The Heat Is Online

Population Dynamics of Songbird Affected by Climate Change

Population Dynamical Consequences of Climate Change for a Small Temperate Songbird

Science, Vol. 287, Feb. 4, 2000

B.-E. Sæther, 1* J. Tufto, 2 S. Engen, 2 K. Jerstad, 3 O. W. Røstad, 4 J. E. Skåtan 5

Predicting the effects of an expected climatic change requires estimates and modeling of stochastic factors as well as density-dependent effects in the population dynamics. In a population of a small songbird, the dipper (Cinclus cinclus), environmental stochasticity and density dependence both influenced the population growth rate. About half of the environmental variance was explained by variation in mean winter temperature. Including these results in a stochastic model shows that an expected change in climate will strongly affect the dynamics of the population, leading to a nonlinear increase in the carrying capacity and in the expected mean population size.

1 Department of Zoology,
2 Department of Mathematical Sciences, Norwegian University of Science and Technology, N-7491 Trondheim, Norway.
3 Aurebekk, N-4500 Mandal, Norway.
4 Department of Biology and Nature Conservation, Agricultural University of Norway, Post Office Box 5014, N-1432 Ås, Norway.
5 N-4592 Birkeland, Norway.
* To whom correspondence should be addressed. E-mail:
Bernt-Erik.Sather@chembio.ntnu.no


A central question in ecology for decades has been how to quantify the relative importance of stochastic and density-dependent factors for fluctuations in population size (1). This question has received increased attention because of the need to predict the biological consequences of climate change (2). To answer this question, we must obtain separate estimates of different forms of stochasticity, such as demographic and environmental variances (3) and the strength of density dependence, and use these estimates to model the impact of a climate change on the population fluctuations. Several studies have predicted changes in species ranges, demographic rates, or average population sizes in response to climate change (4). However, missing are quantitative analyses that explicitly link climate change and population fluctuations in a mechanistic population model. Here we provide such a link and obtain predictions of markedly altered population dynamics for a songbird, mediated primarily through winter temperature.

Modeling the dynamics of populations in a stochastic environment involves estimating the separate effects of density regulation and stochastic factors. The variance of the change in population size can be split into the demographic and environmental variances (5). The demographic variance is caused by stochastic variation among individuals in their contribution to the next generation, whereas the environmental variance is due to stochasticity similarly affecting a certain group of individuals at a certain time (3). Several studies (6) have now shown that knowledge of demographic as well as environmental stochasticity is important for understanding temporal fluctuations in population size. Thus, quantifying the effects and predicting the consequences of an expected climate change, which possibly involves changes in both the mean and the variance of several climatic variables, will require estimates of how these changes will affect the behavior of the population dynamic processes. The dipper (Cinclus cinclus), a 50- to 60-g passerine species widely distributed in aquatic habitats close to running water all over the Palearctic region (7), is suitable for studying those questions because at northern latitudes the amount of ice strongly affects which areas have available winter feeding habitats. Thus, this relation provides a possible link between population dynamics and climate.

We studied a population of dippers in southern Norway (8), where a large proportion of all individuals was color-ringed for individual recognition (9) over a 20-year period (1978-97). Large fluctuations in population size occurred during the study period (Fig. 1A), from a minimum of 27 pairs in 1982 to a maximum of 117 pairs in 1993. The recruitment rate of the population, Rt = Xt plus;1Xt," src="/content/vol287/issue5454/fulltext/854/img001.gif" where Xt is the size of the breeding population in year t, also showed large annual variation (Fig. 1B). The recruitment rate decreased with increasing population size (r = -0.49, n = 18, P < 0.05) (Fig. 1B). Accounting for annual variation in the number of immigrants Mt+1 (10), no significant density-dependent decrease was found in the net reproductive rate NRt = Xt & plus;1Mt+1Xt," src="/content/vol287/issue5454/fulltext/854/img002.gif" (r = -0.27, n = 18, P 0.1) (Fig. 1B). Thus, the decrease in recruitment rate was mainly due to a reduction of the immigration rate IRt = Mt+1Xt" src="/content/vol287/issue5454/fulltext/854/img003.gif" with increasing population size (r = -0.56, n = 18, P < 0.05) (Fig. 1C). However, no significant density dependence in the absolute number of immigrating females was found (r = 0.27, n = 18, P 0.1).


/cgi/content/full/287/5454/854/F1

/cgi/content/full/287/5454/854/F1Fig. 1. (A) The annual variation in population size (Xt). (B) The recruitment rate Rt (solid circles) and the net recruitment rate NRt (open circles) and (C) the immigration rate IRt, in relation to population size (Xt). [View Larger Version of this Image (18K GIF file)]


To estimate parameters, we modeled the dynamics of the population by assuming that the change in the logarithm of the net recruitment rate (Delta log NRt) was normally distributed

Dgr;ln NRt = lnXt&plus;1−Mt&plus;1Xt∼" src="/content/vol287/issue5454/fulltext/854/img004.gif"

 

r−&agr;X,+&bgr;Ct,&sfgr;′e2 + &sfgr;d2Xt" src="/content/vol287/issue5454/fulltext/854/img005.gif"

(1)

where Ct is the climatic variable (11, 12), sigma e2 is the residual environmental variance not accounted for by the variation in Ct, sigma d2 is the demographic variance estimated from individual-based data (13), alpha is the strength of density regulation, and beta denotes the strength of dependency on the climatic variable. Observations of mean winter temperatures Ct were centered such that the expectation E(Ct) = 0 for the whole period with available climate data (38 years). It follows that the environmental variance is about

e2 = &sfgr;′e2&plus;var(&bgr;Ct) = &sfgr;′e2 &plus; &bgr;2var(Ct)" src="/content/vol287/issue5454/fulltext/854/img006.gif"

(2)

The number of immigrants was correlated with the winter temperature (r = 0.62, n = 18, P = 0.003) (Fig. 2A). To incorporate this in the model used for estimation and prediction, we assumed that Mt was Poisson-distributed with parameter lambda t, with each lambda t being independently lognormally distributed with expectation depending on mean winter temperature by letting each

log(&lgr;t) ∼ N(&mgr;0+&mgr;1Ct,&sfgr;&lgr;2)" src="/content/vol287/issue5454/fulltext/854/img007.gif"

(3)

where sigma 'lambda 2 is the variance in the log of the immigration rate, µ0 is the mean log immigration rate at Ct = 0 and µ1 measures the dependence of the immigration rate on temperature.


/cgi/content/full/287/5454/854/F2

/cgi/content/full/287/5454/854/F2Fig. 2. The number of immigrants plotted against mean winter temperature (A) and the relative change in population size in relation to mean winter temperatures that were centered (B) and the NAO index (C). [View Larger Version of this Image (13K GIF file)]


To facilitate modeling of the effects of a climatic change, we used mean winter (January to March) temperature (°C) and total winter precipitation as Ct in Eqs. 1 and 3. These variables were chosen because they are commonly used when developing climatic scenarios (2). We estimated the posterior distribution (quantifying degree of belief in alternative parameter values conditional on the data) for parameters in the model (Eqs. 1 and 3) by Markov Chain Monte Carlo methods (14). These analyses showed that variation in the logarithm of the net reproductive rate was influenced by population density and by climate (Table 1). Low recruitment occurred in years with high population densities. Furthermore, fewer individuals for a given population size were recruited after cold winters (Fig. 2B). This may be because mean winter temperature was closely correlated with the annual variation in the number of days with ice cover in the study area (r = -0.83, n = 38, P < 0.001). However, the recruitment rate was not significantly correlated with winter precipitation (r = 0.13, n = 12, P 0.1). On the basis of the annual variation in mean winter temperature Ct over the past 40 years (SD = 2.09°C) and the estimate of the parameters substituted into Eq. 2, we find that about half of the total environmental variance was explained by variation in winter temperature (Table 1).

The slope (µ1) in the regression of log(lambda t) on Ct was of similar magnitude but somewhat smaller than the slope (beta ) in the regression of the log net recruitment rate on Ct (Table 1), indicating that cold winters have similar effects on the survival of both dispersing and nondispersing individuals.

Table 1. The estimates of the parameters describing the population dynamics (log NRt, see Eq. 1) of the dipper Cinclus cinclus in southern Norway and posteriori probabilities (degree of belief conditional on the observed data) of some hypotheses.


Parameter

Estimate (mean ± SD)

Posteriori probabilities


Population growth rate (r)

-0.086 ± 0.186

P(r 0) = 0.31

Density regulation (alpha )

0.0042 ± 0.0014

P(alpha 0) = 0.998

Effects of winter temperature (beta )

0.15 ± 0.03

P(beta 0) = 0.9999

Total environmental variance (sigma e2)

0.21 ± 0.06

Environmental variance from winter

temperature (sigma ce2)

0.10 ± 0.05

Residual environmental variance (sigma 'e2)

0.11 ± 0.04

Demographic variance (sigma d2)

0.268 ± 0.018

Expected number of immigrants at c = 0

50.4 ± 4.4

at c = 2.5

67.7 ± 7.5

Slope in Poisson regression for Mt1)

0.11 ± 0.03

P1 0) = 0.999

The deterministic carrying capacity K was found numerically (for each realization from the posterior) by setting Xt and Xt+1 equal to K and Mt equal to its expectation and then solving for K.

In the Northern Hemisphere, climatic conditions during winter are often determined by annual variation in large-scale fluctuations in atmospheric mass between the subtropic and the subpolar North Atlantic regions, the North Atlantic Oscillation (NAO). The NAO index is an integrated measure that influences a large number of climatic variables that affect the winter weather over large areas of the Northern Hemisphere (15). For instance, a high positive NAO index is, in general, associated with relatively warm winters with much precipitation in northern Atlantic coastal Europe (16). Variation in the NAO index is closely correlated with global fluctuations in temperature (17). Because the net recruitment rate is positively correlated with the NAO index (Fig. 2C), such large-scale climatic changes are likely to affect the dynamics of the local dipper population.

The Intergovernmental Panel on Climate Change (IPCC) has developed different scenarios of future greenhouse gas and aerosol precursor emissions on the basis of socioeconomic assumptions for the period 1990-2100 (2). These predicted emissions were then used to project atmospheric concentrations of greenhouse gases and aerosols and their effects on natural radiation processes. When these effects are entered into climatic models, some scenarios suggest an increase in mean winter temperature of 2° to 3°C in the region in which our study population is located (2), although the quantitative effects of the anthropogenic influences on the atmosphere must be considered uncertain (18).

To examine the effects of the fluctuations in climate and a change in long-term mean on the dipper population dynamic, we model the climatic process Ct as a first-order autoregressive process (19)

t&plus;1−c∼N&lsqb;a(Ct−c),&sfgr;c2&rsqb;" src="/content/vol287/issue5454/fulltext/854/img008.gif"

(4)

where c and sigma c2 are the mean and variance, respectively, and a determines the return time in the process. According to the scenarios from IPCC for the region (2), we model the change in the long-term mean winter temperature by setting c = 2.5 in Eq. 4.

We find that an increase in mean winter temperature is likely to strongly affect the population dynamics of the dipper in the study area. The expected carrying capacity (K) increased by 58% (Fig. 3A), from K = 115 (SD = 10) for c = 0 to K = 182 (SD = 22) for c = 2.5. Similarly, such an increase in mean temperature increased the expectation of the stationary distribution of the population size Xt from 117 to 184 (Fig. 3B). When we run the model, assuming no relation between climate and immigration rate, the effect of a change from c = 0 to c = 2.5 on K was still large [K = 167 (SD = 20) for c = 2.5]. Thus, the major effect of a change in winter climate on the dipper population will occur through an influence on the local dynamics.


/cgi/content/full/287/5454/854/F3

/cgi/content/full/287/5454/854/F3Fig. 3. (A) The posterior distribution of the deterministic carrying capacity (K) and (B) the stationary distribution of the population size (Xt) before (solid lines) and after (dashed lines) an increase in winter temperature of 2.5°C. (C) The change in the carrying capacity (K) as a function of a change in mean winter temperature (°C). [View Larger Version of this Image (17K GIF file)]


To quantitatively examine how the magnitude of a change in mean winter temperature affected the population dynamics of the dipper, we computed the change in carrying capacity K (evaluated at the estimated mean of all model parameters) over a range of -2.5° to 2.5°C for the mean value of c in Eq. 4. Our analysis suggests a nonlinear relation between the carrying capacity K and change in mean temperature c (Fig. 3C). Thus, for c = -2.5, K will decrease to about 45 pairs, whereas a similar increase in the temperature will increase K considerably more, to about 178 pairs.

Several studies have documented effects of changes in climate on several demographic characteristics of birds and mammals. For instance, many temperate bird species lay eggs earlier in the year, probably because of warmer springs (20, 21). Similarly, the spawning dates of amphibians such as Rana spp. were positively correlated with the NAO index (20). Our study complements those results by showing that climatic changes may also strongly affect the dynamical characteristics of a population (Figs. 2 and 3). These large effects on the population dynamics of relative small changes in climate (Fig. 3) were due to an effect on the local dynamics as well as higher immigration rate in mild winters. These interactions between processes operating at different geographical scales suggest that climate changes may have major consequences for the pattern of fluctuations in bird populations over space and time (22).

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  26. This study was financed by the Norwegian Directorate for Nature Management, the Department of Environment, the County Governor in Vest-Agder, and the Norwegian Research Council. We are grateful to two anonymous reviewers for helpful comments on the manuscript.

27 August 1999; accepted 7 December 1999