Global-scale predictions of community and ecosystem properties from simple ecological theory

We show how theoretical developments in macroecology, life-history theory and food-web ecology can be combined to formulate a simple model for predicting the potential biomass, production, size and trophic structure of consumer communities. The strength of our approach is that it uses remote sensing data to predict properties of consumer communities in environments that are challenging and expensive to sample directly. An application of the model to the marine environment on a global scale, using primary production and temperature estimates from satellite remote sensing as inputs, suggests that the global biomass of marine animals more than 10(-5) g wet weight is 2.62 x 10(9)t (=8.16 gm(-2) ocean) and production is 1.00 x 10(10) tyr-1 (31.15 gm(-2)yr(-1)). Based on the life-history theory, we propose and apply an approximation for distinguishing the relative contributions of different animal groups. Fish biomass and production, for example, are estimated as 8.99 x 10(8)t (2.80 gm(-2)) and 7.91 x 108 t yr(-1) (2.46 gm(2)yr(-1)respectively, and 50% of fish biomass is shown to occur in 17% of the total ocean area (8.22 gm(-2)). The analyses show that emerging ecological theory can be synthesized to set baselines for assessing human and climate impacts on global scales.     

Effects of climate‐driven primary production change on marine food webs: implications for fisheries and conservation

Climate change is altering the rate and distribution of primary production in the world's oceans. Primary production is critical to maintaining biodiversity and supporting fishery catches, but predicting the response of populations to primary production change is complicated by predation and competition interactions. We simulated the effects of change in primary production on diverse marine ecosystems across a wide latitudinal range in Australia using the marine food web model Ecosim. We link models of primary production of lower trophic levels (phytoplankton and benthic producers) under climate change with Ecosim to predict changes in fishery catch, fishery value, biomass of animals of conservation interest, and indicators of community composition. Under a plausible climate change scenario, primary production will increase around Australia and generally this benefits fisheries catch and value and leads to increased biomass of threatened marine animals such as turtles and sharks. However, community composition is not strongly affected. Sensitivity analyses indicate overall positive linear responses of functional groups to primary production change. Responses are robust to the ecosystem type and the complexity of the model used. However, model formulations with more complex predation and competition interactions can reverse the expected responses for some species, resulting in catch declines for some fished species and localized declines of turtle and marine mammal populations under primary productivity increases. We conclude that climate‐driven primary production change needs to be considered by marine ecosystem managers and more specifically, that production increases can simultaneously benefit fisheries and conservation. Greater focus on incorporating predation and competition interactions into models will significantly improve the ability to identify species and industries most at risk from climate change.     

On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change

Most climate change predictions omit species interactions and interspecific variation in dispersal. Here, we develop a model of multiple competing species along a warming climatic gradient that includes temperature-dependent competition, differences in niche breadth and interspecific differences in dispersal ability. Competition and dispersal differences decreased diversity and produced so-called 'no-analogue' communities, defined as a novel combination of species that does not currently co-occur. Climate change altered community richness the most when species had narrow niches, when mean community-wide dispersal rates were low and when species differed in dispersal abilities. With high interspecific dispersal variance, the best dispersers tracked climate change, out-competed slower dispersers and caused their extinction. Overall, competition slowed the advance of colonists into newly suitable habitats, creating lags in climate tracking. We predict that climate change will most threaten communities of species that have narrow niches (e.g. tropics), vary in dispersal (most communities) and compete strongly. Current forecasts probably underestimate climate change impacts on biodiversity by neglecting competition and dispersal differences.     

Local adaptation to temperature conserves top-down control in a grassland food web

A fundamental limitation in many climate change experiments is that tests represent relatively short-term 'shock' experiments and so do not incorporate the phenotypic plasticity or evolutionary change that may occur during the gradual process of climate change. However, capturing this aspect of climate change effects in an experimental design is a difficult challenge that few studies have accomplished. I examined the effect of temperature and predator climate history in food webs composed of herbaceous plants, generalist grasshopper herbivores and spider predators across a natural 4.8°C temperature gradient spanning 500 km in northeastern USA. In these grasslands, the effects of rising temperatures on the plant community are indirect and arise via altered predator-herbivore interactions. Experimental warming had no direct effect on grasshoppers, but reduced predation risk effects by causing spiders from all study sites to seek thermal refuge lower in the plant canopy. However, spider thermal tolerance corresponded to spider origin such that spiders from warmer study sites tolerated higher temperatures than spiders from cooler study sites. As a consequence, the magnitude of the indirect effect of spiders on plants did not differ along the temperature gradient, although a reciprocal transplant experiment revealed significantly different effects of spider origin on the magnitude of top-down control. These results suggest that variation in predator response to warming may maintain species interactions and associated food web processes when faced with long term, chronic climate warming.     

Bayesian phylogenetic model selection using reversible jump Markov chain Monte Carlo

A common problem in molecular phylogenetics is choosing a model of DNA substitution that does a good job of explaining the DNA sequence alignment without introducing superfluous parameters. A number of methods have been used to choose among a small set of candidate substitution models, such as the likelihood ratio test, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and Bayes factors. Current implementations of any of these criteria suffer from the limitation that only a small set of models are examined, or that the test does not allow easy comparison of non-nested models. In this article, we expand the pool of candidate substitution models to include all possible time-reversible models. This set includes seven models that have already been described. We show how Bayes factors can be calculated for these models using reversible jump Markov chain Monte Carlo, and apply the method to 16 DNA sequence alignments. For each data set, we compare the model with the best Bayes factor to the best models chosen using AIC and BIC. We find that the best model under any of these criteria is not necessarily the most complicated one; models with an intermediate number of substitution types typically do best. Moreover, almost all of the models that are chosen as best do not constrain a transition rate to be the same as a transversion rate, suggesting that it is the transition/transversion rate bias that plays the largest role in determining which models are selected. Importantly, the reversible jump Markov chain Monte Carlo algorithm described here allows estimation of phylogeny (and other phylogenetic model parameters) to be performed while accounting for uncertainty in the model of DNA substitution.