Approaches to projecting climate change effects on arctic fish populations

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Published: February 9, 2010, 3:27 pm
Updated: May 7, 2012, 10:59 am
Author: International Arctic Science Committee
Topic Editor: Mark McGinley
Topics:

This is Section 8.5.2 of the Arctic Climate Impact Assessment
Lead Authors: Frederick J.Wrona,Terry D. Prowse, James D. Reist; Contributing Authors: Richard Beamish, John J. Gibson, John Hobbie, Erik Jeppesen, Jackie King, Guenter Koeck, Atte Korhola, Lucie Lévesque, Robie Macdonald, Michael Power,Vladimir Skvortsov,Warwick Vincent; Consulting Authors: Robert Clark, Brian Dempson, David Lean, Hannu Lehtonen, Sofia Perin, Richard Pienitz, Milla Rautio, John Smol, Ross Tallman, Alexander Zhulidov

Uncertainty in projections of future temperature, hydrology, and precipitation, and their associated consequences for vegetation and nutrient patterns in arctic aquatic ecosystems, makes projecting the specific effects of climate change on a fish species difficult. To date, fisheries (Freshwater ecosystems and fisheries in the Arctic) literature has suggested three approaches to this problem:

  1. the use of regionally specific climate projections that can be coupled directly to knowledge of the physiological limits of the species;
  2. the use of empirical relationships relating local climate (weather) to measurements of species or stock dynamics (e.g., abundance, size, growth rate, fecundity) and comparison of population success temporally (e.g., from a period of climatically variable years) or spatially (e.g., locales representing the extremes of variation in weather conditions such as latitudinal clines); and
  3. the use of current distributional data and known or inferred thermal preferences to shift ecological residency zones into geographic positions that reflect probable future climate regimes.

Physiological approaches (8.5.2.1)

Temperature is typically regarded as a factor affecting individual physiological and behavioral processes, but it is also a key characteristic of the habitat of an organism. Hutchinson[1] defined the niche of an animal as the complete range of environmental variables to which it must be adapted for survival. At the fringes of the distributional range, abiotic parameters associated with particular niche axes are likely to exert a greater influence over the physiological responses (e.g., growth) of the species to its environment than elsewhere. Growth rates and population dynamics of fish living at the limits of their distribution usually differ from those of the same species living in the optimum temperature range[2]. For example, in studies of northern populations of yellow perch, Power M. and van den Heuvel[3] noted that although heterogeneous thermal environments allow fish opportunities to compensate for temperature fluctuations by selecting for preferred temperatures, such opportunities are limited in the portion of the geographic range where temperatures do not typically exceed those that define the optimum scope for growth. Accordingly, unless future temperatures increase above the point where the maximum scope for growth is realized, northern fish will be limited in their abilities to select for optimal growth temperatures and, consequently, are very likely to more strongly reflect the influence of temperature on growth than southern populations. This also suggests that analogues derived from lower-latitude populations will not be accurate guides to the probable impacts of temperature increases on subarctic and arctic fishes. Nevertheless, the effects of climate change in the north are very likely to include faster, temperature-driven growth and maturation rates, reductions in winter mortality, and expanded habitat availability for many species[4]. However, somatic gains will possibly be offset by increased maintenance-ration demands to support temperature-induced increases in metabolism. Ration demands for lacustrine fish are likely to be met as temperatures increase, since warm-water lakes are generally more productive than cold-water lakes[5]. Basic knowledge of temperature–growth relationships and temperature-dependent energy demands is lacking for many key arctic fish species, particularly those exhibiting primarily riverine life histories, thus accurate physiologically based projections of climate change impacts cannot be made.

Empirical approaches (8.5.2.2)

Empirical approaches to projecting the possible effects of climate change on fish [[population]s] can be subdivided into two groups. The first group examines the integrated responses of a population measured by yield or production over time. The second group examines the population characteristics spatially and uses inherent latitudinal variability to make inferences about how they will change under climate change scenarios.

Temporal Yield/Production Projections: There are numerous models for projecting freshwater fish production in lakes[6]. However disagreement exists among researchers as to which lake characteristics most significantly influence productivity. Comparative studies based on lakes covering a wide range of geographic areas and trophic status have suggested that fish production in oligotrophic to hypereutrophic lakes of moderate depth is better correlated with primary production than the morphoedaphic index[7]. Limitations surrounding such modeling center on the deficiencies in fish distribution data and knowledge of the interactive effects of climate-induced changes in key environmental variables[8]. Together with limited fishery (Freshwater ecosystems and fisheries in the Arctic) databases of sufficient length, these limitations in most cases preclude this approach for projecting productivity changes in arctic populations.

Latitudinal Projections: Organism life-history characteristics often vary with latitude because of predictable changes in important environmental factors[9]. Among the most important environmental factors which may vary with latitude is temperature, which is known to influence growth rate in fish [[population]s][10] and thereby indirectly affect life-history attributes that determine population dynamics (e.g., longevity, age-at-maturity, and fecundity). In salmonids, temperature has been shown to influence movement and migration[11], habitat occupancy[12], migration timing[13], smolting[14], growth rate[15], age-at-maturity[16], fecundity[17], and the proportion of repeat spawners[18]. Many studies have demonstrated latitudinally separated disparate populations of the same species with distinctive metabolic rates, thermal tolerances, egg development rates, and spawning temperature requirements consistent with a compensatory adaptation to maximize growth rates at a given temperature[19]. Fish living in low-temperature, high-latitude locales would therefore be expected to compensate by increasing metabolic and growth rates at a given temperature relative to fish in high-temperature, low-latitude locales. There are two generalizations that may be made from studies on latitudinal variation in growth rates: high-latitude fish populations often attain larger maximum body size than conspecifics at lower latitudes; and, although lower temperatures often reduce activity and constrain individuals to grow more slowly, they compensate by accelerating growth rate or larval development time relative to low-latitude conspecifics when raised at identical temperatures. Although adaptation to low temperature probably entails a form of compensation involving relative growth acceleration of high-latitude forms at low temperature, the shift in metabolism increases metabolic costs at higher temperatures, leaving cold-adapted forms with an energetic disadvantage in the higher-temperature environments[20] that are likely to result from climate change. Accordingly, fish populations are likely to be locally adapted for maximum growth rate and sacrifice metabolic efficiency at rarely experienced temperatures to maximize growth efficiency at commonly experienced temperatures. This suggests that the effects of temperature increases on northern fish will possibly include decreased growth efficiency and associated declines in size-dependent reproductive success. Therefore, particular responses to temperature increases are likely to be population-specific rather than species-specific, which greatly complicates the ability to project future situations for particular species over large areas of the Arctic.

Distributional approaches (8.5.2.3)

Many attempts to project biological responses to climate change rely on the climate-envelope approach, whereby present-day species distributions are mapped with respect to key climate variables (e.g., temperature, precipitation) and the distributions shifted in accordance with climate change projections[21]. For example, Shuter and Post[22] have argued that weight-specific basal metabolism increases as size decreases with no associated increase in energy storage capacity, resulting in smaller fish being less tolerant of the starvation conditions typically associated with overwintering. Size-dependent starvation endurance requires that young-of-the-year fish complete a minimum amount of growth during their first season of life. Growth opportunity, however, is increasingly restricted on a south–north gradient and the constraint has been demonstrated to effectively explain the northern distributional limit of yellow perch in central and western North America (Fig. 8.17), European perch (Perca fluviatilis) in Eurasia, and the small-mouth bass in central North America. If winter starvation does form the basis for the geographic distributions of many fishes (e.g., 11 families and 25 genera of fish within Canadian waters[23]), climate-induced changes in growing-season length, and consequent reductions in the period of winter starvation, are very likely to be associated with significant range extensions of many species. Species already well established within low-arctic watersheds are likely to show the greatest potential for range extensions. Associated changes in species assemblages are likely to shift patterns of energy flow in many aquatic systems. For example, increasing the number of cyprinids that consume plankton (e.g., emerald shiner – Notropis atherinoides, lake chub – Couesius plumbeus) in northern waters will possibly divert energy from existing planktivores (e.g., ciscoes) and reduce their population abundances. In turn, top predators (e.g., lake trout) are likely to have altered diets and changes in the ratio of pelagic and benthic sources of carbon in piscivore diets are likely, in turn, to alter tissue mercury concentrations[24] (Section 8.7 (Approaches to projecting climate change effects on arctic fish populations)), thus linking general climate change impacts with local contaminant loadings.

The dominant result of simulations used to project the impact of climate change on the distribution and thermal habitat of fish in north temperate lakes is an increase in available warmer habitat. Temperature influences on thermal habitat use are strong enough that Christie and Regier[25] were able to develop measures of thermal habitat volume during the summer period by weighting the amount of lake-bottom area and pelagic volume with water temperatures within species’ optimal thermal niches. Thermal habitat volume explained variations in total sustained yield of four commercially important species: lake trout, lake whitefish, walleye, and northern pike.

Although distributional changes provide a convenient and easy means of assessing possible range extensions, the flaw in the approach is that species distribution often reflects the influence of interactions with other species[26] or historical effects[27]. Projections based on changes in single-species climate envelopes will therefore be misleading if interactions between species are not considered when projections are made. Microcosm experiments on simple assemblages showed that as the spatial distribution of interdependent [[population]s] changed as a result of temperature increases, the pattern and intensity of dispersal also changed. Thus, climate change will possibly produce unexpected changes in range and abundance in situations incorporating dispersal and species interaction (e.g., competition and predator–prey dynamics). Feedbacks between species are likely to be even more complex than simple experiments allow[28]; for example, distributions of stream-resident salmonids are not simple functions of either temperature or altitude[29]. Accordingly, whenever dispersal and interactions operate in natural populations, climate change is likely to provoke similar phenomena and projections based on extrapolation of the climate envelope may lead to serious errors[30].

In theory, the temperature signal should be strong enough to project long-term changes in the availability of fish thermal habitat and to use available empirical relationships to project sustainable yields. However, until the results of such research are available for arctic fishes, interannual variability and latitudinal differences in climate will provide the best tests for hypotheses about the importance and effects of climate change on arctic fish species[31].

Chapter 8: Freshwater Ecosystems and Fisheries
8.1. Introduction (Approaches to projecting climate change effects on arctic fish populations)
8.2. Freshwater ecosystems in the Arctic
8.3. Historical changes in freshwater ecosystems
8.4. Climate change effects
8.4.1. Broad-scale effects on freshwater systems
8.4.2. Effects on hydro-ecology of contributing basins
8.4.3. Effects on general hydro-ecology
8.4.4. Changes in aquatic biota and ecosystem structure and function
8.5. Climate change effects on arctic fish, fisheries, and aquatic wildlife
8.5.1. Information required to project responses of arctic fish
8.5.2. Approaches to projecting climate change effects on arctic fish populations
8.5.3. Climate change effects on arctic freshwater fish populations
8.5.4. Effects of climate change on arctic anadromous fish
8.5.5. Impacts on arctic freshwater and anadromous fisheries
8.5.6. Impacts on aquatic birds and mammals
8.6. Ultraviolet radiation effects on freshwater ecosystems
8.7. Global change and contaminants
8.8. Key findings, science gaps, and recommendations

References


Citation

Committee, I. (2012). Approaches to projecting climate change effects on arctic fish populations. Retrieved from http://editors.eol.org/eoearth/wiki/Approaches_to_projecting_climate_change_effects_on_arctic_fish_populations
  1. ↑ Hutchinson, G.E., 1957. Concluding remarks. Population studies: animal ecology and demography. Cold Spring Harbor Symposia on Quantitative Biology, 22:415–427.
  2. ↑ Elliott, J.M., 1994. Quantitative Ecology and the Brown Trout. Oxford University Press, 304pp.
  3. ↑ Power, M. and M.R. van den Heuvel, 1999. Age-0 yellow perch growth and its relationship to temperature. Transactions of the American Fisheries Society, 128:687–700.
  4. ↑ Regier, H.A., P. Lin, K.K. Ing and G.A. Wichert, 1996. Likely responses to climate change of fish associations in the Laurentian Great Lakes Basin: concepts, methods and findings. Boreal Environment Research, 1:1–15.
  5. ↑ Regier, H.A., P. Lin, K.K. Ing and G.A. Wichert, 1996. Likely responses to climate change of fish associations in the Laurentian Great Lakes Basin: concepts, methods and findings. Boreal Environment Research, 1:1–15.
  6. ↑ Leach, J.H., L.M. Dickie, B.J. Shuter, U. Borgmann, J. Hyman and W. Lysack, 1987. A review of methods for prediction of potential fish production with application to the Great Lakes and Lake Winnipeg. Canadian Journal of Fisheries and Aquatic Sciences, 44(Suppl.2):471–485.
  7. ↑ Downing, J.A., C. Plante and S. Lalonde, 1990. Fish production correlated with primary productivity, not the morphoedaphic index. Canadian Journal of Fisheries and Aquatic Sciences, 47:1929–1936.
  8. ↑ Minns, C.K. and J.E. Moore, 1992. Predicting the impact of climate change on the spatial pattern of freshwater fish yield capability in eastern Canadian lakes. Climatic Change, 22:327–346.
  9. ↑ Fleming, I.A. and M.R. Gross, 1990. Latitudinal clines – a trade-off between egg number and size in Pacific salmon. Ecology, 71:1–11.–L'Abée-Lund, J.H., B. Jonsson, A.J. Jensen, L.M Saettem, T.G. Heggberget, B.O. Johnson and T.F. Naesje, 1989. Latitudinal variation in life-history characteristics of sea-run migrant brown trout Salmo trutta. Journal of Animal Ecology, 58:525–542.–Leggett, W.C. and J.E. Carscadden, 1978. Latitudinal variation in reproductive characteristics of American shad (Alosa sapidissima): evidence for population specific life history strategies in fish. Journal of the Fisheries Research Board of Canada, 35:1469–1478.–Rutherford, S., S. D'Hondt and W. Prell, 1999. Environmental controls on the geographic distribution of zooplankton diversity. Nature, 400:749–753.
  10. ↑ Elliott, J.M., 1994. Quantitative Ecology and the Brown Trout. Oxford University Press, 304pp.–Wootton, R., 1990. Ecology of Teleost Fishes. Chapman and Hall, 404pp.
  11. ↑ Jonsson, N., 1991. Influence of water flow, water temperature and light on fish migration in rivers. Nordic Journal of Freshwater Research, 66:20–35.
  12. ↑ Paul, A.J. and J.R. Post, 2001. Spatial distribution of native and nonnative salmonids in streams of the eastern slopes of the Canadian Rocky Mountains. Transactions of the American Fisheries Society, 130:417–430.
  13. ↑ Berg, O.K. and M. Berg, 1989. Sea growth and time of migration of anadromous Arctic char (Salvelinus alpinus) from the Vardnes river in northern Norway. Canadian Journal of Fisheries and Aquatic Sciences, 46:955–960.
  14. ↑ McCormick, S.D., L.P. Hansen, T.P. Quinn and R.L. Saunders, 1998. Movement, migration, and smolting of Atlantic salmon (Salmo salar). Canadian Journal of Fisheries and Aquatic Sciences, 55(S1):77–92.–Power, G., 1981. Stock characteristics and catches of Atlantic salmon (Salmo salar) in Québec, and Newfoundland and Labrador in relation to environmental variables. Canadian Journal of Fisheries and Aquatic Sciences, 38:1601–1611.
  15. ↑ Brett, J.R., J.E. Shelbourne and C.T. Shoop, 1969. Growth rate and body composition of fingerling sockeye salmon, Oncorhynchus nerka, in relation to temperature and ration size. Journal of the Fisheries Research Board of Canada, 26:2363–2394.–Jensen, A.J., T. Forseth and B.O. Johnsen, 2000. Latitudinal variation in growth of young brown trout Salmo trutta. Journal of Animal Ecology, 69:1010–1020.
  16. ↑ L'Abée-Lund, J.H., B. Jonsson, A.J. Jensen, L.M Saettem, T.G. Heggberget, B.O. Johnson and T.F. Naesje, 1989. Latitudinal variation in life-history characteristics of sea-run migrant brown trout Salmo trutta. Journal of Animal Ecology, 58:525–542.–Power, G., 1981. Stock characteristics and catches of Atlantic salmon (Salmo salar) in Québec, and Newfoundland and Labrador in relation to environmental variables. Canadian Journal of Fisheries and Aquatic Sciences, 38:1601–1611.–Scarnecchia, D.L., 1984. Climatic and oceanic variations affecting yield of Icelandic stocks of Atlantic salmon (Salmo salar). Canadian Journal of Fisheries and Aquatic Sciences, 41:917–935.
  17. ↑ Fleming, I.A. and M.R. Gross, 1990. Latitudinal clines – a trade-off between egg number and size in Pacific salmon. Ecology, 71:1–11.
  18. ↑ Leggett, W.C. and J.E. Carscadden, 1978. Latitudinal variation in reproductive characteristics of American shad (Alosa sapidissima): evidence for population specific life history strategies in fish. Journal of the Fisheries Research Board of Canada, 35:1469–1478.
  19. ↑ Levinton, J.S., 1983. The latitudinal compensation hypothesis: growth data and a model of latitudinal growth differentiation based upon energy budgets. 1. Interspecific comparison of Ophryotrocha (Polychaeta: Dorvilleidae). Biological Bulletin, 165:686–698.
  20. ↑ Levinton, J.S., 1983. The latitudinal compensation hypothesis: growth data and a model of latitudinal growth differentiation based upon energy budgets. 1. Interspecific comparison of Ophryotrocha (Polychaeta: Dorvilleidae). Biological Bulletin, 165:686–698.
  21. ↑ Minns, C.K. and J.E. Moore, 1992. Predicting the impact of climate change on the spatial pattern of freshwater fish yield capability in eastern Canadian lakes. Climatic Change, 22:327–346.
  22. ↑ Shuter, B.J. and J.R. Post, 1990. Climate, population viability, and the zoogeography of temperate fishes. Transactions of the American Fisheries Society, 119:314–336.
  23. ↑ Shuter, B.J. and J.R. Post, 1990. Climate, population viability, and the zoogeography of temperate fishes. Transactions of the American Fisheries Society, 119:314–336.
  24. ↑ Power, M., G.M. Klein, K.R.R.A. Guiguer and M.K.H. Kwan, 2002. Mercury accumulation in the fish community of a sub-Arctic lake in relation to trophic position and carbon sources. Journal of Applied Ecology, 39:819–830.
  25. ↑ Christie, G.C. and H.A. Regier, 1988. Measures of optimal thermal habitat and their relationships to yields for four commercial fish species. Canadian Journal of Fisheries and Aquatic Sciences, 45:301–314.
  26. ↑ Davis, A.J., L.S. Jenkinson, J.H. Lawton, B. Shorrocks and S. Wood, 1998. Making mistakes when predicting shifts in species range in response to global warming. Nature, 391:783–786.–Paine, R.T., 1966. Food web complexity and species diversity. The American Naturalist, 100:65–75.
  27. ↑ Tonn, W.M., 1990. Climate change and fish communities: a conceptual framework. Transactions of the American Fisheries Society, 119:337–352.
  28. ↑ Davis, A.J., L.S. Jenkinson, J.H. Lawton, B. Shorrocks and S. Wood, 1998. Making mistakes when predicting shifts in species range in response to global warming. Nature, 391:783–786.
  29. ↑ Fausch, K.D., 1989. Do gradient and temperature affect distributions of, and interactions between, brook charr (Salvelinus fontinalis) and other resident salmonids in streams? Physiology and Ecology Japan Special Volume, 1:303–322.
  30. ↑ Davis, A.J., L.S. Jenkinson, J.H. Lawton, B. Shorrocks and S. Wood, 1998. Making mistakes when predicting shifts in species range in response to global warming. Nature, 391:783–786.
  31. ↑ Magnuson, J.J. and B.T. DeStasio, 1997. Thermal niche of fishes and global warming. In: C.M. Wood and D.G. McDonald (eds.). Global Warming: Implications for Freshwater and Marine Fish. Society for Experimental Biology Seminar Series, 61:377–408. Cambridge University Press.
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