Detecting population-level consequences of ongoing environmental change without long-term monitoring

Ecology, July, 1999 by Daniel F. Doak, William Morris

INTRODUCTION

There is general agreement that anthropogenic environmental changes have already affected most areas of the world, and that these impacts will become increasingly pronounced over the next century (Schneider 1993, Vitousek 1994, Vitousek et al. 1996). These changes include global phenomena, such as atmospheric warming, ozone depletion, and higher C[O.sub.2] concentrations, as well as more localized impacts such as nitrogen deposition, acid precipitation, introduced species, grazing pressure, and selective harvesting. However, what the consequences of these changes will be for individuals, populations, and communities is far from clear (Kareiva et al. 1993, Korner and Bazzaz 1996, Walker and Steffen 1996). In part, this uncertainty arises because the predicted regional-scale (i.e., continental or subcontinental) changes in temperature, for example, are on less firm footing than are global predictions (Houghton et al. 1990, Tao et al. 1996). In addition, many simultaneous anthropogenic changes will result in a mix of negative effects (e.g., the consequences of elevated levels of ultraviolet (UV) radiation caused by ozone depletion) and positive effects (e.g., the amelioration of arctic and alpine environments by global warming and the enhancement of photosynthesis due to enrichment of atmospheric C[O.sub.2]) on native species and communities. In the face of this uncertainty, there is a considerable need for new data and new methodologies to quantify the effects of environmental changes. Here, we present a method that can be used to detect the population-level effects of past and ongoing environmental changes in the absence of any long-term monitoring data, thus providing a firmer empirical footing for future predictions. The data requirements of this technique are substantial, but not unrealistic, for common, easily sampled species. While, in our Discussion, we will focus on climatic changes, the method is just as useful for detecting more localized population-level responses to anthropogenic impacts or naturally occurring changes in local environments.

Exploration of the results of environmental change can be focused on individual, population, community, or ecosystem responses. However, most studies of global environmental change have focused on traits of individuals (e.g., growth, Tissue and Oechel [1987], Havstrom et al. [1993]; or reproduction, Wookey et al. [1993]) or ecosystem-level features (e.g., total biomass, Harte and Shaw [1995]), while relatively few studies have examined population-level changes, such as trends in the size distributions or numbers of individuals (Korner and Bazzaz 1996). For example, we are aware of only two studies that have documented population responses of herbaceous plants to ongoing climatic warming in the field. Fowbert and Lewis Smith (1994) and Lewis Smith (1994) have recently argued that the signature of climate change can be detected by examining entire populations of herbaceous plants. Taking advantage of long-term censuses of the only two native species of Antarctic vascular plants, pearlwort (Colobanthus quitensis) and hairgrass (Deschampsia antarctica), they were able to show that the numbers of individuals in two populations of each species have increased in the past two to three decades, a trend that paralleled increases in temperature at their study sites. Searching for shifts in the historical ranges of species (Grabherr et al. 1994, Parmesan 1996) provides another population-scale approach to detecting biotic responses to environmental change. These two studies provide some of the most convincing data directly documenting population-level climatic change. However, the data used in these studies, i.e., very long-term censuses, are currently unavailable for most species and locales.

In the current paper, we propose a new method that will allow ecologists to search for the population-level consequences of past environmental change in the absence of long-term data. While the method will not work for all species, it is sensitive enough to measure the effects of environmental change for many plants and animals. Conveniently, it may be especially appropriate for species such as long-lived herbaceous perennial plants that characterize those biomes (e.g., arctic and alpine tundra) that are depauperate in the woody species commonly relied upon as long-term detectors of environmental change. Thus, the technique provides a natural complement to existing methodologies in the detection of environmental change and its ecological consequences. The method uses the fact that species will manifest responses to changing environments not only in individual demographic rates, but also in the distribution of the sizes or ages of individuals within populations, and that for long-lived and slowly growing species, population structure can lag far behind that predicted from current demographic rates. In a nutshell, the idea is that, by determining the degree to which the current size distribution in a population does not accord with current demographic rates (i.e., reproduction, growth, and survival), we can estimate recent changes in demographic rates, even when we have no way of directly determining the size or structure of the population at any time in the past. While the comparison of actual and predicted size or age distributions is straightforward (Caswell 1989), it has most often been viewed as a simple check on the validity of demographic models, with mismatches seen as mere annoyances. Our goal is to turn these "mismatches" into a virtue, thereby facilitating tests of specific hypotheses about the strength and speed of multiple effects of environmental change.