| 1. |
- Hallett, Lauren M., et al.
(författare)
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Restoration ecology through the lens of
- 2023
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Ingår i: Trends in Ecology & Evolution. - : CELL PRESS. - 0169-5347 .- 1872-8383. ; 38:11, s. 1085-1096
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Forskningsöversikt (refereegranskat)abstract
- Advances in restoration ecology are needed to guide ecological restoration in a variable and changing world. Coexistence theory provides a framework for how variability in environmental conditions and species interactions affects species success. Here, we conceptually link coexistence theory and restoration ecology. First, including low-density growth rates (LDGRs), a classic metric of coexistence, can improve abundance-based restoration goals, because abundances are sensitive to initial treatments and ongoing variability. Second, growth-rate partitioning, developed to identify coexistence mechanisms, can improve restoration practice by informing site selection and indicating necessary interventions (e.g., site amelioration or competitor removal). Finally, coexistence methods can improve restoration assessment, because initial growth rates indicate trajectories, average growth rates measure success, and growth partitioning highlights interventions needed in future.
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| 2. |
- Weiss-Lehman, Christopher P., et al.
(författare)
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Disentangling key species interactions in diverse and heterogeneous communities : A Bayesian sparse modelling approach
- 2022
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Ingår i: Ecology Letters. - : Wiley. - 1461-023X .- 1461-0248. ; 25:5, s. 1263-1276
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Tidskriftsartikel (refereegranskat)abstract
- Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non-generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.
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