A heuristic approach to predicting water beetle diversity in temporary and fluctuating waters

Abstract

a b s t r a c tAn understanding of the causal mechanisms and processes that shape macroinvertebrate communities at a local scale has important implications for the management and conservation of freshwater biodiversity. Here we compare the performance of linear and non-linear statistics to explore diversity-environment relationships using data from 76 temporary and fluctuating ponds in two regions of southern England. We focus on aquatic beetle assemblages, which have been shown to be excellent surrogates of wider freshwater macroinvertebrate diversity. Ponds in the region contained a rich coleopteran fauna, totaling 68 species, which provided an excellent model system with which to compare the performance of two nonlinear procedures (artificial neural networks-ANNs and generalised additive models-GAMs) and one more traditional linear approach (Multiple linear regression-MLR) to modelling diversity-environment relationships. Of all approaches employed, the best fit was obtained using an ANN model with only four input variables (conductivity, turbidity, magnesium concentration and depth). This model accounted for 82% of the observed variability in Shannon diversity index across ponds. In contrast, the best GAM and MLR models only explained 50% and 14% of this variation, respectively. Contribution profile analysis of conductivity, turbidity, magnesium concentration and depth, obtained from the best fit ANN through a hierarchical cluster analysis, allowed the identification of direct and proxy effects in relation to the environmental variables measured in this study. In each case, distinct clusters of ponds were identified in contribution profile analysis, suggesting that ponds across the two regions fall into a number of discrete groups, whose beetle faunas respond in subtly yet significantly different ways to key environmental variables. Aquatic coleopteran diversity in ponds in the two regions appears to be driven at a local scale by changes in relatively few physicochemical gradients, which are related to diversity in a clearly non-linear manner.

Long‐term monitoring reveals decreasing water beetle diversity, loss of specialists and community shifts over the past 28 years

“…We found a positive correlation of pH and conductivity (Supporting Information Table S3), and at the same time decreasing abundances and species richness with increasing conductivity (Supporting Information S3). This is in line with the above outlined, corresponding effects of detritus on pH, but is in contrast to the observations of some studies in which rising conductivity was shown to have a positive effect on species richness (Eyre et al ., ; Heino, ; Bilton et al ., ), at least up to a threshold of around 600 μS (Gutiérrez‐Estrada & Bilton, ). Our results suggest that water bodies with lower abundances of water beetles and fewer species had conductivity levels exceeding this 600 μS threshold; however, there was no such clear threshold in our data set.…”

Assessing the ecological integrity of endorheic wetlands, with focus on Mediterranean temporary ponds

“…Since it has been shown for some groups (e.g. Coleoptera) that considering only higher taxonomic levels already allows the monitoring of water bodies (Criado and Alaez, 1995;Oertli et al, 2005a;Gutiérrez-Estrada and Bilton, 2010), detailed species identifications, which are often difficult and time-consuming, may not always be necessary. Nevertheless, if detailed species identifications are required, we consider DNA barcoding as a potential tool to overcome identification difficulties, such as shown for example by Hajibabaei et al (2011) for benthic freshwater macroinvertebrates.…”

Application of receptor models on water quality data in source apportionment in Kuantan River Basin

“…Many study emphasizes the comparison between model result in good prediction performance of ANN, whilst the used of linear models are of the less efficient model that might attribute to the non-linearity of the data sets (Thompson et al , [12]; Chaloulakou et al , [9]; Bandyopadhyay and Chattopadhyay, [7]; Sousa et al , [8]; Jahandideh et al , [10]; Gutierrez-Estrada and Bilton, [13]; Rossel and Behrens, [14]; Wu et al , [15]). ANNs are greatly suited to dynamic non linear system modelling (Mirsepassi, [16]) and have advantages over conventional simulation methods have been discussed in detail by French et al , [17].…”