Effect of landscape factors on fish distribution in arctic Alaskan lakes

Abstract

1. The distribution of species is affected by many factors operating at a variety of temporal and spatial scales in a heterogeneous landscape. In lakes, fish communities are dynamic, influenced by landscape-level factors that control colonisation and extinction. 2. We used classification and regression tree (CART) analyses to quantify the importance of landscape-level factors in determining the distribution of fish species in 168 arctic Alaskan lakes. Factors including lake size, depth, outflow gradient, distance to other lakes, lake order, altitude, river drainage and age of glacial surface were analysed. These factors could affect either access of fish to a lake (colonisation variables), or their survival in a lake that already had been colonised (extinction variables). 3. The presence of a species was predicted accurately in 78.4% ± 10.5% (mean ± SD) of cases, and absence in 75.0% ± 6.1% of cases. The relative importance of extinction versus colonisation variables varied with species. Extinction variables were most important for lake trout (Salvelinus namaycush) and slimy sculpin (Cottus cognatus), a mixture of extinction and colonisation variables was important for arctic char (Salvelinus alpinus), and colonisation variables were most important for arctic grayling (Thymallus arcticus) and round whitefish (Prosopium cylindraceum). 4. Ecological differences among species account for much of the difference in relative importance of colonisation versus extinction variables. In addition, stream piracy events have occurred over geologic time scales, which have resulted in lakes that are currently inaccessible but support relict fish populations. 5. Climate warming, currently occurring in the arctic, is likely to alter further the stream network, which could have dramatic effects on fish distributions by affecting access to isolated lakes or isolating lakes that are currently accessible.

Using change-point analysis and weighted averaging approaches to explore the relationships between common benthic diatoms and in-stream environmental variables in mid-atlantic highlands streams, USA

“…Transformation of relative abundance data into categories and subsequent CT analysis might provide an alternative to RT for less common species whose relative abundance might not be well-characterized by the fixed count methodologies commonly employed in diatom studies. Our CT analyses had misclassification rates comparable to CT of fish abundance (misclassification rate 22-25%; Hershey et al, 2006) and tree species (misclassification rate 20-71%; Iverson & Prasad, 1998). N. dissipata, N. palea, and R. sinuata have low ranges in relative abundance and were found at less than half of the sites in the study.…”

“…Diatom autecology would benefit from approaches that allow interactions between variables to be explicitly incorporated. Increasingly, regression tree approaches have been used to examine speciesenvironmental relationships in plants and animals (e.g., Iverson & Prasad, 1998;De'ath & Fabricius, 2000;O'Conner & Wagner, 2004;Hershey et al, 2006). Regression trees (RT) and classification trees (CT) are useful for visually facilitating interpretation, revealing data structures, and displaying interactions (Clark & Pregibon, 1993;De'ath & Fabricius, 2000).…”

Prediction of stream fish assemblages from land use characteristics: implications for cost-effective design of monitoring programmes

“…Classification trees were then used to predict the occurrence of the stream fish assemblages identified with cluster analyses using the land use, landscape and in-stream variables described above. Classification trees are a powerful method to analyse complex ecological data (De'ath and Fabricius 2000) and have recently been used to describe the distribution of individual fish species in arctic lakes (Hershey et al 2006) and in Michigan streams (Steen et al 2008). The library rpart in S-Plus (Insightful Corp.) using untransformed response and predictor variables was used to generate the classification trees, and separate models were developed for each of the four spatial scales (50 m riparian corridor, 500 m riparian corridor, the entire catchment and in-stream variables) and for the two stream types.…”

The Interplay Between Landscape Structure and Biotic Interactions

“…Fish community composition in lake regions can be strongly influenced by landscape structure: in Arctic Alaska, the dominant competitor, lake trout (Salvelinus namaycush), only naturally occurs in well-connected lakes. It is likely that arctic char (Salvelinus alpinus) have been able to dominate in some isolated lakes because they are landlocked, relict populations that are inaccessible to competitive lake trout [89].…”