Microbial biodiversity and plant functional trait interactions in multifunctional ecosystems

Applied Soil Ecology, Journal Year: 2024, Volume and Issue: 201, P. 105515 - 105515

Published: July 2, 2024

Language: Английский

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Biodiversity monitoring, earth observations and the ecology of scale

Ecology Letters, Journal Year: 2018, Volume and Issue: 21(10), P. 1572 - 1585

Published: July 13, 2018

Abstract Human activity and land‐use change are dramatically altering the sizes, geographical distributions functioning of biological populations worldwide, with tremendous consequences for human well‐being. Yet our ability to measure, monitor forecast biodiversity – crucial addressing it remains limited. Biodiversity monitoring systems being developed improve this capacity by deriving metrics from an array in situ data (e.g. field plots or species occurrence records) Earth observations ( EO ; e.g. satellite airborne imagery). However, there few ecologically based frameworks integrating these into meaningful change. Here, I describe how concepts pattern scale ecology could be used design such a framework. review three core topics: role measuring modelling patterns , scale‐dependent challenges linking opportunities apply mapping. From analysis emerges actionable approach measuring, forecasting change, highlighting key establish as backbone global‐scale, science‐driven conservation.

Language: Английский

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Functional traits and community composition: A comparison among community‐weighted means, weighted correlations, and multilevel models

Methods in Ecology and Evolution, Journal Year: 2018, Volume and Issue: 10(3), P. 415 - 425

Published: Nov. 1, 2018

Abstract Of the several approaches that are used to analyse functional trait–environment relationships, most popular is community‐weighted mean regressions (CWMr) in which species trait values averaged at site level and then regressed against environmental variables. Other include model‐based methods weighted correlations of different metrics associations, best known fourth‐corner correlation method. We investigated these three general statistical for associations: CWM r, five (Peres‐Neto, Dray, & ter Braak, Ecography , 40, 806–816, 2017), two multilevel models ( MLM ) using four computing p ‐values. first compared applied a plant community dataset. To determine validity conclusions, we performed simulation study. r gave highly significant associations both traits, whereas other mix support. had inflated type I errors some scenarios, implying results data could be spurious. The generally good error control but low power. One models, from Jamil, Ozinga, Kleyer, Braak Journal Vegetation Science 24, 988–1000, 2013) high power when an appropriate method was obtain In particular, if there no among their abundances sites, parametric bootstrap likelihood ratio test LRT When abundances, conditional correct lower There overall identifying associations. For simple task testing between single variables with permutation tests all control, ease implementation advantage. more complex multivariate analyses model fitting, needed, recommend 2 (Jamil et al., 2013). However, care must taken ensure MLM2. Because exhibited rates, it should always avoided.

Language: Английский

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Dos and don'ts when inferring assembly rules from diversity patterns

Global Ecology and Biogeography, Journal Year: 2020, Volume and Issue: 29(7), P. 1212 - 1229

Published: April 1, 2020

Abstract Aim More than ever, ecologists seek to understand how species are distributed and have assembled into communities using the “filtering framework”. This framework is based on hypothesis that local assemblages result from a series of abiotic biotic filters applied regional pools these leave predictable signals in observed diversity patterns. In theory, statistical comparisons expected patterns enable data‐driven tests assembly processes. However, so far this has fallen short delivering generalizable conclusions, challenging whether (and how) can be used characterize underlying processes better. Methods By synthesizing previously raised critiques suggested solutions comprehensive way, we identify 10 pitfalls lead flawed interpretations α‐diversity patterns, summarize developed circumvent provide general guidelines. Results We find most issues arise an overly simplistic view potential influence which often motivated by practical constraints study design, focal scale methodology. outline for each pitfall, such as methods spanning over spatial, environmental or phylogenetic scales, suggest guidelines best scientific practices community ecology. Among key future challenges integration mechanistic modelling multi‐trophic interactions. Main conclusions Our conclusion filtering still holds promise, but only if researchers successfully navigate major pitfalls, foster interactions directly account uncertainty their conclusions.

Language: Английский

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Trait-based modelling in ecology: A review of two decades of research

Ecological Modelling, Journal Year: 2019, Volume and Issue: 407, P. 108703 - 108703

Published: July 5, 2019

Language: Английский

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