Characterisation of false-positive observations in botanical surveys

Groom, Quentin; Whild, Sarah J.

doi:10.7287/peerj.preprints.2927v1

Cited by 2 publications

(2 citation statements)

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“…Error-prone proxies (EPPs) In ecological studies, errors in the measured variables (e.g. miscounting, species misidentification and errors in digitalisation) are ubiquitous and often unavoidable (Chen et al 2013;Morrison 2016;Groom & Whild 2017). Measurement error in the predictors is what caused the false CNDD in stockrecruitment relationships.…”

Section: Discussionmentioning

confidence: 99%

Bias in the detection of negative density dependence in plant communities

et al. 2019

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Regression dilution is a statistical inference bias that causes underestimation of the strength of dependency between two variables when the predictors are error‐prone proxies (EPPs). EPPs are widely used in plant community studies focused on negative density‐dependence (NDD) to quantify competitive interactions. Because of the nature of the bias, conspecific NDD is often overestimated in recruitment analyses, and in some cases, can be erroneously detected when absent. In contrast, for survival analyses, EPPs typically cause NDD to be underestimated, but underestimation is more severe for abundant species and for heterospecific effects, thereby generating spurious negative relationships between the strength of NDD and the abundances of con‐ and heterospecifics. This can explain why many studies observed rare species to suffer more severely from conspecific NDD, and heterospecific effects to be disproportionally smaller than conspecific effects. In general, such species‐dependent bias is often related to traits associated with likely mechanisms of NDD, which creates false patterns and complicates the ecological interpretation of the analyses. Classic examples taken from literature and simulations demonstrate that this bias has been pervasive, which calls into question the emerging paradigm that intraspecific competition has been demonstrated by direct field measurements to be generally stronger than interspecific competition.

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Section: Discussionmentioning

confidence: 99%

Bias in the detection of negative density dependence in plant communities

et al. 2019

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“…This might be a property of the spatial distribution of rare species, but it may also reflect an over‐sampling of rare species. For example, if observers are more interested in observing and recording rare species than common ones or if rare species are more likely to be false‐positive observations (Groom & Whild, ). Biogeographic atlas data are rarely, if ever, a completely systematic sample and often contain spatial and taxonomic sampling biases (Dennis & Thomas, ; Rich & Woodruff, ).…”

Section: Discussionmentioning

confidence: 99%

How to predict fine resolution occupancy from coarse occupancy data

et al. 2018

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The area of occupancy (AOO) is a widely used index in conservation assessments, notably in criteria B2 of the International Union for Conservation of Nature (IUCN) red‐list. However, IUCN guidelines require assessing AOO at finer resolution than is generally available. For this reason, extrapolation techniques have been proposed to predict finer AOO from coarser resolution data. Here, we apply 10 published downscaling models to the distributions of a large number of plant and bird species' in contrasting landscapes. We further compare the output of two ensemble models, one relying on all 10 downscaling models and one a subset of five models that can be fit rapidly and robustly, with minimal oversight required. We further compare the accuracy of downscaled predictions with respect to species prevalence. Across all landscapes and taxa, the models predicted AOO consistently. Some, such as the power law and Hui models, were nonlinear with respect to species prevalence. Some models consistently over or under predicted, such as the Nachman and Poisson models. Furthermore, some models proved to give more variable predictions than other models, e.g. Nachman and power law. For these reasons, none of these models are suitable for downscaling if used individually. The Thomas model was also rejected, because it is too computationally intensive, even though its predictions are relatively unbiased. The most effective model, when used by itself, was the improved binomial model. However, the two ensemble models were able to provide accurate predictions of AOO with low variability compared to using any one single model. There was no significant loss in performance using the simpler ensemble model, and therefore this solution is the least computationally intensive and requires least user oversight. Our results show that downscaling models could be potential tools to reliably estimate AOO for conservation assessments. Under circumstances where there is no a priori reason to prefer one model over another then an ensemble of these models may be the best solution for batch analysis of IUCN status under criteria B2. Moreover, we foresee the use of downscaling for the production of other biodiversity indicators, such as for invasive species monitoring.

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Characterisation of false-positive observations in botanical surveys

Cited by 2 publications

References 1 publication

Bias in the detection of negative density dependence in plant communities

Bias in the detection of negative density dependence in plant communities

How to predict fine resolution occupancy from coarse occupancy data

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