A bioenergetic assessment of the influence of stocking practices on rainbow trout (Oncorhynchus mykiss) growth and consumption in a New Zealand lake

Abstract

Summary To investigate the carrying capacity and factors affecting growth of rainbow trout in Lake Rotoiti, we employed a bioenergetics model to assess the influence of stocking rates, timing of releases and prey abundance on growth and prey consumption. We hypothesised that stocking rates and prey abundance would affect growth and prey consumption by influencing per‐capita prey availability, and that the environmental conditions encountered by fish at the time of stocking would affect growth and consumption. Prey consumption of stocked rainbow trout was calculated with the Wisconsin bioenergetics model. We calculated growth trajectories of released trout based on data from stocked trout that were released in spring and autumn from 1993 to 2009 and then re‐captured by anglers. Diet, prey energy density, body mass lost during spawning and lake temperature were measured locally. Stocking timing had no effect on return rates to anglers or length or weight of caught fish. Although trout released in autumn were smaller than those released in spring, autumn‐released trout grew at a faster rate and had similar lengths and weights to spring cohorts after 2 years of growth in the lake. Modelled consumption parameters were negatively correlated with trout population size, suggesting that stocking rates (347–809 fish ha−1 year−1) caused density‐dependent effects on growth. Although common smelt (Retropinna retropinna) accounted for 85% of total prey consumption, no significant relationship was found between prey consumption by individual trout and adult smelt abundance, possibly because trout are targeting smaller smelt that our abundance estimate did not account for. Releasing trout in autumn appears to be advantageous for growth, possibly because (i) temperature is more suitable for growth in autumn–winter than in spring–summer and (ii) prey for small trout is abundant in autumn. Mild winter conditions appear to enhance overwinter survival and growth of rainbow trout in warm‐temperate lakes compared to higher latitudes. This implies that moderately productive warm‐temperate lake ecosystems are highly suitable for trout growth in winter, but less so in summer, when lake stratification and high nutrient levels may create conditions suitable for algal blooms and hypolimnetic deoxygenation. High growth rates of trout in warm‐temperate lakes can therefore be supported by timing releases to coincide with favourable winter conditions.

Modeling Brook Trout Carrying Capacity in Owhi Lake, Washington, Using Bioenergetics

“…; Blair et al. ). Each of these factors carries unique considerations related to the carrying capacity of the system.…”

“…; Blair et al. ), and determining how human dimensions interplay with population dynamics and prey (Anderson et al. ; Blanchfield et al.…”

“…; Blair et al. ), and identify potential predator–prey imbalances due to high predator demand (Rand et al. ; Cross et al.…”

“…Fisheries managers are often tasked with ensuring the conservation and success of local fish populations. From a broad perspective, these tasks may include controlling invasive species (Tyus and Saunders 2000;Sanderson et al 2009), balancing natural populations with stocking inputs (Berejikian et al 2008;Blair et al 2013), and determining how human dimensions interplay with population dynamics and prey (Anderson et al 2008;Blanchfield et al 2015). In many instances, managers are concerned with these issues because of potential density-dependent effects (e.g., Raborn et al 2007;Evans et al 2014).…”

“…Quantifying fish consumption demand (hereafter, "demand") is necessary to gauge the cause of prey scarcity and can be accomplished by using bioenergetics modeling (Hanson et al 1997;Beauchamp et al 2007). Bioenergetics modeling has been useful to explicate predator-prey dynamics (Denlinger et al 2006;Vatland et al 2008;Bunnell et al 2011), assess the effects of increasing predator populations (Baldwin et al 2000;Cyterski et al 2002;Blair et al 2013), and identify potential predator-prey imbalances due to high predator demand (Rand et al 1993;Cross et al 2005;Raborn et al 2007;Negus et al 2008;Evans et al 2014). After demand is estimated through modeling, it can be compared with prey biomass and production estimates to aid in explaining a system's capacity for certain populations (e.g., Hayes et al 2007).…”