Understanding Landscape Influences on Aquatic Fauna across the Central and Southern Appalachians

R Daniel Hanks,Paul B Leonard,Robert F Baldwin

doi:10.3390/land9010016

R Daniel Hanks, Paul B Leonard + Show 1 more

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https://doi.org/10.3390/land9010016

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Abstract

For the success of aquatic conservation efforts, it is imperative for there to be an understanding of the influences multiple stressors across the landscape have on aquatic biota, as it provides an understanding of spatial patterns and informs regional stakeholders. The central and southern Appalachians contain biodiversity hotspots for aquatic fauna. Therefore, we sought to create a comprehensive multimetric model that is based on the influence of abiotic factors on fish and aquatic macroinvertebrates that could predict watershed quality. Good spatial coverage exists for land use/land cover (LULC) and other physicochemical components throughout the region, yet biological data is unevenly distributed, which creates difficulties in making informed management and conservation decisions across large landscapes. We used boosted regression trees (BRT) to model a variety of biological responses (fish and aquatic macroinvertebrate variables) to abiotic predictors and by combining model outputs created a single score for both abiotic and biotic values throughout the region. The mean variance that was explained by BRT models for fish was 73% (range = 48–85%) and for aquatic macroinvertebrates was 81% (range = 76–89%). We categorized both predictor and response variables into themes and targets, respectively, to better understand large scale patterns on the landscape that influence biological condition of streams. The most important themes in our models were geomorphic condition for fish and water quality for aquatic macroinvertebrates. Regional models were developed for fish, but not for aquatic macroinvertebrates due to the low number of sample sites. There was strong correlation between regional and global watershed scores for fish models but not between fish and aquatic macroinvertebrate models. We propose that the use of such multimetric scores can inform managers, NGOs, and private land owners regarding land use practices, thereby contributing to large landscape scale conservation efforts.

Highlights

Aquatic biodiversity is declining faster than terrestrial biodiversity globally and one of the contributing factors is the condition of the landscapes in which aquatic systems are embedded, coupled with the aquatic environments themselves [1,2]
We present the results where separate models were run for each Freshwater Ecoregion. We refer to these models as regional models and, because there were not enough data points within the regional data sets for aquatic macroinvertebrates, they were developed for fish only
We developed separate fish and aquatic macroinvertebrate steam condition models highlighting the existence of both similarities and differences in how environmental factors structure these two aquatic taxonomic communities while using boosted regression trees (BRT)

Summary

Introduction

Aquatic biodiversity is declining faster than terrestrial biodiversity globally and one of the contributing factors is the condition of the landscapes in which aquatic systems are embedded, coupled with the aquatic environments themselves [1,2]. There is a need to understand the drivers of this decline across a geography of concern in order to reverse or stabilize this trend and conserve aquatic species and ecosystems (e.g., the central and southern Appalachians). Biological sampling efforts can be geographically extensive (e.g., National Fish Habitat Partnership, state agency sampling, etc.), yet it is intractable to consider biological sampling of every stream segment or small watershed across large geographies. Land 2020, 9, 16 physical, chemical, and biological influences that affect the population and community condition, model those effects, and extend predictions to unsampled stream segments or watersheds. Assessing the biological condition of rivers and streams has been performed at various spatial scales and grains. As data availability and computing power both increase, the ability to perform spatial analysis across large geographic extents and at small grain size is increasing. Examples of large scale spatial analyses include Esselman et al [3], who used fish as indicators, the US Environmental

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