Waning Habitats Due to Climate Change: the Effects of Changes in Streamflow and Temperature at the Rear Edge of the Distribution of a Cold-Water fish
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Hydrol. Earth Syst. Sci., 21, 4073–4101, 2017 https://doi.org/10.5194/hess-21-4073-2017 © Author(s) 2017. This work is distributed under the Creative Commons Attribution 3.0 License. Waning habitats due to climate change: the effects of changes in streamflow and temperature at the rear edge of the distribution of a cold-water fish José María Santiago1, Rafael Muñoz-Mas2, Joaquín Solana-Gutiérrez1, Diego García de Jalón1, Carlos Alonso1, Francisco Martínez-Capel2, Javier Pórtoles3, Robert Monjo3, and Jaime Ribalaygua3 1Laboratorio de Hidrobiología, ETSI Montes, Forestal y Medio Natural, Universidad Politécnica de Madrid, Camino de las Moreras s/n, Madrid 28040, Spain 2Institut d’Investigació per a la Gestió Integrada de Zones Costaneres (IGIC), Universitat Politècnica de València, C/ Paranimf 1, Grau de Gandia, Valencia 46730, Spain 3Fundación para la Investigación del Clima, C/ Tremps 11. Madrid 28040, Spain Correspondence to: José María Santiago ([email protected]) Received: 18 November 2016 – Discussion started: 9 January 2017 Revised: 16 June 2017 – Accepted: 7 July 2017 – Published: 14 August 2017 Abstract. Climate changes affect aquatic ecosystems by al- edge of the distribution of this species. However, geology tering temperatures and precipitation patterns, and the rear will affect the extent of this shift. Approaches like the one edges of the distributions of cold-water species are espe- used herein have proven to be useful in planning the preven- cially sensitive to these effects. The main goal of this study tion and mitigation of the negative effects of climate change was to predict in detail how changes in air temperature and by differentiating areas based on the risk level and viability precipitation will affect streamflow, the thermal habitat of a of fish populations. cold-water fish (the brown trout, Salmo trutta), and the syn- ergistic relationships among these variables at the rear edge of the natural distribution of brown trout. Thirty-one sites in 14 mountain rivers and streams were studied in central Spain. Models of streamflow were built for several of these sites 1 Introduction using M5 model trees, and a non-linear regression method was used to estimate stream temperatures. Nine global cli- Water temperatures are a primary influence on the physi- mate models simulations for Representative Concentration cal, chemical and biological processes in rivers and streams Pathways RCP4.5 and RCP8.5 scenarios were downscaled (Caissie, 2006; Webb et al., 2008) and, subsequently, the to the local level. Significant reductions in streamflow were organisms that live completely or partially in the water. predicted to occur in all of the basins (max. −49 %) by the Temperature is a major feature of the ecological niche of year 2099, and seasonal differences were noted between the poikilothermic species (e.g. Magnuson and Destasio, 1997; basins. The stream temperature models showed relationships Angilletta, 2009) and a key factor in energy balance of fish. between the model parameters, geology and hydrologic re- It affects the rate of food intake, metabolic rate and growth sponses. Temperature was sensitive to streamflow in one set performance (Forseth et al., 2009; Elliott and Elliott, 2010; of streams, and summer reductions in streamflow contributed Elliott and Allonby, 2013). It is also involved in many other to additional stream temperature increases (max. 3.6 ◦C), al- physiological functions, such as blood function and repro- though the sites that are most dependent on deep aquifers ductive maturation (Jeffries et al., 2012), reproductive timing will likely resist warming to a greater degree. The predicted (Warren et al., 2012), gametogenesis (Lahnsteiner and Leit- increases in water temperatures were as high as 4.0 ◦C. Tem- ner, 2013), cardiac function (Vornanen et al., 2014), gene ex- perature and streamflow changes will cause a shift in the rear pression (White et al., 2012; Meshcheryakova et al., 2016), Published by Copernicus Publications on behalf of the European Geosciences Union. 4074 J. M. Santiago et al.: Waning habitats due to climate change ecological relationships (Hein et al., 2013; Fey and Herren, increase, and droughts put populations under stress. The im- 2014), and fish behaviour (Colchen et al., 2017). pact of water temperatures on the distribution of salmonid Natural patterns of water temperature and streamflow are fish is well documented (e.g. Beer and Anderson, 2013; Eby profoundly linked with climatic variables (Caissie, 2006; et al., 2014); however, the combined effects of rising stream Webb et al., 2008). Therefore, stream temperature is strongly temperatures and reductions in streamflow remain relatively correlated with air temperature (Mohseni and Stefan, 1999), unexamined, with some exceptions (e.g. Wenger et al., 2011; whereas streamflow has a complex relationship with precip- Muñoz-Mas et al., 2016). Jonsson and Jonsson (2009) pre- itation (McCuen, 1998; Gordon et al., 2004). In addition, at- dicted that the expected effects of climate change on water mospheric temperature influences the type of precipitation temperatures and streamflow will have implications for the (rain or snow) that occurs and the occurrence of snowmelt; migration, ontogeny, growth and life-history traits of Atlantic conversely, river discharge is also a main explanatory factor salmon, Salmo salar Linnaeus, 1758, and brown trout, Salmo of water temperature for some river systems (Neumann et trutta Linnaeus, 1758. Thus, investigation of these habitat al., 2003; van Vliet et al., 2011). Furthermore, geology af- variables in the context of several climate scenarios should fects surface water temperatures by means of groundwater help scientists to assess the magnitude of these changes on discharge (Caissie, 2006; Loinaz et al., 2013), influenced by the suitable range and life history of these species. the aquifer depth (shallow or deep) and the water’s residence The objective of this study is to predict how and to what time (Kurylyk et al., 2013; Snyder et al., 2015). extent the availability of suitable habitat for the brown trout, Climate change is already affecting aquatic ecosystems a sensitive cold-water species, will change within its current by altering water temperatures and precipitation patterns. natural distribution under the new climate scenarios through Stream temperature increases have been documented over a study of changes in streamflow and temperature and their the last several decades over the whole globe, such as in interactions. Specifically, in this paper, we (i) assessed the ef- Europe (e.g. Orr et al., 2015, documented a mean increase fects of both streamflow and geology on stream temperature; in stream temperature of 0.03 ◦C per year in England and (ii) predicted the changes in streamflow and stream tempera- Wales), Asia (e.g. Chen et al., 2016, documented a mean ture implied by the climate change scenarios used in the 5th increase in stream temperature of 0.029–0.046 ◦C per year Assessment Report (AR5) of the IPCC; and (iii) assessed the in the Yongan River, eastern China), America (e.g. Kaushal expected effects of these changes on trout habitat aptitude. To et al., 2010, documented mean increases in stream tempera- this end, hydrologic simulations with M5 model trees cou- ture of 0.009–0.077 ◦C per year) and Australia (e.g. Chess- pled with non-linear water temperature models at the daily man, 2009, documented mean increases in stream temper- time step were fed with high-resolution, downscaled versions ature of 0.12 ◦C per year between macroinvertebrate sam- of the air temperature and precipitation fields predicted using pling campaigns). Abundant information is also available re- the most recent climate change scenarios (IPCC, 2013). The garding the impact of recent climate changes on streamflow effects of basin geology on the stream temperature models regimes worldwide (e.g. Luce and Holden, 2009; Leppi et and on the estimated changes in thermal regimes were stud- al., 2012) and, more specifically, in the Iberian Peninsula ied. Finally, the changes in the thermal habitat of trout were (e.g. Ceballos-Barbancho et al., 2008; Lorenzo-Lacruz et al., assessed by studying the violation of the tolerable tempera- 2012; Morán-Tejeda et al., 2014). However, detailed predic- ture thresholds of the brown trout. tions are uncommon (e.g. Thodsen, 2007). The predictions of the Intergovernmental Panel on Climate Change (IPCC, 2013) suggest that these alterations will continue through- 2 Materials and methods out the 21st century, and they will have consequences for the The logical framework followed is summarized in Fig. 1. distribution of freshwater fish (e.g. Comte et al., 2013; Ruiz- First, the daily global climate models output presented by the Navarro et al., 2016). These changes may have an especially IPCC were downscaled to the study area. Then, the obtained strong effect on cold-water fish, which have been shown local climate models output were applied to generate simu- to be very sensitive to climate warming (Williams et al., lations of streamflow and water temperature. The results are 2015; Santiago et al., 2016). For example, among salmonids, daily values that can be used for the assessment of fish habitat DeWeber and Wagner (2015) found stream temperature to be suitability and availability. the most important determinant of the probability of occur- The procedure yielded results in the form of continuous rence of brook trout, Salvelinus fontinalis (Mitchill, 1814). time series, but they are presented for two time horizons: the The rear edge populations (sensu Hampe and Petit, 2005: year 2050 (H-2050) and the year 2099 (H-2099). The values “populations residing at the current low-latitude margins of for these horizons correspond to the average of the values of species’ distribution ranges”) of a cold-water species are es- the different variables for the decades 2041–2050 and 2090– pecially sensitive to changes in water temperature, in ad- 2099, respectively. dition to reductions in the available habitable volume (i.e. streamflow). The rear edge is the eroding margin of the range where lineages mix, the genetic drift and local adaptations Hydrol.