RR-J) MC-M and RR-J conceived the experiments, MF and MC-M conducted the experiments, MC-M and RR-J analyzed the results. MC-M and RR-J wrote the manuscript. All authors reviewed and approved the manuscript.
AbstractMany freshwater ecosystems worldwide, and particularly Mediterranean ones, show increasing levels of salinity. This changes in water conditions could affect abundance and distribution of inhabiting species as well as the provision of ecosystem services. In this study we conduct laboratory experiments using the macroinvertebrate Smicridea annulicornis as a model organism. Our factorial experiments were designed to evaluate the effects of geographical origin on organisms and salinity levels on survival and behavioral responses of caddisflies. The experimental organisms were captured from rivers belonging to three hydrological basins along a 450 Km latitudinal gradient in the Mediterranean region of Chile. Animals were exposed to three conductivity levels, from 180 to 1400 µS/cm, close to the historical averages of the source rivers. We measured the behavioral responses to experimental stimulii and the survival time. Our results showed that geography origin shaped the behavioral and survival responses to salinity. In particular, survival and activity decreased more strongly with increasing salinity in organisms coming from more dilute waters. This suggests local adaptation to be determinant for salinity responses in this species of benthic invertebrate. In the current scenario of fast temporal and spatial changes in water levels and salt concentration, the conservation of geographic intra-specific variation of aquatic species is crucial for lowering the risk of salinity-driven biodiversity loss. 60 freshwater organisms are negatively affected by increased salinity [28,29]. According to 61 this hypothesis, we would expect that low-salinity adapted organisms will be more 62 July 11, 2019 2/10 130 exhibition of any given behavior, here referred as "activity" was analyzed using a model 131 which included body size, the time at measurement as well as the kind of behavioral 132 trait adopted in each activity event recorded at each experimental condition. Because 133 the behaviors were recorded as the presence or absence of the responses in discrete time 134 (count data), our variable responses swimming, sheltering, pushing-up and walking were 135 biased. Thus, to analyze the behavioral response variables, we fit Generalized Linear 136 Models (GLMs) with error family according to this characteristic (quasibinomial). Body 137 size and measurement times were used as covariates into the models to avoid under-sub 138 estimating the behavioral responses to the combination of stress conditions. All the 139 analyses were performed using R software [34].