Null model analysis of species nestedness patterns

Nestedness is a common biogeographic pattern in which small communities form proper subsets of large communities. However, the detection of nestedness in binary presence-absence matrices will be affected by both the metric used to quantify nestedness and the reference null distribution. In this study, we assessed the statistical performance of eight nestedness metrics and six null model algorithms. The metrics and algorithms were tested against a benchmark set of 200 random matrices and 200 nested matrices that were created by passive sampling. Many algorithms that have been used in nestedness studies are vulnerable to type I errors (falsely rejecting a true null hypothesis). The best-performing algorithm maintains fixed row and fixed column totals, but it is conservative and may not always detect nestedness when it is present. Among the eight indices, the popular matrix temperature metric did not have good statistical properties. Instead, the Brualdi and Sanderson discrepancy index and Cutler's index of unexpected presences performed best. When used with the fixed-fixed algorithm, these indices provide a conservative test for nestedness. Although previous studies have revealed a high frequency of nestedness, a reanalysis of 288 empirical matrices suggests that the true frequency of nested matrices is between 10% and 40%.     

Spatial and temporal patterns of reproduction,larval settlement,and recruitment of the compound ascidian Aplidium stellatum

Spatial and temporal variation in recruitment of the compound ascidian Aplidium stellatum was examined on a shallow-water limestone outcropping in the Northeastern Gulf of Mexico from 1983 to 1985. Fifty-two percent of the recruits appeared on vertical surfaces, which were rare at this site. In the laboratory, tadpole larvae of A. stellatum consistently settled on vertical vs horizontal surfaces in a 2 to 1 ratio, regardless of the area of vertical surface offered. This settlement response was insufficient to account entirely for the field recruitment pattern, suggesting greater mortality of newly-settled individuals or larvae on horizontal vs vertical surfaces. Fifty-six percent of the variation in recruitment over 21 months could be explained by variation in the percentage of zooids brooding larvae in adult colonies. Over 28 consecutive months, the percentage of zooids brooding larvae was positively correlated with egg counts of the previous month (r ²=0.75), which in turn were weakly correlated with monthly average water temperature (r ²=0.36). These results suggest that larval production contributed substantially to temporal variation in recruitment of A. stellatum. The short larval life of tadpoles of A. stellatum and the relative isolation of the population were probably responsible for the correlation between recruitment and larval production.     

Forecasting extinction risk with nonstationary matrix models.

Matrix population growth models are standard tools for forecasting population change and for managing rare species, but they are less useful for predicting extinction risk in the face of changing environmental conditions. Deterministic models provide point estimates of lambda, the finite rate of increase, as well as measures of matrix sensitivity and elasticity. Stationary matrix models can be used to estimate extinction risk in a variable environment, but they assume that the matrix elements are randomly sampled from a stationary (i.e., non-changing) distribution. Here we outline a method for using nonstationary matrix models to construct realistic forecasts of population fluctuation in changing environments. Our method requires three pieces of data: (1) field estimates of transition matrix elements, (2) experimental data on the demographic responses of populations to altered environmental conditions, and (3) forecasting data on environmental drivers. These three pieces of data are combined to generate a series of sequential transition matrices that emulate a pattern of long-term change in environmental drivers. Realistic estimates of population persistence and extinction risk can be derived from stochastic permutations of such a model. We illustrate the steps of this analysis with data from two populations of Sarracenia purpurea growing in northern New England. Sarracenia purpurea is a perennial carnivorous plant that is potentially at risk of local extinction because of increased nitrogen deposition. Long-term monitoring records or models of environmental change can be used to generate time series of driver variables under different scenarios of changing environments. Both manipulative and natural experiments can be used to construct a linking function that describes how matrix parameters change as a function of the environmental driver. This synthetic modeling approach provides quantitative estimates of extinction probability that have an explicit mechanistic basis.     

Null model analysis of species associations using abundance data

The influence of negative species interactions has dominated much of the literature on community assembly rules. Patterns of negative covariation among species are typically documented through null model analyses of binary presence/absence matrices in which rows designate species, columns designate sites, and the matrix entries indicate the presence (1) or absence (0) of a particular species in a particular site. However, the outcome of species interactions ultimately depends on population-level processes. Therefore, patterns of species segregation and aggregation might be more clearly expressed in abundance matrices, in which the matrix entries indicate the abundance or density of a species in a particular site. We conducted a series of benchmark tests to evaluate the performance of 14 candidate null model algorithms and six covariation metrics that can be used with abundance matrices. We first created a series of random test matrices by sampling a metacommunity from a lognormal species abundance distribution. We also created a series of structured matrices by altering the random matrices to incorporate patterns of pairwise species segregation and aggregation. We next screened each algorithm-index combination with the random and structured matrices to determine which tests had low Type I error rates and good power for detecting segregated and aggregated species distributions. In our benchmark tests, the best-performing null model does not constrain species richness, but assigns individuals to matrix cells proportional to the observed row and column marginal distributions until, for each row and column, total abundances are reached. Using this null model algorithm with a set of four covariance metrics, we tested for patterns of species segregation and aggregation in a collection of 149 empirical abundance matrices and 36 interaction matrices collated from published papers and posted data sets. More than 80% of the matrices were significantly segregated, which reinforces a previous meta-analysis of presence/absence matrices. However, using two of the metrics we detected a significant pattern of aggregation for plants and for the interaction matrices (which include plant-pollinator data sets). These results suggest that abundance matrices, analyzed with an appropriate null model, may be a powerful tool for quantifying patterns of species segregation and aggregation.     

Linking the brown and green: nutrient transformation and fate in the Sarracenia microecosystem

Linkages between detritus-based ("brown") food webs and producer-based ("green") food webs are critical components of ecosystem functionality, but these linkages are hard to study because it is difficult to measure release of nutrients by brown food webs and their subsequent uptake by plants. In a three-month greenhouse experiment, we examined how the detritus-based food web inhabiting rain-filled leaves of the pitcher plant Sarracenia purpurea affects nitrogen transformation and its subsequent uptake by the plant itself. We used isotopically enriched prey (detritus) and soluble inorganic nitrogen, and manipulated food web structure to determine whether the presence of a complete brown web influences uptake efficiency of nitrogen by the plant. Uptake efficiency of soluble inorganic nitrogen was greater than that of nitrogen derived from mineralized prey. Contrary to expectation, there was no effect of the presence in the food web of macroinvertebrates on uptake efficiency of either form of nitrogen. Further, uptake efficiency of prey-derived nitrogen did not differ significantly among S. purpurea and two congeneric species (S. flava and S. alata) that lack associated food webs. Although upper trophic levels of this brown food web actively process detritus, it is the activity of the microbial component of this web that ultimately determines nitrogen availability for S. purpurea.