Reconstructing ecological networks with noisy dynamics

Ecosystems functioning is based on an intricate web of interactions among living entities. Most of these interactions are difficult to observe, especially when the diversity of interacting entities is large and they are of small size and abundance. To sidestep this limitation, it has become common to infer the network structure of ecosystems from time series of species abundance, but it is not clear how well can networks be reconstructed, especially in the presence of stochasticity that propagates through ecological networks. We evaluate the effects of intrinsic noise and network topology on the performance of different methods of inferring network structure from time-series data. Analysis of seven different four-species motifs using a stochastic model demonstrates that star-shaped motifs are differentially detected by these methods while rings are differentially constructed. The ability to reconstruct the network is unaffected by the magnitude of stochasticity in the population dynamics. Instead, interaction between the stochastic and deterministic parts of the system determines the path that the whole system takes to equilibrium and shapes the species covariance. We highlight the effects of long transients on the path to equilibrium and suggest a path forward for developing more ecologically sound statistical techniques

Species co-occurrence networks: Can they reveal trophic and non-trophic interactions in ecological communities?

Co‐occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co‐occurrence data from the rocky intertidal zone in central Chile to a well‐resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co‐occurrence data to better detect known trophic or non‐trophic, positive or negative interactions. We found a poor correspondence between the co‐occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non‐interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non‐trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co‐occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co‐occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche‐expanding positive non‐trophic interactions. Detection of links (sensitivity or specificity) was not higher for well‐known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co‐occurrence networks must be interpreted with caution, especially when extending interaction‐based ecological theory to interpret network variability and stability. Co‐occurrence networks may be particularly valuable for analysis of community dynamics that blends interactions and environment, rather than pairwise interactions alone

Trends in the Representation of Women Among US Geoscience Faculty From 1999 to 2020: The Long Road Toward Gender Parity

Inequalities persist in the geosciences. White women and people of color remain under-represented at all levels of academic faculty, including positions of power such as departmental and institutional leadership. While the proportion of women among geoscience faculty has been cataloged previously, new programs and initiatives aimed at improving diversity, focused on institutional factors that affect equity in the geosciences, necessitate an updated study and a new metric for quantifying the biases that result in under-representation. We compile a data set of 2,531 tenured and tenure-track geoscience faculty from 62 universities in the United States to evaluate the proportion of women by rank and discipline. We find that 27% of faculty are women. The fraction of women in the faculty pool decreases with rank, as women comprise 46% of assistant professors, 34% of associate professors, and 19% of full professors. We quantify the attrition of women in terms of a fractionation factor, which describes the rate of loss of women along the tenure track and allows us to move away from the metaphor of the “leaky pipeline.” Efforts to address inequities in institutional culture and biases in promotion and hiring practices over the past few years may provide insight into the recent positive shifts in fractionation factor. Our results suggest a need for 1:1 hiring between men and women to reach gender parity. Due to significant disparities in race, this work is most applicable to white women, and our use of the gender binary does not represent gender diversity in the geosciences