort are reduced by using relatively few sensors requiring minimal upkeep. Ongoing updates to community composition forecasts based on novel community data and better understanding of the associated uncertainty should improve future decision-making for both resource management and conservation efforts.

Original languageEnglish
Pages (from-to)466-470
Number of pages5
JournalEcological Research
Volume37
Issue number4
DOIs
Publication statusPublished - Jul 2022

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics

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