Harnessing global fisheries to tackle micronutrient deficiencies

There is considerable debate about whether community ecology will ever produce general principles. We suggest here that this can be achieved but that community ecology has lost its way by focusing on pairwise species interactions independent of the environment. We assert that community ecology should return to an emphasis on four themes that are tied together by a two-step process: how the fundamental niche is governed by functional traits within the context of abiotic environmental gradients; and how the interaction between traits and fundamental niches maps onto the realized niche in the context of a biotic interaction milieu. We suggest this approach can create a more quantitative and predictive science that can more readily address issues of global change.

Probabilistic programming allows for automatic Bayesian inference on user-defined probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling allow inference on increasingly complex models. This class of MCMC, known as Hamiltonian Monte Carlo, requires gradient information which is often not readily available. PyMC3 is a new open source probabilistic programming framework written in Python that uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed. Contrary to other probabilistic programming languages, PyMC3 allows model specification directly in Python code. The lack of a domain specific language allows for great flexibility and direct interaction with the model. This paper is a tutorial-style introduction to this software package.

Understanding the processes shaping biological communities under multiple disturbances is a core challenge in ecology and conservation science. Traditionally, ecologists have explored linkages between the severity and type of disturbance and the taxonomic structure of communities. Recent advances in the application of species traits, to assess the functional structure of communities, have provided an alternative approach that responds rapidly and consistently across taxa and ecosystems to multiple disturbances. Importantly, trait-based metrics may provide advanced warning of disturbance to ecosystems because they do not need species loss to be reactive. Here, we synthesize empirical evidence and present a theoretical framework, based on species positions in a functional space, as a tool to reveal the complex nature of change in disturbed ecosystems. Copyright © 2012 Elsevier Ltd. All rights reserved.