Food web properties vary with climate and land use in South African streams

Abstract

1. Land use intensification and climate change are two prominent drivers of variation in biological communities. However, we know very little about how these two potential environmental stressors interact. Here we use a stable isotope approach to quantify how animal communities respond to urban and agriculture land use, and to latitudinal variation in climate (rainfall and temperature), in 29 streams across South Africa.2. Community structure was shaped by both land use and climatic factors. The taxonomic diversity of invertebrates was best explained by an independent negative effect of urbanization, while abundance declined in summer. However, we could not use our variables to predict fish diversity (suggesting that other factors may be more important).3. Both trophic functional diversity (quantifed using isotopic richness) and food chain length declined with increasing temperature. Functional redundancy (quantifed using isotopic uniqueness) in the invertebrate community was high in wet areas, and a synergistic interaction with urbanization caused the lowest values in dry urban regions. There was an additive effect of agriculture and rainfall on abundance-weighted vertebrate functional diversity (quantified using isotopic dispersion), with the former causing a decline in dispersion, with this partially compensated for by high rainfall. 4. In most cases, we found that a single dominant driver (either climate or land use) explained variation between streams. We only found two incidences of combined effects improving the model, one of which was amplified (i.e. the drivers combinedThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Proposed approaches to systematic planning of research and monitoring to support a South African inland fisheries policy

“…A1). Longterm monitoring could reveal negative or positive responses to global warming, depending on precipitation patterns and species' thermal tolerances (Dallas and Rivers-Moore, 2014;Paukert et al, 2017;Jackson et al, 2020;Kao et al, 2020). Although dam surface area is likely to be important in practice, e.g., due to variation in total fish population size, dam area was not included in any models and is likely unimportant when selecting dams for a longterm monitoring programme.…”

“…The current study revealed groups of dams with shared climatic characteristics that seem to associate with certain primary catchments due to the strong effect of the escarpment (Figs 1 and 2A). Although the gillnet models cannot generalise CPUE estimates to unsampled dams, these climate clusters roughly indicate dams where certain fish species could thrive, or dams where fish populations should be monitored for vulnerability to climate change (Britz et al, 2015;Jackson et al, 2020;Kao et al, 2020). Dominant species' CPUE estimates broadly match the climate clusters, with comparatively higher CPUE for L. aeneus and L. capensis associated with the relatively cooler Cluster 1, whereas O. mossambicus are largely present in Cluster 4 where maximum temperature is higher (Table 2, Fig.…”

“…However, dams likely influence and are influenced by a larger surrounding region (e.g. a river drainage basin or primary catchment, Walmsley, 2002;Jooste et al, 2014;Jackson et al, 2020). Therefore, the current study also examined the available data in relation to South Africa's primary catchments, specifically whether land use characteristics surrounding dams are comparable to general conditions in the catchments and, consequently, whether catchments are useful units within which to organise fishery management.…”

“…Rivers-Moore et al, 2011 used GIS climate data and expert scores to identify river basins suitable for stocking with fishery species. However, there is a further need to quantify variation in human land use and climate, which may vary in their relative importance or have synergistic effects on fish populations (Dallas and Rivers-Moore, 2014;Camp et al, 2020;Jackson et al, 2020;Kao et al, 2020). An assessment of the characteristics of a large number of dams covering a large geographic area complements both random stratified and model-based survey design by evaluating the full range of variation in climatic and human-related covariates that need to be represented by selected sampling sites (Peel et al, 2012;Baker et al, 2019).…”

Warming and Predation Alter Consumer Coexistence

“…The effects of warming and predation were considered additive if models excluding their interaction term had lower AIC values. For the nonadditive interactions, interaction effect sizes (F-values) were subtracted from the sum of the main effect sizes producing values representing the difference between additive and interactive effects, following previous studies (Jackson et al 2020; Lakeman-Fraser and Ewers 2014). If the value was positive, then the relationship was considered to be antagonistic (the effect of the drivers were reduced when acting interactively); if however, the value was negative, then the relationship was considered to be synergistic (the effect of the drivers were increased when acting interactively) (Table S1).…”