A large‐scale approach can help detect general processes driving the dynamics of brown trout populations in extensive areas

Alonso, Carlos; Jalón, Diego García de; Álvarez, Javier; Gortázar, Javier

doi:10.1111/j.1600-0633.2011.00484.x

Cited by 17 publications

(13 citation statements)

References 50 publications

(67 reference statements)

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“…Dramatic fluctuations in abundance of trout have been observed previously (e.g., Alonso et al, ; Lobón‐Cerviá, ; Zorn & Nuhfer, , ). However, unlike those in Gilmore Creek, many of these fluctuations are truly cyclical when subjected to time series analyses.…”

Section: Discussionsupporting

confidence: 54%

Population dynamics of brown trout in a Minnesota (USA) stream: A 25‐year study

Mundahl

2017

River Research & Apps

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Brown trout (Salmo trutta) were surveyed by mark recapture in a 200-m section of Gilmore Creek, Minnesota, annually during fall 1989-2013 to assess long-term trends in abundance. Young-ofyear (YOY) fish comprised >68% of the population annually, but age 3 and older fish were present in 23 of 25 years. Trout abundance varied irregularly, peaking every 4 to 6 years. Fall densities of YOY brown trout were positively correlated with median annual stream discharge but inversely correlated with 10% exceedance discharge in May, at a nearby gaged stream. Changes in brown trout abundances were synchronized with those of trout in 2 nearby streams. Annual mortality rates (mean = 74%) and sizes of YOY trout were correlated with YOY densities, with high densities (>1.0 fish/m 2 ) producing small size during fall and high cohort mortality. High YOY densities resulted in low proportional size structure-quality (PSS Q , <20%) 1 and 2 years later. If similar brown trout population dynamics occur in other streams within the region, interpretation of short-term studies of brown trout (e.g., regulation evaluations, creel surveys, population response to habitat improvement, seasonal movements, and growth rates) may be confounded.

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Section: Discussionsupporting

confidence: 54%

Population dynamics of brown trout in a Minnesota (USA) stream: A 25‐year study

Mundahl

2017

River Research & Apps

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“…Given that climate operates at the regional scale, it was not surprising that synchrony among sites was observed along the 170-km range within the SNP (e.g. Alonso et al, 2011). For a wide-ranging species such as brook trout, future work is warranted to identify spatial extent of synchronous population dynamics at a broader spatial scale.…”

Section: Discussionmentioning

confidence: 99%

Seasonal temperature and precipitation regulate brook trout young‐of‐the‐year abundance and population dynamics

et al. 2015

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Summary Abundance of the young‐of‐the‐year (YOY) fish can vary greatly among years and it may be driven by several key biological processes (i.e. adult spawning, egg survival and fry survival) that span several months. However, the relative influence of seasonal weather patterns on YOY abundance is poorly understood. We assessed the importance of seasonal air temperature (a surrogate for stream temperature) and precipitation (a surrogate for stream flow) on brook trout (Salvelinus fontinalis) YOY summer abundance using a 29‐year data set from 115 sites in Shenandoah National Park, Virginia, U.S.A. We used a Bayesian hierarchical model that allowed the effect of seasonal weather covariates to vary among sites and accounted for imperfect detection of individuals. Summer YOY abundance was affected by preceding seasonal air temperature and precipitation, and these regional‐scale drivers led to spatial synchrony in YOY abundance dynamics across the 170‐km‐long study area. Mean winter precipitation had the greatest effect on YOY abundance and the relationship was negative. Mean autumn precipitation, and winter and spring temperature had significantly positive effects on YOY abundance, and mean autumn temperature had a significant negative effect. In addition, the effect of summer precipitation differed along a latitudinal gradient, with YOY abundance at more northern sites being more responsive to inter‐annual variation in summer precipitation. Strong YOY years resulted in high abundance of adults (>age 1 + fish) in the subsequent year at more than half of sites. However, higher adult abundance did not result in higher YOY abundance in the subsequent year at any of the study sites (i.e. no positive stock–recruitment relationship). Our results indicate that YOY abundance is a key driver of brook trout population dynamics that is mediated by seasonal weather patterns. A reliable assessment of climate change impacts on brook trout needs to account for how alternations in seasonal weather patterns impact YOY abundance and how such relationships may differ across the range of brook trout distribution.

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“…These effects have been demonstrated on many streams in countries encompassing very different climatic, topographic, and geological contexts (e.g. France: Cattanéo et al, 2002Bret et al, 2016;Bergerot & Cattanéo, 2017, U.S.A.: Zorn & Nuhfer, 2007, Spain: Lobón-Cerviá, 2004, 2007Alonso et al, 2011). Depending on the ontogenetic stage, effects of high flows can be explained by the inability of fish to maintain their stream position or to find shelter under high water velocities, or by starvation in non-optimal habitats, or mortality due to substrate scouring during flooding.…”

Section: Synchrony Between Brown Trout Population Abundance and Flowmentioning

confidence: 99%

“…France: Cattanéo et al, 2002Bret et al, 2016;Bergerot & Cattanéo, 2017, U.S.A.: Zorn & Nuhfer, 2007, Spain: Lobón-Cerviá, 2004, 2007Alonso et al, 2011). These effects have been demonstrated on many streams in countries encompassing very different climatic, topographic, and geological contexts (e.g.…”

Section: Spatiotemporal Similarities Of Seasonal Flow Regimes Explamentioning

confidence: 99%

Similarity in seasonal flow regimes, not regional environmental classifications explain synchrony in brown trout population dynamics in France

Bergerot

Victor²,

Cattanéo

2019

Freshwater Biology

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Investigating the variables driving synchrony in populations and the spatial scales at which they operate is worthwhile for both a basic understanding of population dynamics and for management. Various environmental classifications have been developed to group sites according to predefined similarities in abiotic variables (with spatial and/or temporal components) and to explain population dynamics in running waters. These classifications may be useful to managers and decision makers, as studies have shown relationships between time‐dependent population variability and environmental classifications on different spatial scales. We investigated whether synchrony among brown trout (Salmo trutta) populations could be explained by three regional (hydro‐ecoregions, hydrological units, and natural flow regime classification) and one local environmental classification and their associated variables. We first analysed whether densities of two size classes (0+ and >0+ fish) varied over a large spatial scale (i.e. 112 sites spread throughout France sampled for >11 years). We then determined whether the degree of synchrony matched the spatial patterns defined by the environmental classifications. Finally, we investigated whether variables such as river distance between sites, hydrology, and temperature were associated with patterns of synchrony. Overall, relationships observed between synchronies in environmental variables and trout population densities were weak. Synchrony was best explained by the classification based on hydrological units, defined as discrete physical units that provided boundaries with real biological significance for river assemblages (12% for 0+ density and 9% for >0+ density and total densities). However, when considering environmental variables alone, synchrony in trout populations was mainly associated (from 11% to 23%) with seasonal flow variables such as high flows during reproductive and emergence periods. Temperature and river distance did not explain synchrony between trout populations. High flows during emergence appear to be a key driver of brown trout population dynamics. Floods during the reproductive period could explain synchrony among brown trout populations, although the effect was weaker than that of high flows during the emergence period. We concluded that existing physical, morphological, and biogeographical classifications at regional spatial scales were not sufficiently integrative to account for environmental drivers (such as flow variables) that were most associated with the spatiotemporal patterns of synchrony in trout populations. Flow explained synchrony better than classifications did. Further investigations should focus on the spatial patterns of flow variations during the emergence and reproductive periods to derive geographical areas that best capture synchrony patterns in trout dynamics. This approach could be extended to other freshwater fish species for which climatic drivers are important, and may help to explain the distributional changes of exotic species,...

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A large‐scale approach can help detect general processes driving the dynamics of brown trout populations in extensive areas

Cited by 17 publications

References 50 publications

Population dynamics of brown trout in a Minnesota (USA) stream: A 25‐year study

Population dynamics of brown trout in a Minnesota (USA) stream: A 25‐year study

Seasonal temperature and precipitation regulate brook trout young‐of‐the‐year abundance and population dynamics

Similarity in seasonal flow regimes, not regional environmental classifications explain synchrony in brown trout population dynamics in France

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