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The effect of single biome occupancy on the estimation of biome shifts and the detection of biome conservatism.

ABSTRACT: Biome conservatism is often regarded as common in diversifying lineages, based on the detection of low biome shift rates or high phylogenetic signal. However, many studies testing biome conservatism utilise a single-biome-per-species approach, which may influence the detection of biome conservatism. Meta-analyses show that biome shift rates are significantly lower (less than a tenth), when single biome occupancy approaches are adopted. Using New Zealand plant lineages, estimated biome shifts were also significantly lower (14-67% fewer biome shifts) when analysed under the assumption of a single biome per species. Although a single biome approach consistently resulted in lower biome shifts, it detected fewer instances of biome conservatism. A third of clades (3 out of 9) changed status in biome conservatism tests between single and multiple biome occupancy approaches, with more instances of significant biome conservatism when using a multiple biome occupancy approach. A single biome approach may change the likelihood of finding biome conservatism because it assumes biome specialisation within species, falsely recognises some biome shift types and fails to include other biome shift types. Our results indicate that the degree of biome fidelity assumed has a strong influence on analyses assessing biome shift rates, and biome conservatism testing. We advocate analyses that allow species to occupy multiple biomes.

SUBMITTER: Dale EE

PROVIDER: S-EPMC8009365 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Multispecies occupancy models can estimate species richness from spatially replicated multispecies detection/non-detection survey data, while accounting for imperfect detection. A model extension using data augmentation allows inferring the total number of species in the community, including those completely missed by sampling (i.e., not detected in any survey, at any site). Here we investigate the robustness of these estimates. We review key model assumptions and test performance via simulations, under a range of scenarios of species characteristics and sampling regimes, exploring sensitivity to the Bayesian priors used for model fitting. We run tests when assumptions are perfectly met and when violated. We apply the model to a real dataset and contrast estimates obtained with and without predictors, and for different subsets of data. We find that, even with model assumptions perfectly met, estimation of the total number of species can be poor in scenarios where many species are missed (>15%-20%) and that commonly used priors can accentuate overestimation. Our tests show that estimation can often be robust to violations of assumptions about the statistical distributions describing variation of occupancy and detectability among species, but lower-tail deviations can result in large biases. We obtain substantially different estimates from alternative analyses of our real dataset, with results suggesting that missing relevant predictors in the model can result in richness underestimation. In summary, estimates of total richness are sensitive to model structure and often uncertain. Appropriate selection of priors, testing of assumptions, and model refinement are all important to enhance estimator performance. Yet, these do not guarantee accurate estimation, particularly when many species remain undetected. While statistical models can provide useful insights, expectations about accuracy in this challenging prediction task should be realistic. Where knowledge about species numbers is considered truly critical for management or policy, survey effort should ideally be such that the chances of missing species altogether are low.

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The effect of single biome occupancy on the estimation of biome shifts and the detection of biome conservatism.

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