Gonzalo Sotomayor , Henrietta Hampel , Raúl F. Vázquez , Marie Anne Eurie Forio , Peter L.M. Goethals
{"title":"底栖大型无脊椎动物功能多样性与河流生境质量:关键生物学性状分类","authors":"Gonzalo Sotomayor , Henrietta Hampel , Raúl F. Vázquez , Marie Anne Eurie Forio , Peter L.M. Goethals","doi":"10.1016/j.ecoinf.2025.103235","DOIUrl":null,"url":null,"abstract":"<div><div>Functional diversity (FD) calculations using benthic macroinvertebrates are useful for freshwater ecosystem evaluation. However, it is critical to determine the key traits and their categories that shape a community. This study (i) investigated the effect of fluvial habitat quality (characterised by a fluvial habitat index – FHI) on the trends of individual functional macroinvertebrate categories (FMaCs) and the rRao FD index; and (ii) evaluated the information provided by each FMaC for rRao index calculation along the FHI gradient. Macroinvertebrate samples were collected at 12 locations in Ecuador's Paute River Basin over six years. Families of macroinvertebrates were classified into eight traits and 42 FMaCs. A K-means cluster analysis produced three groups of sampling points based on their FHI values. For each FHI cluster, the percentage of each FMaC within its corresponding trait was calculated. The R<sup>2</sup> coefficient was computed between the FHI cluster values and the previously obtained FMaC percentages. A second K-means clustering was performed on the R<sup>2</sup> dataset, resulting in three groups of R<sup>2</sup> values directly associated with FMaCs. We then assessed the sensitivity of the rRao index to the exclusion of specific trait categories by sequentially removing groups of FMaCs, ordered by decreasing R<sup>2</sup> importance. This allowed us to evaluate the stability and robustness of functional diversity estimates when less informative traits were removed. Results indicated that certain FMaCs had a greater influence on rRao variation across habitat quality clusters, particularly those related to body form, locomotion, and exoskeleton hardness. In degraded habitats, certain FMaCs contributed little to rRao variation, suggesting limited functional differentiation within the multi-trait functional space and potentially lower monitoring value under such conditions. The most informative traits for rRao index calculation were body form, flexibility, and locomotion. These findings contribute to improved trait-based ecological modelling of macroinvertebrates and offer insights for river managers regarding potential ecohydrological stressors.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103235"},"PeriodicalIF":7.3000,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Functional diversity of benthic macroinvertebrates and fluvial habitat quality: Key biological trait categories\",\"authors\":\"Gonzalo Sotomayor , Henrietta Hampel , Raúl F. Vázquez , Marie Anne Eurie Forio , Peter L.M. Goethals\",\"doi\":\"10.1016/j.ecoinf.2025.103235\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Functional diversity (FD) calculations using benthic macroinvertebrates are useful for freshwater ecosystem evaluation. However, it is critical to determine the key traits and their categories that shape a community. This study (i) investigated the effect of fluvial habitat quality (characterised by a fluvial habitat index – FHI) on the trends of individual functional macroinvertebrate categories (FMaCs) and the rRao FD index; and (ii) evaluated the information provided by each FMaC for rRao index calculation along the FHI gradient. Macroinvertebrate samples were collected at 12 locations in Ecuador's Paute River Basin over six years. Families of macroinvertebrates were classified into eight traits and 42 FMaCs. A K-means cluster analysis produced three groups of sampling points based on their FHI values. For each FHI cluster, the percentage of each FMaC within its corresponding trait was calculated. The R<sup>2</sup> coefficient was computed between the FHI cluster values and the previously obtained FMaC percentages. A second K-means clustering was performed on the R<sup>2</sup> dataset, resulting in three groups of R<sup>2</sup> values directly associated with FMaCs. We then assessed the sensitivity of the rRao index to the exclusion of specific trait categories by sequentially removing groups of FMaCs, ordered by decreasing R<sup>2</sup> importance. This allowed us to evaluate the stability and robustness of functional diversity estimates when less informative traits were removed. Results indicated that certain FMaCs had a greater influence on rRao variation across habitat quality clusters, particularly those related to body form, locomotion, and exoskeleton hardness. In degraded habitats, certain FMaCs contributed little to rRao variation, suggesting limited functional differentiation within the multi-trait functional space and potentially lower monitoring value under such conditions. The most informative traits for rRao index calculation were body form, flexibility, and locomotion. 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Functional diversity of benthic macroinvertebrates and fluvial habitat quality: Key biological trait categories
Functional diversity (FD) calculations using benthic macroinvertebrates are useful for freshwater ecosystem evaluation. However, it is critical to determine the key traits and their categories that shape a community. This study (i) investigated the effect of fluvial habitat quality (characterised by a fluvial habitat index – FHI) on the trends of individual functional macroinvertebrate categories (FMaCs) and the rRao FD index; and (ii) evaluated the information provided by each FMaC for rRao index calculation along the FHI gradient. Macroinvertebrate samples were collected at 12 locations in Ecuador's Paute River Basin over six years. Families of macroinvertebrates were classified into eight traits and 42 FMaCs. A K-means cluster analysis produced three groups of sampling points based on their FHI values. For each FHI cluster, the percentage of each FMaC within its corresponding trait was calculated. The R2 coefficient was computed between the FHI cluster values and the previously obtained FMaC percentages. A second K-means clustering was performed on the R2 dataset, resulting in three groups of R2 values directly associated with FMaCs. We then assessed the sensitivity of the rRao index to the exclusion of specific trait categories by sequentially removing groups of FMaCs, ordered by decreasing R2 importance. This allowed us to evaluate the stability and robustness of functional diversity estimates when less informative traits were removed. Results indicated that certain FMaCs had a greater influence on rRao variation across habitat quality clusters, particularly those related to body form, locomotion, and exoskeleton hardness. In degraded habitats, certain FMaCs contributed little to rRao variation, suggesting limited functional differentiation within the multi-trait functional space and potentially lower monitoring value under such conditions. The most informative traits for rRao index calculation were body form, flexibility, and locomotion. These findings contribute to improved trait-based ecological modelling of macroinvertebrates and offer insights for river managers regarding potential ecohydrological stressors.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.