Key words
Self-organizing map, logistic regression, small stream, land use, boreal, climate change
Introduction
Studying the relationship between species and their environment is at the core of ecology. Modelling this relationship has long been performed, using a wide array of methods (Franklin, 1995; Guisan & Zimmermann, 2000; Domisch, Jähnig, Simaika, Kuemmerlen, & Stoll, 2015). The focus in developing these models may be to study species-environment relationships or to predict the occurrence of the studied species. In fisheries research, the identification of the environmental variables that characterize fish distributions has been one of the main objectives (e.g. Nelson, Plaits, Larsen, & Jensen, 1992; Rieman & McIntyre, 1995). Predictive models may help in fish-based bioassessment (Brosse, Lek, & Townsend, 2001; Oberdorff, Pont, Hugueny, & Chessel, 2001; Oberdorff, Pont, Hugueny, & Porcher, 2002), and in focusing inventory and management activities on areas where species are considered likely to occur (Porter, Rosenfeld, & Parkinson, 2000).
Several studies have indicated that field-measured site-scale (local) variables such as stream width, water depth, water chemistry, riverbed substrate, flowrate, undercut banks, canopy cover, riparian vegetation, and the slope at the sampling site can predict the occurrence of fish species (e.g. Gorman & Karr, 1978; Watson & Hillman, 1997; Terra, Hughes, & Araujo, 2016). However, these field measurements are laborious and thus demanding for adoption as predictors of species occurrence in fisheries management, for example. An easier way to predict species occurrence would be to use large-scale map-based (regional) variables such as the size of the upper catchment, the elevation and land use in the upper catchment (Porter et al., 2000). Indeed, catchment-scale variables can have a greater impact than site-scale variables on stream fish assemblages (DeRolph, Nelson, Kwak, & Hain, 2015; Mitsuo, 2017).
The process of taking natural landscapes for human use can cause detrimental effects on terrestrial and aquatic ecosystems (Huston, 2005; Pugh, Pandolfi, Franklin, & Gangloff, 2020). For example, increased land use for agriculture, urban areas, and forestry can impact fish populations through alterations in stream hydrology, geomorphology, water quality, sedimentation, riparian vegetation, and habitat heterogeneity, eventually leading to species loss or replacement (Allan, Erickson, & Fay, 1997; Lange, Townsend, Gabrielsson, Chanut, & Matthaei, 2014; Pugh et al., 2020). Recent developments in geographical information systems (GIS) technology (Lü, Batty, Strobl, Lin, Zhu, & Chen, 2019) have facilitated easy access to a wide range of catchment characteristics above any site of a stream network. These catchment characteristics, typically expressed as the percentage coverage of the upper catchment, are extensively used in studying the effects of land use on stream biota.
About 80% of the millions of kilometers of European river networks consist of small streams, commonly known as brooks, creeks, or headwaters (Kristensen & Globevnik, 2014). Small headwater streams are important contributors to aquatic biodiversity and may suppress the negative impacts of anthropogenic stress on downstream reaches (Burdon et al., 2016; Baattrup-Pedersen, Larsen, Andersen, Jepsen, Nielsen, & Rasmussen, 2018). However, in the European Water Framework Directive (WFD; European Commission 2000), small streams with a catchment size of less than 10 km2 are mostly omitted from river basin management plans or merged into larger water bodies (Kristensen & Globevnik, 2014, Baatturp-Pedersen et al., 2018).
In this study, we chose to examine fish in small streams for some specific reasons. We inferred that in small streams/catchments, a single land-use attribute such as an urban area can easily reach high coverage, and therefore, the effect of land use on fish species occurrence should be relatively easy to trace. In small streams, the upstream catchment area is always located relatively near the sampling site, and the impact of land use should therefore be more direct. Indeed, proximity to the stream has appeared an important factor in estimating the impact of land use on stream biota (Wang, Lyons, & Kanehl, 2001). Small streams with a small volume of water also have only a limited ability to dilute pollutants such as nutrients from agriculture (Kristensen & Globevnik, 2014). Small tributary streams have appeared to be particularly sensitive to nutrient enrichment (Bussi et al., 2018). The impact of human activities is therefore potentially greater on small water bodies than on larger ones (Kristensen & Globevnik, 2014).
Our main aims in this study were (1) to explore the relationship of map-based environmental variables and the occurrence of fish species in small boreal streams; (2) extract fish species clusters and evaluate their ecological relevance; (3) study species occurrence in relation to annual mean temperature from the perspective of the climate change in this region; and (4) identify species-specific responses to man-induced pressures for the future development of diagnostic indices in bioassessment of small boreal streams.
Material and methods
Altogether, 11 environmental variables were measured (Table 1). The studied area covered Southern and Central Finland in the boreal region from about 60 o to 67 o, which are mostly covered with coniferous forest. The highest altitude among sampling sites was about 300 m in the studied territory characterized by lowlands (Table 1). The variables were map-based, with the exception of one field-collected variable, water temperature at sampling (electrofishing). Upstream catchment boundaries were delineated for each site with Geographical Information System, using the Digital Elevation Model (DEM) raster database from National Land Survey of Finland (NLS) and vector data of Drainage Basins in Finland (Finnish Environment Institute, SYKE). Only sites with a catchment area < 100 km2 were included in the study. The proportions of different land covers in the catchment areas were extracted from the CORINE Land Cover 2012 data. The quantity of forest drainage by ditching was estimated as a percentage of ditched peatlands from the drainage data of the Finnish Environment Institute. Annual air temperature and precipitation data were derived from the WorldClim database (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005).
Electrofishing data from small Finnish streams were gathered mainly from a national database (Hertta/Koekalastusrekisteri) managed by the Natural Resources Institute Finland (Luke) and hosted by the SYKE. Additional data were acquired from Metsähallitus (a state-owned enterprise responsible for the management of state-owned land and water areas). The total number of single-run electrofishing samples was 776, conducted at 487 sites, indicating that some of the sites were sampled more than once. As a rule, repeated sampling at the same site was performed at different years. Most of the sampling had been performed at the period 2000–2020. The electrofishing sites usually represented wadable riffles with stony bottoms. Escape nets were not used at any of the sampling sites, which typically covered 50–150 m2. As the electrofishing sampling had been performed in July–October, natural seasonal decline in stream water temperatures was reflected in the measured temperatures. European standard EN 14011:2003 (Water quality—sampling of fish with electricity) was followed in sampling. Fish data were converted to species presence/absence for all analyses in this study.