1. The impact of agricultural activities on waterways is a global issue, but the magnitude of the problem is often not clearly recognized by landowners, and land and water management agencies. 2. The Pomahaka River in southern New Zealand represents a typical lowland catchment with a long history of agricultural development. Fifteen sites were sampled along a 119‐km stretch of the river. Headwater sites were surrounded by low‐intensity sheep farming, with high‐intensity pasture and dairying occurring in the mid‐reach and lower reaches. 3. Water clarity decreased significantly from about 6 m in the headwaters to less than 2 m in the lower reaches. Benthic sediment levels increased significantly downriver, peaking at 35 mg m−−2 below several tributaries with high‐intensity agriculture in their catchments. Periphyton levels were also significantly greater in the lower reaches than the headwaters, and coincided with increased nitrogen (DIN) and phosphorus (SRP) concentrations. 4. Macro‐invertebrate species richness did not change significantly throughout the river, but species composition did with Ephemeroptera, and to a lesser extent, Plecoptera and Trichoptera dominating the headwater sites (where there was high water clarity, and low nutrient and periphyton levels). Downriver these assemblages were replaced by molluscs, oligochaetes and chironomids. 5. Canonical correspondence analysis indicated that agricultural intensity and physical conditions associated with agriculture activity (e.g. impacted waters, high turbidity and temperature) were strongly associated with the composition of benthic assemblages at differing reaches down the Pomahaka River. 6. The present results indicate that quantifying agricultural intensity within a catchment, particularly relative livestock densities, may provide a useful tool for identifying threshold levels above which river health declines.

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We developed and tested a combined foraging and bioenergetics model for predicting growth over the lifetime of drift-feeding brown trout. The foraging component estimates gross energy intake within a fish-and prey size-dependent semicircular foraging area that is perpendicular to the flow, with options for fish feeding across velocity differentials. The bioenergetics component predicts how energy is allocated to growth, reproduction, foraging costs, and basal metabolism. The model can reveal the degree to which growth is limited by the density and size structure of invertebrate drift within the physiological constraints set by water temperature. We tested the model by predicting growth based on water temperature and on drift density and size structure data from postemergence to age 12, and we compared the predictions with observed size at age as determined from otoliths and scales for a New Zealand river brown trout population. The model produced realistically shaped growth curves in relation to the observed data, accurately predicting mean size at age over the lifetime of the trout, assuming 24-h maximum rations and including diurnal drift-foraging costs (predicted versus observed weight r 2 ϭ 0.94; length r 2 ϭ 0.97). The model predicted that, within a given water-temperature regime, growth is limited primarily by reproduction costs but also by increasing foraging costs as trout grow (a phenomenon that is associated with the increasing foraging time that is required in order to feed to satiation on small invertebrate drift prey). Invertebrate drift size structure significantly influenced predicted growth, especially maximum size, through its effect on foraging time. The model has potential in terms of the exploration of growth-limiting factors and has associated use as an environmental-impact tool and as an aid for hypothesis generation in studies of salmonid growth processes.

We examined summer habitat use by 189 drift‐feeding brown trout Salmo trutta, 45–65 cm long (fork length), by measuring substrate, depth, mean velocity, focal point velocity use, and adjacent velocity into which fish were feeding in three New Zealand rivers. We compared habitat used with habitat available (simulated by hydraulic modeling), and we derived habitat use, habitat preference, and logistic regression models of habitat selection. Focal points usually were associated with large substrate components including bedrock projections, boulders, and large cobbles. Depths between 0.67 and 0.86 m were most commonly used by brown trout but the preferred depth was 1.0 m. Mean velocities between 0.38 and 0.48 m/s were the most commonly used, but preferred mean velocities were 0.05 m/s higher. Optimal focal point velocities, 0.19–0.28 m/s, were lower than mean velocities. Vertical and lateral velocity shears were calculated as measures of the velocity differentials over which fish were feeding. The velocity shears most commonly used by brown trout were between 0.50 and 0.65 m/s per meter for vertical shear and 0.02 and 0.06 m/s per meter for lateral shear. Those most preferred by brown trout were between 0.50 and 1.20 m/s per meter for vertical shear and between 0.06 and 0.26 m/s per meter for lateral shear. Depth, mean velocity, and substrate were selected independently of each other. Significant differences between rivers were found for the use of substrate, depth, focal point velocity, mean velocity, and lateral velocity shear. Depth and mean velocity were consistently significant variables in logistic regression models of habitat selection, accounting for 33–85% of the explained deviances. Large substrate components accounted for 47% of the explained deviance in one river. Lateral and vertical velocity shear together accounted for 15–27% of the explained deviances. Logistic regression and joint habitat preference models were better predictors of suitable habitat in the rivers for which they were developed than were joint habitat use models. However, joint habitat use models may be more general, because, unlike logistic regression and joint habitat preference models, the predictive success of transferred joint habitat use models was similar to that of models tested on the river for which they were developed. Combined‐river habitat use, habitat preference, and logistic regression models are presented for general habitat management applications when habitat criteria are not available for specific rivers.