Predicting Multivariate Abundances

Warton, David I.

doi:10.1007/978-3-030-88443-7_15

Cited by 11 publications

(15 citation statements)

References 18 publications

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“…If dependence is not correctly accounted for, it will lead to incorrect inference (often underestimating uncertainty). Modelling common dependence types is straightforward in most statistical software (see Warton, 2022 for a detailed guide; and the mixed, multilevel and hierarchical models in R CRAN Task View; Bolker et al., 2022, for a comprehensive list of models). Briefly, generalised linear (mixed) models (McCulloch & Neuhaus, 2006), implemented in the lme4 (Bates et al., 2015, p. 4) package, can model dependence due to clustering by including a random intercept for the clustering variable (~ (1|mesocosm)).…”

Section: Principle 2 Develop a Model That Accounts For The Distributi...mentioning

confidence: 99%

Four principles for improved statistical ecology

Popovic,

Mason,

Drobniak

et al. 2024

Methods Ecol Evol

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Increasing attention has been drawn to the misuse of statistical methods over recent years, with particular concern about the prevalence of practices such as poor experimental design, cherry picking and inadequate reporting. These failures are largely unintentional and no more common in ecology than in other scientific disciplines, with many of them easily remedied given the right guidance. Originating from a discussion at the 2020 International Statistical Ecology Conference, we show how ecologists can build their research following four guiding principles for impactful statistical research practices: (1) define a focussed research question, then plan sampling and analysis to answer it; (2) develop a model that accounts for the distribution and dependence of your data; (3) emphasise effect sizes to replace statistical significance with ecological relevance; and (4) report your methods and findings in sufficient detail so that your research is valid and reproducible. These principles provide a framework for experimental design and reporting that guards against unsound practices. Starting with a well‐defined research question allows researchers to create an efficient study to answer it, and guards against poor research practices that lead to poor estimation of the direction, magnitude, and uncertainty of ecological relationships, and to poor replicability. Correct and appropriate statistical models give sound conclusions. Good reporting practices and a focus on ecological relevance make results impactful and replicable. Illustrated with two examples—an experiment to study the impact of disturbance on upland wetlands, and an observational study on blue tit colouring—this paper explains the rationale for the selection and use of effective statistical practices and provides practical guidance for ecologists seeking to improve their use of statistical methods.

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Section: Principle 2 Develop a Model That Accounts For The Distributi...mentioning

confidence: 99%

Four principles for improved statistical ecology

Popovic,

Mason,

Drobniak

et al. 2024

Methods Ecol Evol

View full text Add to dashboard Cite

show abstract

“…Second, we allowed detection probability to vary with the availability of the other three nonforested landcover types. To accommodate both these assumptions, we fit an abundance model with an offset term for the size of the area (in hectares) that was nonforested in each plot (Warton 2022). We then modelled the effect of additive log-ratio transformations of the meadow and deadfall cover types on detection using the unvegetated cover type as the common denominator in additive log-ratio transformations.…”

Section: Hierarchical Model Of Abundance Accounting For Imperfect Det...mentioning

confidence: 99%

Estimating true density in large, alpine herbivores using Google Earth imagery

Christianson

Winnie

2023

Wildlife Biology

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Density is a fundamental ecological parameter that is often difficult to estimate in large herbivores due to the wide spatial extent of their populations and imperfect detectability of individuals. We searched Google Earth imagery using an independent doubleobserver protocol to estimate elk Cervus elaphus density in Yellowstone National Park, WY, USA, on summer range that had never been systematically surveyed. Detectability of elk was high at 0.836 (95% CRI: 0.769, 0.909) producing high precision in our density estimates. Elk density estimates ranged from a minimum of 8.9 elk km −2 over a 214.8 km 2 search area (95% CRI: 7.9, 10.9) to a maximum of 14.0 elk km −2 over 98.5 km 2 of nonforested, alpine terrain (95% CRI: 13.5, 14.9). These estimates of elk density are comparable to the highest elk densities ever recorded in Yellowstone which primarily derive from winter, at low elevations, prior to wolf Canis lupus reintroduction. A log-linear positive effect of open landcover on density suggested preference for open terrain. Multiple survey designs may be possible with satellite imagery that would allow further refinement of noninvasive abundance estimation over large scales.

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“…NMDS and constrained ordination), but this approach loses information and generates artefacts (Warton et al 2012). However, for over a decade now, it has been possible to analyse multivariate abundance data directly (Warton 2022).…”

Section: What Can Jsdms and Internal Structure Analysis Bring To Edna...mentioning

confidence: 99%

Assembly processes inferred from eDNA surveys of a pond metacommunity are consistent with known species ecologies

Cai,

Pichler,

Biggs

et al. 2023

Preprint

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Metacommunity theory unites community and spatial ecology. Recent innovations in sequencing technology may now allow us, for the first time, to confront this theory with entire trophic community data at large scales. We metabarcoded vertebrate eDNA from 320 ponds in England. Using this ‘novel community data’, we calculated the ‘internal structure’ of this metacommunity, which is the contributions of environmental, biotic, and spatial processes, as well as stochasticity, to the distribution of each species and the composition of each site. Our results suggest that community assembly processes in this pondscape do not conform to a single metacommunity paradigm, but rather vary among species and over space. Combining this variation with external knowledge of species biologies allows us to better understand the differential influences of environmental filtering, species interactions, and dispersal limitation on several of the vertebrate species, paving the way for the routine empirical study of metacommunities.

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Predicting Multivariate Abundances

Cited by 11 publications

References 18 publications

Four principles for improved statistical ecology

Four principles for improved statistical ecology

Estimating true density in large, alpine herbivores using Google Earth imagery

Assembly processes inferred from eDNA surveys of a pond metacommunity are consistent with known species ecologies

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