Keith J. Fritschie

Biodiversity simultaneously enhances the production and stability of community biomass, but the effects are independent

To predict the ecological consequences of biodiversity loss, researchers have spent much time and effort quantifying how biological variation affects the magnitude and stability of ecological processes that underlie the functioning of ecosystems. Here we add to this work by looking at how biodiversity jointly impacts two aspects of ecosystem functioning at once: (1) the production of biomass at any single point in time (biomass/area or biomass/ volume), and (2) the stability of biomass production through time (the CV of changes in total community biomass through time). While it is often assumed that biodiversity simultaneously enhances both of these aspects of ecosystem functioning, the joint distribution of data describing how species richness regulates productivity and stability has yet to be quantified. Furthermore, analyses have yet to examine how diversity effects on production covary with diversity effects on stability. To overcome these two gaps, we reanalyzed the data from 34 experiments that have manipulated the richness of terrestrial plants or aquatic algae and measured how this aspect of biodiversity affects community biomass at multiple time points. Our reanalysis confirms that biodiversity does indeed simultaneously enhance both the production and stability of biomass in experimental systems, and this is broadly true for terrestrial and aquatic primary producers. However, the strength of diversity effects on biomass production is independent of diversity effects on temporal stability. The independence of effect sizes leads to two important conclusions. First, while it may be generally true that biodiversity enhances both productivity and stability, it is also true that the highest levels of productivity in a diverse community are not associated with the highest levels of stability. Thus, on average, diversity does not maximize the various aspects of ecosystem functioning we might wish to achieve in conservation and management. Second, knowing how biodiversity affects productivity gives no information about how diversity affects stability (or vice versa). Therefore, to predict the ecological changes that occur in ecosystems after extinction, we will need to develop separate mechanistic models for each independent aspect of ecosystem functioning.

The influence of phylogenetic relatedness on species interactions among freshwater green algae in a mesocosm experiment

Summary 1. A long-standing hypothesis in ecology and evolutionary biology is that closely related species are more ecologically similar to each other and therefore compete more strongly than distant relatives do. A recent hypothesis posits that evolutionary relatedness may also explain the prevalence of mutualisms, with facilitative interactions being more common among distantly related species. Despite the importance of these hypotheses for understanding the structure and function of ecological communities, experimental tests to determine how evolutionary relatedness influences competition and facilitation are still somewhat rare. 2. Here, we report results of a laboratory experiment in which we assessed how competitive and facilitative interactions among eight species of freshwater green algae are influenced by their relatedness. We measured the prevalence of competition and facilitation among 28 pairs of freshwater green algal species that were chosen to span a large gradient of phylogenetic distances. For each species, we first measured its invasion success when introduced into a steady-state population of another resident species. Then, we compared its growth rate when grown alone in monoculture to its growth rate when introduced as an invader. The change in the species’ population growth rate as an invader (sensitivity) is used as a measure of the strength of its interaction with the resident species. A reduced growth rate in the presence of another species indicates competition, whereas an increased growth rate indicates facilitation. 3. Although competition between species was more frequent (75% of interactions), facilitation was common (the other 25% of interactions). We found no significant relationship between the phylogenetic distance separating two interacting species and the success of invasion, nor the prevalence or strength of either competition or facilitation. Interspecific interactions depended more on the identity of the species, with certain taxa consistently acting as good or bad competitors/facilitators. These species were not predictable a priori from their positions on a phylogeny. 4. Synthesis. The phylogenetic relatedness of the green algae species used here did not predict the prevalence of competitive and facilitative interactions, rejecting the hypothesis that close relatives compete strongly and contesting recent evidence that facilitation is likely to occur between distant relatives.

Evolutionary relatedness does not predict competition and co-occurrence in natural or experimental communities of green algae

The competition-relatedness hypothesis (CRH) predicts that the strength of competition is the strongest among closely related species and decreases as species become less related. This hypothesis is based on the assumption that common ancestry causes close relatives to share biological traits that lead to greater ecological similarity. Although intuitively appealing, the extent to which phylogeny can predict competition and co-occurrence among species has only recently been rigorously tested, with mixed results. When studies have failed to support the CRH, critics have pointed out at least three limitations: (i) the use of data poor phylogenies that provide inaccurate estimates of species relatedness, (ii) the use of inappropriate statistical models that fail to detect relationships between relatedness and species interactions amidst nonlinearities and heteroskedastic variances, and (iii) overly simplified laboratory conditions that fail to allow eco-evolutionary relationships to emerge. Here, we address these limitations and find they do not explain why evolutionary relatedness fails to predict the strength of species interactions or probabilities of coexistence among freshwater green algae. First, we construct a new data-rich, transcriptome-based phylogeny of common freshwater green algae that are commonly cultured and used for laboratory experiments. Using this new phylogeny, we re-analyse ecological data from three previously published laboratory experiments. After accounting for the possibility of nonlinearities and heterogeneity of variances across levels of relatedness, we find no relationship between phylogenetic distance and ecological traits. In addition, we show that communities of North American green algae are randomly composed with respect to their evolutionary relationships in 99% of 1077 lakes spanning the continental United States. Together, these analyses result in one of the most comprehensive case studies of how evolutionary history influences species interactions and community assembly in both natural and experimental systems. Our results challenge the generality of the CRH and suggest it may be time to re-evaluate the validity and assumptions of this hypothesis.

Advancing Ecosystem Science by Promoting Greater Use of Theory and Multiple Research Approaches in Graduate Education

Since the inaugural edition of Ecosystems was published in 1998, ecosystem science has undergone substantial changes including the development of new research methods and an increasing emphasis on collaborations across traditional academic boundaries. In response to this transformation, we reflect on the current state of theory in ecosystem science, and make recommendations for training the next generation of Ph.D.-level ecosystem scientists. Specifically, we call for increased integration of theory into ecosystem science and outline the utility of iterating between theory and data generated by observations, experiments, and quantitative models. We recommend exposing graduate students to these three major approaches for generating data and propose strategies that students, advisors, and departments can employ to ensure this exposure. Ultimately, a successful training program will provide students with an understanding of key theories related to ecosystem science and how they interact with data, an appreciation for the interconnectedness of approaches to scientific inference, and a well-developed skill set in at least one approach—thereby empowering them to confidently tackle our pressing environmental problems. Although this is a daunting list of goals, continuing to advance our understanding of how ecosystems function necessitates a rigorous and well-developed training program.