Reviews in Fish Biology and Fisheries

, Volume 26, Issue 2, pp 135–151 | Cite as

Impacts of climatic variation on trout: a global synthesis and path forward

  • Ryan P. Kovach
  • Clint C. Muhlfeld
  • Robert Al-Chokhachy
  • Jason B. Dunham
  • Benjamin H. Letcher
  • Jeffrey L. Kershner


Despite increasing concern that climate change may negatively impact trout—a globally distributed group of fish with major economic, ecological, and cultural value—a synthetic assessment of empirical data quantifying relationships between climatic variation and trout ecology does not exist. We conducted a systematic review to describe how temporal variation in temperature and streamflow influences trout ecology in freshwater ecosystems. Few studies (n = 42) have quantified relationships between temperature or streamflow and trout demography, growth, or phenology, and nearly all estimates (96 %) were for Salvelinus fontinalis and Salmo trutta. Only seven studies used temporal data to quantify climate-driven changes in trout ecology. Results from these studies were beset with limitations that prohibited quantitatively rigorous meta-analysis, a concerning inadequacy given major investment in trout conservation and management worldwide. Nevertheless, consistent patterns emerged from our synthesis, particularly a positive effect of summer streamflow on trout demography and growth; 64 % of estimates were positive and significant across studies, age classes, species, and locations, highlighting that climate-induced changes in hydrology may have numerous consequences for trout. To a lesser degree, summer and fall temperatures were negatively related to population demography (51 and 53 % of estimates, respectively), but temperature was rarely related to growth. To address limitations and uncertainties, we recommend: (1) systematically improving data collection, description, and sharing; (2) appropriately integrating climate impacts with other intrinsic and extrinsic drivers over the entire lifecycle; (3) describing indirect consequences of climate change; and (4) acknowledging and describing intrinsic resiliency.


Climate change Trout Streamflow Temperature Ecology Climatic variation 


Climate change is rapidly shifting temperature and streamflow conditions in freshwater habitats worldwide, with major implications for many aquatic species (Woodward et al. 2010), including trout (Oncorhynchus and Salmo spp., Salmonidae) and char (Salvelinus spp., Salmonidae) (henceforth referred to as ‘trout’). Climate change may be especially problematic for trout because they require coldwater habitats that are increasingly fragmented by various human actions, thereby forcing many populations to tolerate environmental changes in situ (Williams et al. 2009). In recent decades, there have been increasing efforts to use species distribution models (SDMs) to identify thermal thresholds potentially limiting trout distributions and to project how climate change will influence future distributions (e.g., Keleher and Rahel 1996; Wenger et al. 2011; Filipe et al. 2013). SDMs ubiquitously predict substantial reductions in trout distributions as global temperatures increase over the next century (see Comte et al. 2013). In some cases, nearly all currently suitable habitat is predicted to be unsuitable by the end of the century, underscoring that climate change may have serious consequences for trout. These alarming patterns are strongly shaping conservation and management strategies, prioritization, and resource allocation (e.g., Williams et al. 2015).

Unfortunately, efforts to “ground truth” results from SDMs or extrapolations of laboratory results are limited, an empirical shortcoming that is contributing to considerable uncertainty and confusion regarding the potential and realized impacts of climatic variation and change on trout. A logical starting point for addressing the uncertainty surrounding results from SDMs or laboratory studies is to retrospectively and systematically evaluate how climatic variation influences species through time. In other words, are the observed impacts of climatic variation on trout congruous with what we expect based on patterns derived from laboratory and spatial studies? Despite the clear value of such an effort, no study has comprehensively synthesized observed relationships between temporal variation in temperature or streamflow and trout ecology across species, life-history stages, and seasons.

This research gap is surprising, as time-series provide a powerful means to directly quantify and communicate how trout are influenced by local environmental conditions that are directly driven by climatic variation, especially temperature and streamflow (Wenger et al. 2010). Additionally, temporal studies can help identify populations, life-stages, seasonal dynamics, and environmental conditions to which trout are particularly sensitive, and thus identify opportunities and challenges facing managers and conservationists (Ehrlén and Morris 2015).

To cohesively synthesize current understanding of how climatic variation influences trout, we performed a systematic literature review to: (1) summarize relationships between interannual variation in streamflow and temperature and trout demography, growth, and phenology; (2) describe observed changes in trout ecology or evolution associated with climate change; (3) critically examine limitations, deficiencies, and sources of uncertainty; and (4) provide explicit recommendations outlining a future path that will improve empirical understanding of how climate change has, and will, influence trout populations worldwide.

Literature review

Interannual variation

In July of 2014, we used Web of Science to search for articles describing relationships between interannual climatic variation and trout ecology by iteratively combining the following terms: trout/char(r) * streamflow/stream temperature/lake temperature * abundance/survival/growth/migration timing (where ‘/’ designates ‘or’). Subsequently, we used literature-cited references within relevant publications (i.e., articles matching the criteria described below) to identify other applicable research. Additionally, we used Google Scholar to examine all publications that cited each applicable paper. We continued to track citations in Google Scholar throughout 2014.

We defined ‘demography’ as any measure relating to population dynamics (e.g., survival, abundance, and recruitment), ‘growth’ as any measure of body condition (e.g., length, weight, condition factor), and ‘phenology’ as the timing of any seasonally recurring life history event (e.g., timing of spawning, migration, emergence, etc.). ‘Temperature’ and ‘streamflow’ were used generally to include air temperature and precipitation, both of which were used as surrogates for stream/lake temperature and flow. All response and predictor variables are described in the Supporting Information (Table S1). The search was limited to studies using a minimum of 5 years of data. We focused on studies conducted on wild populations, excluding experimental approaches or those focused on fish born in hatchery conditions.

Response variables representing demography and growth were highly variable across studies and were measured on dramatically different scales (e.g., density vs. abundance), and summary statistics necessary to standardize effect sizes were rarely reported (e.g., the mean and standard deviation of response variables, range of predictor variables). As such, we were forced to focus on the direction rather than the magnitude of observed relationships, thereby limiting opportunities for quantitative meta-analyses. After identifying studies that matched our initial criteria, we recorded the direction of the observed relationship(s) between each response variable (e.g., young of the year abundance) and temperature or streamflow (see Table S1) as ‘negative’, ‘positive’, or ‘no relationship’. ‘Negative’ was recorded when higher temperature or streamflow was associated with reduced indices of demography, lower measures of body growth, and earlier phenology, and vice versa for ‘positive’. We recorded the underlying biological relationship even if the statistical relationship was opposite because of the defined metric representing temperature or streamflow. For example, if the number of low streamflow days was negatively related to abundance, a ‘positive’ relationship was recorded because there was evidence for greater fish abundances with higher streamflow.

Positive or negative relationships had to be demonstrated by statistically significant parameter estimates (α < 0.05) or sufficient support in information theoretical approaches (AIC). Non-informative parameters were recorded as ‘no-relationship’ if their 95 % confidence interval included 0, or if their addition to the model did not reduce AICC by at least 2.0 relative to the less parameterized model (Arnold 2010). When there was statistical evidence for a quadratic relationship, we recorded the direction of the relationship at the highest observed values of the temperature or streamflow predictor variable. When there were significant interactions between predictor variables, we recorded the observed relationship occurring at the mean value for each temperature or streamflow variable.

Studies matching these criteria were only from the Northern Hemisphere. Therefore, we classified streamflow and temperature relationships by season: spring (March–May), summer (June–August), fall (September–November), or winter (December–February). When streamflow or temperature variables spanned multiple seasons, we recorded the observed relationship for each season separately. For example, the observed effect of mean streamflow on adult survival from June to October was recorded for both summer and fall. Similarly, we categorized relationships by three major age classes: ‘Age-0’; ‘Age-1’; and ‘Age-2+’. These age classes generally correspond with maturity schedules reported in the literature (i.e., young of the year, juvenile, adult), but maturation at Age-1 does occur in various trout species. We used spring as the “birthdate” for all fish given that emergence generally occurs during or immediately following this period. Therefore, winter was the last season for fish in the ‘Age-0’ and the ‘Age-1’ categories before they graduated to the next age class. When age classes were grouped together during analyses, the observed relationship was recorded for all age categories represented in the response variable. For the Age-0 category, the ‘spring’ period also included observed relationships between demography and winter streamflow/temperature (i.e., incubation and emergence). To facilitate spring-flow comparisons for populations that experience runoff during early-summer months (i.e., high-latitude or high-elevation locations), we categorized relationships under ‘Spring’ when peak streamflows occurred in the early summer (June) as opposed to spring months.

To avoid duplication, when data from the same population(s) were used in multiple studies, we only used results from the most recent publication or the publication most directly focused on temporal variation in temperature or streamflow. If multiple methods (e.g., general linear model and quantile regression) or variables (e.g., maximum temperature and mean temperature) were used in a single study, we recorded the direction of any significant or supported relationships, even if other methods or variables provided non-significant results. Results from different streams were recorded separately unless they were grouped (e.g., via averaging or mixed effects modeling). When results from replicate sampling locations were provided within a single stream, river or lake, we recorded the predominant pattern. We summarized observed relationships according to streamflow or temperature, season, and life-stage by vote-counting (e.g., Kemp et al. 2011).

Climatic change

We surveyed the literature for studies that used temporal data spanning at least 10 years to test for changes in trout ecology or evolution associated with climate change. Applicable studies were identified during the literature search described previously. Additionally, we performed a Web of Science search that used the following terms: climate * change * trout. We included all studies describing temporal changes in trout ecology or evolution associated with climatic change, including studies that “directly” tested for climate change causation, and studies that “indirectly” attributed climate change causation based on spatial patterns in the data (e.g., declines in low elevation warm sites but not high-elevation cold sites).

Patterns in the literature

Interannual variation

Forty-two studies met the search and filtering criteria (Table 1). Most studies focused on S. fontinalis in eastern North America and S. trutta in western Europe (Table 2; Fig. 1). Nearly all estimates (n = 444) focused on responses in demography (60 %) or growth (38 %); phenology represented only 2 % of estimates. Across life stages, seasons, and response variables (i.e., demography, body growth, and phenology), most relationships were non-significant (55 %; Table S1). Overall, there was a higher proportion of significant relationships between demographic measures and temperature or streamflow (48 % of total) compared to measures of body growth and temperature or streamflow (37 % of total).
Table 1

Studies reporting one or more estimates relating interannual climatic variation to trout demography, growth, and phenology





1, 3, 5, 9, 11, 15, 17, 18, 23, 27, 28, 32, 33, 41, 42

5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 23, 26, 27, 28, 29, 30, 31, 32, 33, 35, 36, 37, 38, 40, 42


4, 5, 8, 12, 15, 16, 24, 27, 28, 32, 39, 42

4, 5, 14, 15, 18, 27, 28, 32, 39, 42


2, 19, 20, 22, 25, 41

2, 21, 22

Studies are organized by whether they tested for an effect of streamflow and/or temperature. This table only includes studies that matched our filtering criteria (e.g., no duplicate estimates for the same population); other studies that test for relationships between trout ecology and temperature or streamflow are found in the literature

1 Ayllón et al. (2013), 2 Bennett et al. (2014), 3 Borgstrøm and Museth (2005), 4 Buehrens et al. (2014), 5 Carline (2006), 6 Carlson et al. (2008), 7 Cattanéo et al. (2002), 8 Lobón Cerviá (2000),9 Lobón-Cerviá and Mortensen (2005), 10 Lobón-Cerviá (2014), 11 Clews et al. (2010), 12 Egglishaw and Shackley (1977), 13 Grantham et al. (2012), 14 Grossman et al. (2010), 15 Grossman et al. (2012), 16 Hesthagen et al. (2004), 17 Huntsman and Petty (2014), 18 Jensen and Johnsen (1999), 19 Jensen et al. (2012), 20 Jonsson and Jonsson (2002), 21 Jonsson and Antonsson (2005), 22 Jonsson and Jonsson (2009b), 23 Kanno et al. (2015), 24 Kazyak et al. (2013), 25 Kovach et al. (2013), 26 Latterell et al. (1998), 27 Letcher et al. (2015), 28 Mesick (1995), 29 Nehring and Anderson (1993), 30 Nicola et al. (2009), 31 Quiñones et al. (2014), 32 Richard et al. (2015), 33 Robinson et al. (2010), 34 Solomon and Paterson. (1980), 35 Spina (2001), 36 Strange et al. (1992), 37 Unfer et al. (2010), 38 Vincenzi et al. (2007), 39 Vøllestad and Olsen (2008), 40 Warren et al. (2009), 41 Warren et al. (2012), 42 Zorn and Nuhfer (2007)

Table 2

The number of studies and estimates that have quantified the relationship between interannual climatic variation (temperature and streamflow) and temporal variation in trout demography, growth, or phenology

Reproductive timing









S. trutta






S. fontinalis






S. malma





S. alpinus





S. marmoratus






O. mykiss





O. clarkii












Values are summarized by species and response category (i.e., demography, growth, and phenology)

aTwo demographic estimates combined S. trutta and S. fontinalis. They are recorded for each species separately; thus, the sum of the estimates across species is different than the overall number of estimates

Fig. 1

The spatial distribution of estimates relating interannual climatic variation to trout demography, growth, and phenology

Relationships between temperature or streamflow and trout demography varied across seasons and age classes, but consistent patterns were observed in the summer and fall (Fig. 2). Streamflow was positively associated with demography in the summer and fall for all age categories; overall, summer streamflow had the most consistent relationship with trout demography and growth (see below). A positive relationship between summer streamflow and demography was detected in 67 % of estimates over all age classes. A negative effect of summer streamflow on demography was never observed, regardless of age, species, location or study. Similarly, most relationships between summer or fall temperature and demography were negative (51 and 53 %, respectively) across all life-stages. There was only one instance where summer temperature was positively related to demography, a high elevation lake (1236 m) in Norway where Age-0 abundance was higher when summer temperatures were warmer (Borgstrøm and Museth 2005). All significant estimates between fall water temperature and demography were negative.
Fig. 2

The proportion of relationships between trout demography and temperature (Top) or streamflow (Bottom) that were negative, positive, or null. Patterns are summarized by age class and season, where Overall refers to all estimates combined across age classes, Sp spring, Su summer, F fall, and W winter. The numerical values under the season labels are the number of observed relationships in each category. An asterisk highlights when a directional climatic effect was observed for the majority (>50 %) of relationships

Impacts of winter or spring streamflow or temperature on trout demography were variable and frequently non-significant. A notable exception was the impact of spring streamflow on the demography of Age-0 trout, where increasing streamflows were often (57 %) negatively related to abundance or density (i.e., recruitment); however, positive effects of spring streamflows were observed in several instances (9 %). Overall, a higher proportion of estimates detected a significant relationship between interannual variation in streamflow and demography (53 % of estimates were significant) than temperature and demography (42 % of estimates were significant).

Relationships between measures of trout growth and interannual variation in streamflow and temperature were also variable, and in many cases streamflow or temperature was unrelated to growth (Fig. 3). Nevertheless, positive relationships between summer streamflow and growth were consistently observed for Age-0 (60 %), Age-1 (60 %), and Age-2+ (57 %) trout. Similar to relationships between demography and streamflow, a negative relationship between growth and interannual variation in summer streamflow variation was never detected. Overall, streamflow was related to demography and growth slightly more frequently than temperature (48 vs. 37 % of the total estimates). Temperature was largely unrelated to growth for Age-1 and Age-2+ fish regardless of season; only 27 % of estimates detected a temperature effect for these age categories. Although estimates were limited, temperature was positively associated with growth in Age-0 trout during the spring and winter.
Fig. 3

The proportion of relationships between trout growth and temperature (Top) or streamflow (Bottom) that were negative, positive, or null. All abbreviations follow those described in Fig. 3

Only 11 estimates from seven publications quantified the relationship between interannual variation in temperature or streamflow and trout phenology. Eight of 11 estimates were focused on migration timing, while three focused on timing of spawning. Given these limitations, there is little opportunity for generalizing how variability in temperature or streamflow is related to trout phenology. That said, a negative relationship between temperature and spring/early-summer phenology was detected in six of seven estimates (i.e., phenological events were earlier when temperatures were warmer).

Climatic change

Seven studies tested for climate-induced temporal changes in trout ecology or evolution, of which three ‘directly’ tested for an effect of climate change. Muhlfeld et al. (2014) demonstrated that intogressive hybridization between a native (O. c. lewisi) and non-native trout (O. mykiss) trout increased where spring streamflow (precipitation) decreased and summer temperatures increased. Another study (Eby et al. 2014) tested for temporal changes in Salvelinus confluentus occupancy, but found weak evidence for an effect of temperature or elevation on spatio-temporal patterns in occupancy (confidence intervals surrounding parameter estimates for temperature and elevation overlapped zero and climatic predictors variables did not decrease AIC by greater than 2.0 relative to the null model). One study tested for phenological shifts in two trout populations (O. clarkii and S. malma); although migration timing was strongly related to temperature and temperatures increased over time, temporal trends in median dates of migration timing were non-significant (Kovach et al. 2013).

Four studies ‘indirectly’ tested for temporal changes in trout ecology associated with climate change. Comte and Grenouillet (2013) documented a marginal (~1.6 m) shift in the lower extent of the S. trutta distribution in France, and persistence was slightly more common than extirpation at locations predicted to be climatically unsuitable (Grenouillet and Comte 2014). However, three studies documented trout population declines associated with climate warming (Hari et al. 2006; Clews et al. 2010; Almodóvar et al. 2012), particularly for populations at lower elevations (Hari et al. 2006; Almodóvar et al. 2012).


Temperature and streamflow are frequently invoked as key mechanisms regulating trout populations, but existing reviews have generally focused on detailed case-studies from one or several populations, thereby providing a relatively limited scope of inference. The empirical relationships between temperature or streamflow and trout ecology compiled in this synthetic review highlight the generalizable and idiosyncratic nature of climatic impacts acting on trout. Below, we describe consistent patterns that emerged across various populations and species, identify key areas of uncertainty and heterogeneity, and provide major recommendations for improving our understanding of how climatic variation influences trout populations, particularly in the context of a warming future.

Interannual variation

Consistent patterns in trout-climate relationships

A positive effect of summer streamflow on trout demography and growth was observed in 64 % of estimates spanning multiple species, age classes, locations, and a wide array of study designs. Streamflow appears to receive far less attention than the potential thermal consequences of climate change in both the research and management communities. This is clearly an oversight, given that snowpack and resulting summer streamflows are likely to decrease in many portions of the northern hemisphere (Barnett et al. 2008; Adam et al. 2009). As such, management and conservation efforts will need to consider streamflow projections in future management scenarios, and research focused on understanding and predicting the impacts of climate change on trout should incorporate summer streamflow, or acknowledge the uncertainty of failing to do so. Unfortunately, future projections of streamflow are notably uncertain (IPCC 2007), model-based predictions of summer streamflow at unmonitored locations are challenging (Wenger et al. 2010), and long-term, in situ flow-monitoring stations or stream gages are being discontinued (Fekete et al. 2012). Research efforts focused on improving streamflow predictions and monitoring across space and time are critically needed as human demands for increasingly scarce water resources exacerbate the negative impacts of climate change (Barnett et al. 2008).

It may be of some surprise that summer streamflow was more consistently related to trout demography and growth than temperature. However, sensitivity to interannual variation in streamflow or temperature likely depends on the magnitude and variability of each variable at any given location (Letcher et al. 2015), and flow often varies tremendously over interannual periods (Poff et al. 1997). Moreover, long-term monitoring efforts are often conducted where population abundances are relatively robust (i.e., not at range margins where abundances may be lower). Temperatures in such locations may be near optimal for the trout species of interest, and variability in temperature may not be large enough to elicit detectable responses (Penaluna et al. 2015). These issues highlight the need for better quantitative efforts and more effective sampling and monitoring (see below) along broad climatic gradients. Along the same lines, many modeling efforts only test for linear effects of temperature and streamflow, though nonlinear relationships are expected (e.g., growth curves are curvilinear) and have been observed (e.g., Ayllón et al. 2013; Lobón-Cerviá, 2014). Ignoring nonlinearities could easily obscure trout-climate relationships, potentially biasing observed patterns in the literature.

A negative relationship between trout demography and summer or fall temperature was also common, a finding that agrees with SDMs and empirically highlights the potential sensitivity of trout to future warming. Streamflow and temperature are to some degree confounded (i.e., low streamflows are often associated with higher temperatures) during low streamflow periods in the summer and fall (Arismendi et al. 2013). Although this makes it challenging to causally identify the environmental factors driving variation in summer demography, temperature and streamflow can additively and interactively influence trout survival and growth at different seasons and age classes (Letcher et al. 2015), underscoring that both variables should be included in climate change projections. Additionally, the consistent negative relationship between fall temperature or streamflow and trout demography highlights the need to consider additional seasonal effects beyond summer.

Negative relationships between winter/spring streamflow and Age-0 recruitment were frequently reported in the literature. A negative effect of high spring streamflows on recruitment—via redd scouring or washout of emergent juveniles—is a well-described phenomenon (e.g., Cattanéo et al. 2002; Lobón-Cerviá 2014, Strange et al. 1992; Kanno et al. 2015), that can synchronize population dynamics over large-spatial areas (Cattanéo et al. 2003; Lobón-Cerviá, 2004). A substantial portion of the existing literature relating streamflow or temperature variation to trout demography focused on this issue; there were over three times as many estimates for the spring streamflow Age-0 category than the next highest seasonal category (Fig. 2). Interestingly, negative effects of winter/spring streamflow on Age-0 demography appeared to be slightly more common in S. trutta (observed in 69 % of estimates) than S. fontinalis (43 % of estimates). This pattern is surprising since both species spawn in the fall, suggesting equal susceptibility to redd scouring. However, the proportion of estimates detecting a negative effect of spring streamflow on Age-0 recruitment in spring-spawning O. mykiss was also 43 %. Whether these differences are due to sampling variation (the combined number of estimates for S. fontinalis and O. mykiss was 14), publication bias, or actual inter-specific variation in Age-0 susceptibility to spring streamflow is unknown, but of major importance for predicting future trout dynamics given that Age-0 recruitment often has high sensitivities in population projection models (e.g., Kanno et al. 2015). Positive relationships between spring streamflow and Age-0 demography were also observed, reflecting that increasing spring streamflow can benefit recruitment until a threshold is reached, after which streamflow can have strong negative effects (Lobón-Cerviá 2014).

Finally, long-term phenological data were remarkably limited, but temperature was consistently related to phenological events occurring in the spring. Given the observed relationships between trout phenology and temperature and widespread evidence for climate-induced phenological shifts in various taxa (Parmesan and Yohe 2003), it is surprising that we failed to find studies that reported a temporal shift in trout phenology. However, this may result from data limitations, as phenological shifts have been observed in Pacific (Kovach et al. 2015a) and Atlantic salmon (Otero et al. 2014).

Ecological context

Beyond the previously described patterns, relationships between temperature or streamflow and trout demography and growth were variable within and among studies. Even for those trout-climate relationships that were relatively consistent, many estimates were non-significant. This begs the question: why are climatic impacts not detected more frequently?

Biocomplexity—life history and phenotypic diversity reflecting environmental complexity (sensu Hilborn et al. 2003)—certainly plays a major role in the observed heterogeneity in trout responses to climatic variation. Studies in Pacific salmon have highlighted that population responses to climatic variation can vary widely, even among geographically proximate populations (Hilborn et al. 2003; Schindler et al. 2010). This heterogeneity in salmonid-climate relationships is a function of fine-scale environmental heterogeneity itself (Lisi et al. 2013), as well as population specific responses to environmental variation due to local adaptation (Taylor 1991; Fraser et al. 2011). Heterogeneous relationships between temperature or streamflow and trout ecology have also been documented at very fine spatial scales (Elliott 1989; Nicola et al. 2009; Letcher et al. 2015; Penaluna et al. 2015). Although this phenomenon greatly complicates our ability to predict the dynamics of trout under future climate change, biocomplexity provides buffering under shifting climatic regimes through the portfolio effect (Schindler et al. 2010).

Additionally, behavioral adaptation to climatic variation, especially use of within-stream thermal heterogeneity, likely buffers populations from interannual variation in temperature or streamflow. Although data are limited, there is increasing evidence that salmonid fish actively seek and even exploit thermal variation in stream (e.g., Torgerson et al. 1999; Armstrong et al. 2013; Brewitt and Danner 2014) and lake environments (Goyer et al. 2014). Thus, future research focused on understanding the consequences of climate change for trout need to account for thermal heterogeneity, especially groundwater processes, as failing to do so provides overly pessimistic projections of future conditions (Snyder et al. 2015).

The active use and exploitation of fine-scale thermal variability (e.g., Armstrong et al. 2013) may explain the surprising lack of estimates detecting a significant relationship between interannual temperature and trout growth. Moreover, the influence of temperature on other processes, such as maturation, may further mask thermal impacts to growth trajectories. For example, trout in colder environments accumulate more lipids, which in turn leads to maturation at younger ages and smaller sizes (Sloat et al. 2014). Finally, it is unclear how variability in temperature or streamflow influences food availability (though the strong positive effect of summer streamflow on growth suggests flow-mediated benthic drift is important; see also Letcher et al. 2015), emphasizing the need to consider the impacts of climate change on overall community dynamics in freshwaters environments (Woodward et al. 2010).

Intraspecific density also strongly influences the effects of temperature or streamflow on trout growth or demography. Multiple studies demonstrated that higher intraspecific densities exacerbated impacts of temperature and streamflow on survival (Carlson et al. 2008; Richard et al. 2015) and growth (Xu et al. 2010), especially during low summer streamflows. Alternatively, a negative effect of summer temperature on S. trutta abundance in Spain was substantially stronger at locations with low abundances and strong anthropogenic impacts (Ayllón et al. 2013). Stronger climatic impacts at very high densities or very low densities may reflect compensatory versus additive dynamics, phenomena that have very different implications for persistence under climate change.

Finally, phenotypic plasticity can allow individuals to rapidly adjust to climate change, and many important phenotypic traits in trout (e.g., developmental rates, emergence timing) demonstrate plasticity to temperature. Additionally, there is often genetic variation underlying phenotype-environment relationships (i.e., reaction norms) across individuals and populations (Hutchings, 2011), indicating that (1) phenotypic plasticity itself can respond to natural selection, and (2) existing patterns of plasticity appear to be locally adapted to match observed thermal regimes (Jensen et al. 2008; Meier et al. 2014). Linking phenotypic plasticity to population dynamics and persistence in the face of climate change is a novel frontier, but recent theoretical (Reed et al. 2010) and empirical (Vedder et al. 2013) work highlights that plasticity can greatly increase probability of persistence under shifting climates.

Data limitations

The available literature varied widely in study design, analytical approach, and data quality, all of which can influence the observed relationships. For example, there was a higher proportion of significant estimates (49 vs. 32 %) between demography and streamflow or temperature when empirically measured data were used for predictor variables instead of surrogate or modeled data (i.e., less accurate descriptions of streamflow and temperature variation). In some cases, studies summarized temperature or streamflow dynamics over multiple seasons, or response variables over multiple age classes, potentially obscuring season- or age-specific relationships. There were also substantial differences in the level of detail employed by various studies, and some studies contributed far more estimates than others (Table S1). Additionally, publication bias is always a concern; however, many studies tested whether other mechanisms, particularly intrinsic density dependence, explained variation in demography or growth, which should alleviate bias toward reporting significant results of either temperature of streamflow (i.e., there were alternative hypotheses and explanations).

Additionally, the underlying climatic variation at a site can strongly influence resulting inference. For example, summer temperature may negatively affect demography only when it exceeds a certain threshold. This pattern was demonstrated in Ayllón et al. (2013), where relationships between a measure of standardized abundance and temperature were relatively flat at locations that did not exceed ~21 °C, but were strongly negative when temperatures surpassed that threshold. Alas, detecting thresholds will be challenging at low-elevations or range margins where trout are already extirpated or at extremely low abundance due to habitat degradation, fragmentation, and invasive species (e.g., Marschall and Crowder 1996; Dunham et al. 1997), all key factors that likely confound spatial results reported in many SDMs (Araújo and Peterson 2012).

A quantitatively rigorous meta-analysis describing how temperature and streamflow influence trout demography, growth, and phenology is urgently needed to help identify critical thresholds, and account for numerous sources of biotic, abiotic, or sampling variation. Currently, this is an impossible task as the data necessary to standardize observed relationships—mean, standard deviation, and range of predictor and response variables—are rarely reported, an oversight we acknowledge in some of our own work. These exact issues are also plaguing conservation research more generally (Haddaway 2015), stressing a major deficiency in research occurring in terrestrial and aquatic systems. Future research should more carefully report relevant summary statistics to facilitate larger-scale efforts, especially given global interest and major economic investment in the management and conservation of trout.

Climatic change

Few studies have tested for temporal changes in trout ecology or evolution due to climate change, but such impacts appear to be occurring. Several lines of evidence suggest that trout populations are declining in southerly portions of species’ ranges or lower elevations. Direct climate-induced extirpations are, however, poorly documented. This may reflect the surprising resiliency of salmonid populations at small abundances (Lobón-Cerviá 2009; Peterson et al. 2013), or a lack of research testing for such changes. Other ecological responses to climate change (e.g., shifts in size or age distributions, maturation schedules, and phenology) have been observed in various fishes (Crozier and Hutchings 2014), but not yet in trout. Again, this likely highlights a lack of relevant research.

While hydrological changes appear to have influenced introgression between invasive O. mykiss and native O. clarkii (Muhlfeld et al. 2014), we were unable to identify studies that have tested for impacts of anthropogenic climate change on other evolutionary processes. Nevertheless, such changes may be occurring given that climate changes can strongly influence patterns of natural selection, genetic drift, and gene flow.

Major recommendations

Clearly, there are major taxonomic and spatial biases in the existing work. There are numerous trout species and genera (e.g., Hucho, Brachymastax) as well as entire continents (Africa, Asia, Australasia, and South America) for which there were few, or no, published relationships. Understanding spatial and interspecific differences in how trout will be impacted by climate change (i.e., differential sensitivity) is important not only for predicting dynamics in their native ranges, but also predicting the success and negative impact of nonnative trout (e.g., Fausch et al. 2001). Although major sources of uncertainty are clearly evident, addressing the following recommendations will greatly aid our efforts to provide meaningful descriptions and predictions of how climate change influences trout.

Improve systematic data collection and access across management and political boundaries

The current body of trout-climate literature using temporal data is limited and not conducive to broad-scale quantitative efforts or meta-analyses. This stands out as a significant obstacle preventing biologists from making the empirical leaps necessary to predict the future dynamics of trout under a changing climate. Addressing this deficiency will require a move toward collaborative data collection and sharing that spans agencies and geographical boundaries, effectively transitioning climate-trout research to appropriate scales.

At a minimum, the following are critically needed: (1) researchers should improve descriptions of methodology, underlying environmental and biotic data, and statistical results; (2) published data should be accessible in appropriate data repositories, a rapidly increasing requirement for other biological disciplines; (3) fisheries managers, biologists, and researchers at local, federal, and academic institutions should create, maintain and use cross-boundary databases for monitoring data; and (4) we should strategically improve and standardize monitoring efforts. Indeed, data quality, methods, and reliability often vary tremendously over time and space, a clear reflection of the fact that monitoring strategies tend to vary among agencies, regions, and even managers within regions. At present, the vast majority of data collection is poorly suited for robust climate-inference and prediction. Meaningful discussion and research into how to improve and standardize monitoring efforts through space and time, not just within but also between natural resource agencies, are critically needed, as are strategic sampling designs that target locations of higher biological or climatic interest (e.g., areas with the highest rates of climate velocity; Isaak and Rieman 2013). The systemic inconsistency in sampling design, statistical methodology, data availability, and reporting found throughout the available literature is concerning for a species group with such high profile and associated economic and cultural value.

It should also be noted that long-term monitoring data not described in this synthesis (i.e., not published) are available for many trout populations. In other words, the current body of literature is limiting, but there is likely immense data potential that could be harnessed by researchers from throughout the northern and southern hemispheres.

Move from rates and states to population dynamics and sensitivity analyses

Research identified herein almost exclusively focuses on specific measurements of demography, growth, and phenology during particular seasons and life-stages. Population trajectories are, however, a combined function of demographic vital rates that are influenced by streamflow and temperature, variation in growth (Vincenzi et al. 2012) and phenology (e.g., Scheuerell et al. 2009, Kennedy and Crozier 2010), and other extrinsic or intrinsic factors (e.g., density). Thus, accurately predicting population dynamics under a changing climate will require comprehensive models that integrate intrinsic and extrinsic relationships across the entire trout life-cycle (Ehrlén and Morris 2015). Moving beyond rates and states to examining how climatic variation influences populations dynamics in light of ecological context will help identify relationships that have the strongest impact on overall population dynamics (i.e., highest sensitivity), providing valuable information for management and monitoring.

A recent effort used integral projections models to show that temporal decreases in S. fontinalis abundance were due to increasing summer and fall stream temperature and associated reductions in YOY survival (Bassar et al. 2015). Although, intrinsic dynamics helped reduce the negative impacts of warming temperatures, especially by increasing growth rates, negative effects of summer temperature overwhelmed compensatory dynamics. Additionally, season-specific effects of streamflow, which were strong, offset one another, resulting in no net impact to population growth rate. Another study similarly demonstrated that compensatory dynamics in growth can provide important resilience to trout populations, but strong shifts in certain conditions (e.g., flooding) can exceed the buffering conferred by intrinsic dynamics (Vincenzi et al. 2011). Identifying the parameter space where compounding impacts exceed compensatory dynamics will be critical for identifying thresholds and predicting future trout dynamics. Although such efforts are often data-intensive, novel quantitative methods are providing avenues for more holistic modeling even when detailed demographic data are limited. For example, Kanno et al. (2015) used long-term, agency-collected, abundance data from multiple sites in a hierarchical Bayesian framework to quantify how climatic variation influences S. fontinalis vital rates, population dynamics, and future persistence probabilities.

Describe indirect effects of climate change

Most research focuses on how climatic variation directly influences trout ecology, with few examples considering how additional biotic stressors interact with climate change, especially nonnative species and disease. Nevertheless, climate-induced changes in demography across biota appear to be almost entirely due to changes in inter-specific interactions, not exceedence of physiological tolerances (Cahill et al. 2012; Ockendon et al. 2014). This is particularly concerning given that climate change is generally predicted to favor many nonnative species, especially in aquatic ecosystems (Rahel and Olden 2008; Sorte et al. 2013).

Multiple lines of evidence suggest that climate change will exacerbate interactions between native and non-native trout (Al-Chokhachy et al. 2013) and between trout and other fishes (Hein et al. 2014). Nevertheless, empirical data linking climatic variation or change to altered interactions between trout and other invasive fishes are extremely limited (but see Muhlfeld et al. 2014). Alternatively, trout have significant negative impacts on freshwater biodiversity, especially galaxiid fishes, throughout the southern hemisphere (McDowall 2006), and identifying the role of climate in exacerbating or mediating their future impact will be critical for many conservation programs (Habit et al. 2012; Arismendi et al. 2014).

Similarly, climate change will influence disease dynamics and virulence, especially novel diseases to which trout and other salmonids are naïve (Miller et al. 2014). In several cases, population declines associated with climate change appear to be due, at least in part, to the emergence of novel pathogens in low elevation and warm habitats (Hari et al. 2006; Jeppesen et al. 2012). Clearly, the indirect effects of climate change need to be better understood and incorporated into forecasting efforts.

Improve understanding and description of resiliency and adaptive capacity

Despite a growing appreciation that ecological and evolutionary dynamics are strongly coupled on contemporary time-scales (Schoener 2011), our empirical understanding of resiliency and adaptive capacity is notably lacking. Although some data suggest there may be limited genetic variation for thermal tolerance in trout (reviewed in Jonsson and Jonsson 2009a; Elliott and Elliott 2010), other studies are beginning to describe a clear genomic basis for thermal adaptation (Narum et al. 2010, 2013). Additionally, focusing on thermal tolerance alone provides an incomplete picture of adaptive potential, as phenological traits—the primary target of climate-induced natural selection (Bradshaw and Holzapfel 2008)—are highly heritable in salmonid fishes (Carlson and Seamons 2008), and have undergone climate-induced microevolution in Pacific salmon populations (Crozier et al. 2011; Kovach et al. 2012). Other studies have demonstrated that salmonid populations are capable of adaptive evolution over short periods (Quinn et al. 2000; Hendry et al. 2000; Westley et al. 2012), further underscoring that microevolution is a phenomenon not to be ignored.

Adaptive evolutionary dynamics are challenging to generalize and predict, but efforts for salmonid fishes exist (Reed et al. 2011). Merging existing long-term individual based studies with appropriate evolutionary models (e.g., Clutton-Brock and Sheldon 2010) will improve our understanding of these dynamics in trout. In the meantime, existing data can be used to quantify how genetic diversity in trout is spatially distributed relative to future climatic stress (Kovach et al. 2015b), related to climatic variation (Whiteley et al. 2015), and influenced by climate change (Muhlfeld et al. 2014). Monitoring programs and experimental studies should also collect relevant genetic and phenotypic data that can inform our future understanding of evolutionary dynamics (Naish and Hard 2008). In the absence of genetic or phenotypic data, portfolio theory can be used to quantify and compare buffering capacity across space (Griffiths et al. 2014), thereby providing course information about intrinsic resiliency.


This review provides the first global synthesis describing how temporal variation in streamflow and temperature influences trout. At present, this understanding—though hinting at several consistent patterns—is limited. Much work remains, but there are clearly opportunities to use existing data and improve data collection, access, and reporting. Although retrospective analyses provide an excellent means to quantify and communicate how climate influences aquatic organisms, no single approach to understanding how climatic variation or change influences trout is a panacea. Future efforts that integrate spatial, temporal, and ecophysiological data to exploit multiple forms of inference (i.e., specificity generality, and mechanism) will be particularly illuminating (Buckley et al. 2010; Cooke et al. 2013; Ehrlén and Morris 2015). More generally, comprehensive approaches that leverage expertise, effort, and data across the diversity of trout species and their habitats will be necessary to quantify general patterns, identify key sources of variation, and account for biological and sampling uncertainty. This synthesis further emphasizes that climate change will pose challenges for trout worldwide; those challenges will be best met with concerted, coordinated and comparable approaches that appropriately match research design, data collection, and analyses simultaneously with broad-scale policy and local management decision-making, an approach clearly lacking in current research and management efforts.



This work was funded by the USGS National Climate Change and Wildlife Center. R.P.K. was supported by a USGS Mendenhall Fellowship. We thank Peter Westley, Javier Lobón-Cerviá and three anonymous reviewers for comments and thoughts that substantially improved the manuscript. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government.

Supplementary material

11160_2015_9414_MOESM1_ESM.pdf (111 kb)
Supplementary material 1 (PDF 110 kb)


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Copyright information

© Springer International Publishing Switzerland (outside the USA) 2015

Authors and Affiliations

  1. 1.Northern Rocky Mountain Science CenterU.S. Geological SurveyWest GlacierUSA
  2. 2.Flathead Lake Biological StationUniversity of MontanaPolsonUSA
  3. 3.Northern Rocky Mountain Science CenterU.S. Geological SurveyBozemanUSA
  4. 4.Forest and Rangeland Ecosystem Science CenterU.S. Geological SurveyCorvallisUSA
  5. 5.S.O. Conte Anadromous Fish Research Science CenterU.S. Geological SurveyTurner FallsUSA

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