Contribution of conspecific negative density dependence to species diversity is increasing towards low environmental limitation in Japanese forests

Species coexistence is a result of biotic interactions, environmental and historical conditions. The Janzen-Connell hypothesis assumes that conspecific negative density dependence (CNDD) is one of the local processes maintaining high species diversity by decreasing population growth rates at high densities. However, the contribution of CNDD to species richness variation across environmental gradients remains unclear. In 32 large forest plots all over the Japanese archipelago covering > 40,000 individual trees of > 300 species and based on size distributions, we analysed the strength of CNDD of individual species and its contribution to species number and diversity across altitude, mean annual temperature, mean annual precipitation and maximum snow depth gradients. The strength of CNDD was increasing towards low altitudes and high tree species number and diversity. The effect of CNDD on species number was changing across altitude, temperature and snow depth gradients and their combined effects contributed 11–18% of the overall explained variance. Our results suggest that CNDD can work as a mechanism structuring forest communities in the Japanese archipelago. Strong CNDD was observed to be connected with high species diversity under low environmental limitations where local biotic interactions are expected to be stronger than in niche-based community assemblies under high environmental filtering.

Are we missing the forest for the trees? Conspecific negative density dependence in a temperate deciduous forest

One of the central goals of ecology is to determine the mechanisms that enable coexistence among species. Evidence is accruing that conspecific negative density dependence (CNDD), the process by which plant seedlings are unable to survive in the area surrounding adults of their same species, is a major contributor to tree species coexistence. However, for CNDD to maintain community-level diversity, three conditions must be met. First, CNDD must maintain diversity for the majority of the woody plant community (rather than merely specific groups). Second, the pattern of repelled recruitment must increase in with plant size. Third, CNDD should extend to the majority of plant life history strategies. These three conditions are rarely tested simultaneously. In this study, we simultaneously test all three conditions in a woody plant community in a North American temperate forest. We examined whether understory and canopy woody species across height categories and dispersal syndromes were overdispersed–a spatial pattern indicative of CNDD–using spatial point pattern analysis across life history stages and strategies. We found that there was a strong signal of overdispersal at the community level. Across the whole community, larger individuals were more overdispersed than smaller individuals. The overdispersion of large individuals, however, was driven by canopy trees. By contrast, understory woody species were not overdispersed as adults. This finding indicates that the focus on trees for the vast majority of CNDD studies may have biased the perception of the prevalence of CNDD as a dominant mechanism that maintains community-level diversity when, according to our data, CNDD may be restricted largely to trees.

Relating the Strength of Density Dependence and the Spatial Distribution of Individuals

Spatial patterns in ecology contain useful information about underlying mechanisms and processes. Although there are many summary statistics used to quantify these spatial patterns, there are far fewer models that directly link explicit ecological mechanisms to observed patterns easily derived from available data. We present a model of intraspecific spatial aggregation that quantitatively relates static spatial patterning to negative density dependence. Individuals are placed according to the colonization rule consistent with the Maximum Entropy Theory of Ecology (METE), and die with probability proportional to their abundance raised to a power α, a parameter indicating the degree of density dependence. This model can therefore be interpreted as a hybridization of MaxEnt and mechanism. Our model shows quantitatively and generally that increasing density dependence randomizes spatial patterning. α = 1 recovers the strongly aggregated METE distribution that is consistent with many ecosystems empirically, and as α → 2 our prediction approaches the binomial distribution consistent with random placement. For 1 < α < 2, our model predicts more aggregation than random placement but less than METE. We additionally relate our mechanistic parameter α to the statistical aggregation parameter k in the negative binomial distribution, giving it an ecological interpretation in the context of density dependence. We use our model to analyze two contrasting datasets, a 50 ha tropical forest and a 64 m2 serpentine grassland plot. For each dataset, we infer α for individual species as well as a community α parameter. We find that α is generally larger in the tightly packed forest than the sparse grassland, and the degree of density dependence increases at smaller scales. These results are consistent with current understanding in both ecosystems, and we infer this underlying density dependence using only empirical spatial patterns. Our model can easily be applied to other datasets where spatially explicit data are available.

On the inference of positive and negative species associations and their relation to abundance

Why do rare species persist in ecosystems? Rare species seem to be at a disadvantage by pure probabilistic odds and perhaps also from poorly adapted species-environment and species-species interactions, though negative density-dependence may help rare species persist. The question of rarity and persistence thus remains unresolved. In a recent paper, Calatayuda et al. (CEA) inferred species-species association networks from spatially replicated abundance data across many taxa and environments. CEA found that rare species were over-represented in positive association networks whereas common species were over-represented in negative association networks. However, the use of abundance and co-occurrence data to infer true species associations is difficult and often inaccurate. This issue arises in no small part because the underlying null models used to infer associations themselves are known to have type I and II error problems in real world applications. Here, I show that the finding of rare species being more represented in positive association networks as found by CEA can be explained by statistical artifacts in the inference of species associations from abundance data. It would therefore not be supported to assign biological interpretations to these findings until more data can be brought to bear on the subject or association types and the persistence of rare species.

Species abundance is jointly determined by functional traits and negative density dependence in a subtropical forest in southern China

Aims The factors affecting species abundance are a subject of ongoing debates in community ecology. Empirical studies have demonstrated that tree abundance is affected by plant functional traits and negative density dependence (NDD). However, few studies have focused on the combined effects of negative density dependence and plant functional traits on species abundance. Methods In this study, we used tree functional traits and two census data from a 50-ha forest dynamic plot in the Heishiding (HSD) Nature Reserve to explore the combined effects of functional traits and NDD on species abundance. Using hierarchical Bayesian models, we analyzed how neighbor densities affected the survival of saplings from 130 species and extracted posterior means of the coefficients to represent NDD. The structural equation modeling (SEM) analysis was then applied to investigate the causal relationships among species functional traits, negative density dependence, and species abundance. Important findings SEM showed that tree functional traits, including specific leaf area (SLA), leaf area (LA), leaf dry matter content (LDMC), leaf N content (LNC), maximum electron transport rate (ETRmax), and conspecific adult negative density dependence (CNDDadult), together explained 20% of the total variation in tree abundance. Specifically, SLA affected tree abundance both directly and indirectly via CNDDadult, with a totally negative influence on abundance. LDMC and LNC had only indirect effects mediated by CNDDadult on tree abundance. ETRmax and LA had directly negative effects on abundance, but their direct connections with CNDDadult were not observed. In addition, CNDDadult was negatively correlated with species abundance, indicating that abundant species are under stronger negative density dependence. Among these investigated traits, SLA contributed the most to the variation in CNDDadult and abundance. We argued that our findings of trait-CNDDadult-abundance relationships can improve our understanding of the determinants of species commonness and rarity in forests.