Abstract
Biomonitoring programs need to balance accurate responses in assessments of changes in biological communities with sampling that is fast and low cost. In this study, we evaluated the concordance among fish and phytoplankton communities of streams. We tested the cross-taxa surrogacy, taxonomic, numerical resolution and ecological substitute group (habitat use and trophic guilds) resolution with Procrustes analyses aim of simplifying the biomonitoring process. We collect a total fish abundance of 8,461 individuals, represented by the ecological classes of habitat, including benthic, nektonic, nektobenthic, marginal and trophic guilds by detritivore, terrestrial invertivore, aquatic invertivore, piscivore, algivore and herbivore. We sampled a phytoplankton total density of 1,466.68 individuals/ml, represented by four Morphology-Based Functional Groups and nine Reynolds Functional Groups. Our results don’t support the use of substitute groups among fish and phytoplankton. For fish, habitat use and trophic guild are good surrogates for species-level data. Additionally, our results don’t support the use of functional groups as surrogates for phytoplankton. We suggest the use of higher taxonomic levels (genus and family) and record only the occurrence of species and/or genus for fish and phytoplankton. Our findings contribute to decreasing the costs and time of biomonitoring programs assessments and/or conservation plans on fish and phytoplankton communities of headwater streams.
Highlights
IntroductionWe experience an accelerated global loss of species (Ceballos et al 2015, 2017; Crist et al 2017)
Environmental changes are primarily caused by anthropogenic drivers
We evaluated the concordance of the spatial ordering among fish and phytoplankton communities of streams through four strategies with the aim of simplifying the biomonitoring process
Summary
We experience an accelerated global loss of species (Ceballos et al 2015, 2017; Crist et al 2017). In this sense, it is urgent to develop strategies to understand and monitor the consequences of environmental changes on biodiversity (Maćkiewicz et al 2018). Financial and human resources used for these activities are often limited. These resources require optimizing sampling protocols that are less expensive yet do not compromise the quality of information (Kallimanis et al 2012; Maćkiewicz et al 2018)
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