Drivers of Plant Population Dynamics in Three Arid to Subhumid Ecosystems

Understanding the relative importance of density-dependent and densityindependent factors in driving population dynamics is one of the oldest challenges in ecology, and may play a critical role in predicting the effects of climate change on populations. We used long-term observational data to describe patterns in plant population regulation for 57 forb and grass species from three different ecosystems (arid desert grassland, semiarid sagebrush steppe, and subhumid mixed-grass prairie). Using a hierarchical partitioning approach, we (i) quantified the relative influence of conspecific density, heterospecific composition, and climate on temporal variation in population growth rates, and (ii) asked how the relative importance of these drivers depends on aridity, species growth form and life expectancy, and abundance and spatial patterns. We found that density-dependence had the strongest effect on species. Climate often had a significant effect, but its strength depended on growth form. Community composition rarely explained significant variation in growth rates. The relative importance of density, composition, and climate did not vary among sites, but was related to species' life histories: compared to forbs, grasses were more sensitive to climate drivers. Abundance and spatial clustering were negatively correlated with the importance of density dependence, suggesting that local rarity is a consequence of self-limitation. Our results show that interspecific interactions play a weaker role than intraspecific interactions and 1 Coauthored by Luke Zachmann, Aldo Compagnoni, Mevin Hooten, Debra Peters, and Peter Adler. 8 climate variability in regulating plant populations. Forecasting the impacts of climate change on populations may require understanding how changes in climate variables will affect the strength of density-dependence, especially for rare species. INTRODUCTION Disagreements about the importance of density-dependent vs. density-independent drivers of population dynamics dominated ecology in the middle of the 20 century (Hixon et al. 2002). The most intense debate pitted Nicholson (1954), who claimed that interactions between individuals within populations regulate population growth, against Andrewartha and Birch (1954), who argued that external factors like weather determine growth. In hindsight, we recognize that the protagonists took extreme positions (Begon et al. 1996) and that both processes operate simultaneously (Sibly & Hone 2002). The focus of contemporary research on the determinants of population growth is on disentangling the relative importance of density-dependent and independent determinants of population growth (e.g., Coulson et al. 2001; Nowicki et al. 2009). This information is essential to forecasts of future population trends, especially the response of plant and animal populations to climate change. Most efforts to forecast climate change impacts are based on the direct responses of individual species to climate (Walther et al. 2002). But species are also influenced by interactions with other species, and climate change can alter these interactions (Tylianakis et al. 2008). In some cases, the indirect effects mediated by species interactions can influence population dynamics more than the direct effects of climate change (Dormann et al. 2004; Suttle et al. 2007). Despite conceptual progress in teasing apart density-dependent and independent 9 determinants of population growth and the direct and indirect effects of climate change, we still lack an empirical basis for generalizing across species and environmental gradients, especially for plant species (e.g., Adler & HilleRisLambers 2008). Most studies have focused on single species, or sets of closely-related species (e.g., Putman et al. 1996). As a result, we have not identified the species traits or environmental correlates that might predict the relative importance of density-dependent and independent drivers. This synthetic understanding could help us anticipate which sites or species are most vulnerable to climate change, even when few data are available. At least three factors might explain general patterns in the relative importance of drivers of plant population dynamics. The first candidate factor is resource availability. Climatic controls on plant population growth may be relatively strong for species at arid sites where low and variable soil moisture constrains growth (Noy-Meir 1973; Huxman et al. 2004). At wetter sites, where resources are more abundant and reliable, densitydependent population regulation and species interactions may exert stronger control over plant growth and population growth rates (Grime 1979; Keddy 1989). On the other hand, species interactions might be important along the entire gradient of resource availability (Newman 1973; Tilman 1988, 1990). The second factor potentially influencing the relative importance of densitydependent and independent processes on population dynamics is life history. For example, the strong morphological, life history, and ecophysiological differences between forbs and grasses in semiarid plant communities (Martin et al. 1991; Turner & Knapp 1996) could influence the relative importance of density-dependent and independent drivers. We might expect shorter-lived species to be more sensitive to climatic variability 10 than longer-lived species, which are buffered against such changes (Morris et al. 2008). Finally, a species' abundance may influence whether density-dependent or independent processes are more important in regulating that species’ population. Density dependence may be a stronger, more detectable force in more abundant species (Wills et al. 1997), perhaps because these species have reached carrying capacity. Since most species are rare, density-dependent population regulation may have little impact on most species most of the time (Grubb 1986). This hypothesis views differences in densitydependence as a consequence of differences in abundance. The alternative is that densitydependence could be the cause of differences in abundance: species may become abundant only if they are not strongly self-limiting. When testing these hypotheses, it may be important to consider the spatial scale at which abundance is estimated. Spatially averaged abundance may poorly characterize plant populations if the fate of individuals is determined by their local neighborhood and the spatial distribution of plants is nonrandom (Harper 1977). In fact, plants are often distributed non-randomly and spatial structure can have large impacts on plant population dynamics (Czaran & Bartha 1992). Growing interest in the link between plant spatial patterns and ecological processes (McIntire & Fajardo 2009), as well as methods to quantify spatial patterns (e.g., Wiegand & Moloney 2004), has enabled us to ask how the strength of density-dependence is related to spatial clustering. We used long-term datasets and hierarchical partitioning (Chevan & Sutherland 1991) to quantify the temporal variation in population growth rates explained by temporal variation in population density, community composition, and climate. Our primary objective was to quantify the relative importance of these three drivers of population 11 growth for species of different growth forms (forbs and grasses) and from different ecosystems (arid desert grassland, semiarid sagebrush steppe, and subhumid mixed-grass prairie). Our second objective was descriptive. We asked how the relative importance of population density, community composition, and climate depends on 1) aridity, 2) species growth form and life expectancy, and 3) abundance and spatial patterns. METHODS Study site and dataset description The data used in our analysis come from chart quadrats, which are permanent 1m quadrats in which all individual plants are identified and mapped each year using a pantograph (Hill 1920). Under Clements' (1907) influence, many range experiment stations across the western U.S. began mapping quadrats between the 1910s and the 1930s and continued annual censuses for decades. We used three chart quadrat datasets that represent widespread plant communities in the Western U.S.: sagebrush steppe at the USDA-ARS Sheep Station in Dubois, Idaho; mixed-grass prairie near Hays, Kansas; and Chihuahuan desert grassland at the Jornada Experimental Range in New Mexico. These sites differ in temperature and the timing and amount of precipitation, ranging from arid (New Mexico) to semiarid (Idaho) to subhumid (Kansas). Table 1 provides details about the climate and sampling coverage at each site. The original maps from each of the sites have been digitized. The data from KS and ID are available online in both spatial and tabular formats along with monthly precipitation and temperature data (Adler et al. 2007; Zachmann, Moffet, & Adler unpublished work). We analyzed the population growth rates of 57 species across the three sites that 12 were common enough to provide a sufficient sample size for model fitting. The number of species included in the analysis varied by site. In Idaho, Kansas, and New Mexico, the number of forbs was 10, 17, and 8, respectively; the number of grasses was 7, 8, and 7,