Interplay between r- and K-strategists leads to phytoplankton underyielding under pulsed resource supply

Fluctuations in nutrient ratios over seasonal scales in aquatic ecosystems can result in overyielding, a condition arising when complementary life-history traits of coexisting phytoplankton species enables more complete use of resources. However, when nutrient concentrations fluctuate under short-period pulsed resource supply, the role of complementarity is less understood. We explore this using the framework of Resource Saturation Limitation Theory (r-strategists vs. K-strategists) to interpret findings from laboratory experiments. For these experiments, we isolated dominant species from a natural assemblage, stabilized to a state of coexistence in the laboratory and determined life-history traits for each species, important to categorize its competition strategy. Then, using monocultures we determined maximum biomass density under pulsed resource supply. These same conditions of resource supply were used with polycultures comprised of combinations of the isolated species. Our focal species were consistent of either r- or K-strategies and the biomass production achieved in monocultures depended on their efficiency to convert resources to biomass. For these species, the K-strategists were less efficient resource users. This affected biomass production in polycultures, which were characteristic of underyielding. In polycultures, K-strategists sequestered more resources than the r-strategists. This likely occurred because the intermittent periods of nutrient limitation that would have occurred just prior to the next nutrient supply pulse would have favored the K-strategists, leading to overall less efficient use of resources by the polyculture. This study provides evidence that fluctuation in resource concentrations resulting from pulsed resource supplies in aquatic ecosystems can result in phytoplankton assemblages' underyielding

Complexity and stability revisited.

Abstract Since Robert May's work on random community matrices it has been known that stability tends to decrease with complexity. Recently, it was shown that this is not necessarily true in competitive ecosystems. We investigated the stability of random ecosystems and found that it can largely be predicted by simple matrix statistics such as the mean and the variance of the interaction coefficients. We use this to explain why stability can increase as well as decrease with complexity in ecological communities. We argue that the variance, and to a lesser extent the mean, of the interaction coefficients go a long way in explaining patterns in the stability of ecosystems. Keywords Assembly, community ecology, community matrix, complexity, ecosystems, interaction matrix, Lotka-Volterra model, random matrix. In this paper we will reconcile these results and show that in random ecosystems the local stability and feasibility of the equilibrium depends on simple statistical properties of the interaction matrix, such as the mean and the variance of the interaction coefficients. This provides a more parsimonious explanation for stability than the complexity. We also discuss the importance of weak links in communities and argue that, rather than to look at the presence of weak interactions per se, the effect of weak interactions depends on their effect on the mean and the variance of the interaction matrix. To understand the effect of weak interactions they need to be compared with the distribution of the interaction coefficients in the rest of the community. M E T H O D S We used a generalized Lotka-Volterra interaction model to describe the population dynamics of a community of n interacting species. The density of species i is given by x i and changes according to The interaction coefficients, a ij , represent the per capita effect of interaction of an individual of species j on species i. The interaction matrix has the interaction coefficients as elements. The intraspecific interaction coefficients a ii are set to )1 and the basic growth rates, r i , are set to unity. The interspecific interaction coefficients are chosen randomly according to the following scheme: a ij ¼ 0 with probability 1 ) C and with probability C the interaction coefficient is drawn randomly from a uniform distribution on the interval [a, b]. The connectance, C, determines the fraction of links in the community. For competitive communities a, b £ 0, for food webs a £ 0 £ b. For simplicity we will refer to the interspecific interaction coefficients as interaction coefficients. The equilibrium densitiesx x i of this model can be found by solving the n equations: X n j¼1 a ijx x j ¼ À

Complexity and stability revisited.

Abstract Since Robert May's work on random community matrices it has been known that stability tends to decrease with complexity. Recently, it was shown that this is not necessarily true in competitive ecosystems. We investigated the stability of random ecosystems and found that it can largely be predicted by simple matrix statistics such as the mean and the variance of the interaction coefficients. We use this to explain why stability can increase as well as decrease with complexity in ecological communities. We argue that the variance, and to a lesser extent the mean, of the interaction coefficients go a long way in explaining patterns in the stability of ecosystems. Keywords Assembly, community ecology, community matrix, complexity, ecosystems, interaction matrix, Lotka-Volterra model, random matrix. In this paper we will reconcile these results and show that in random ecosystems the local stability and feasibility of the equilibrium depends on simple statistical properties of the interaction matrix, such as the mean and the variance of the interaction coefficients. This provides a more parsimonious explanation for stability than the complexity. We also discuss the importance of weak links in communities and argue that, rather than to look at the presence of weak interactions per se, the effect of weak interactions depends on their effect on the mean and the variance of the interaction matrix. To understand the effect of weak interactions they need to be compared with the distribution of the interaction coefficients in the rest of the community. M E T H O D S We used a generalized Lotka-Volterra interaction model to describe the population dynamics of a community of n interacting species. The density of species i is given by x i and changes according to The interaction coefficients, a ij , represent the per capita effect of interaction of an individual of species j on species i. The interaction matrix has the interaction coefficients as elements. The intraspecific interaction coefficients a ii are set to )1 and the basic growth rates, r i , are set to unity. The interspecific interaction coefficients are chosen randomly according to the following scheme: a ij ¼ 0 with probability 1 ) C and with probability C the interaction coefficient is drawn randomly from a uniform distribution on the interval [a, b]. The connectance, C, determines the fraction of links in the community. For competitive communities a, b £ 0, for food webs a £ 0 £ b. For simplicity we will refer to the interspecific interaction coefficients as interaction coefficients. The equilibrium densitiesx x i of this model can be found by solving the n equations: X n j¼1 a ijx x j ¼ À

Assessing the impact of different landscape features on post-fire forest recovery with multitemporal remote sensing data: the case of Mount Taygetos (southern Greece)

Fires affecting large areas usually create a mosaic of recovering plant communities reflecting their pre-fire composition and local conditions of burning. However, post-fire recovery patterns may also reveal the effects of landscape heterogeneity on the natural regeneration process of plant communities. This study combines field data and remote sensing image interpretation techniques to assess the role of various landscape characteristics in the post-fire recovery process in a mountainous region of Greece burned by a severe wildfire. Remote sensing techniques were used to accurately map secluded, large burned areas. By introducing a temporal component, we explored the correlation between post-fire regeneration and underlying topography, soils and basement rock. Pre-fire forest cover was reduced by more than half 8 years after fire. Regarding the dominant pre-fire forest trees, Abies cephalonica did not regenerate well after fire and most pre-fire stands were converted to grasslands and shrublands. In contrast, Pinus nigra regenerated sufficiently to return to its pre-fire cover, especially in areas underlain by softer basement rock. The use of different time series of high-resolution images improved the quality of the results obtained, justifying their use despite their high cost

How do complex food webs persist in nature?

Natural ecological communities are composed of a large and often indeterminate number of taxonomic species that trophically interact in myriad ways. Food webs describe the networks of these relations. While the population dynamics of individual species are often highly variable (Bjornstad and Grenfell, 2001), the overall structure of the trophic relations of the community, its food web, is comparatively more stable as they exhibit remarkably consistent patterns (Martinez, 1993b, 1994; Warren, 1994; Camacho et al., 2002a; Garlaschelli et al., 2003) and follow surprisingly consistent rules (Williams and Martinez, 2000; Camacho\ud et al., 2002b; Cattin et al., 2004). This consistency combined with population variability makes natural food webs both rather dynamically and structurally complex and also somewhat stable over ecological time. For the most part theory has been unable to explain these high levels of\ud complexity in terms of diversity and number of trophic relations because these elements are traditionally thought to decrease stability (May, 1973) and population persistence (Brose et al., 2003; Williams and Martinez, 2004c) in modeled communities. This disparity between real\ud patterns and those predicted by theory has been one of most pressing issues facing ecologists for the past few decades. If the mechanisms driving the trophic dynamics of natural communities are to be understood, this paradox needs to be resolved and a robust theoretical framework needs to be developed that adequately explains the persistence of complex food webs in a way that is consistent with high quality empirical data. Identification of the mechanisms or "devious strategies" (May, 1973) that permit the persistence of complex food webs would be a valuable discovery for community ecology and would resolve a major\ud paradigm within the complexity-stability debate (Brose et al., 2003). This chapter broadly outlines the current state of the complexity-stability relationship in food webs, the different approaches used to examine this issue, our current understanding of the mechanisms that appear to stabilize complex natural food webs and highlights some for\ud the most promising research directions for future focus