Ecologists are frequently challenged with the task of making formal predictions about how populations will respond to different forms of management or perturbations. Whether making predictions about optimal harvest policies, invasive species control, or population recovery, the same principles apply. The broadly applicable key determinants of how a population responds to mortality events are (i) the general life-history strategy and (ii) how density dependent processes regulate the population dynamics rates.
The role of life history
General life-history strategy refers to the fact that there is more than one way to persist as a species. The most basic way to think about this is using the model of r and K selection theory. Life-history strategies that are r or K selected represent two “book ends” of a gradient of possible strategies. Being r selected means that the population has a high potential for growth. In other words, individuals of that species have a large reproductive capacity, giving the population its high potential for rapid growth. These species tend to grow fast, mature early and have short life spans. They will be able to sustain high exploitation rates, will be difficult to control with removal efforts, and will recover quickly from mortality events. Continue reading
Our recent paper sheds light on the issue of imperfect detection when evaluating patterns in fish abundance. We synthesize the information from numerous studies to develop a realistic expectation of when and how the detection probability should vary when sampling fish. Our discussion is presented in the context of evaluating lotic fish responses to flow-related aspects of the environment, but our conclusions are broadly applicable to any fish sampling, whether lotic, lentic or marine. Link to paper
The Virtual Ecologist refers to the practice of simulating complete experimental designs, that is, the ecological process, the observation process and the statistical analysis. This simulation tool can be used to evaluate trade-offs among experimental design components when design complexity prevents the use of traditional power analysis. This post is the third module of the Virtual Ecologist blog series. The first module introduced the Virtual Ecologist method and the second module demonstrated its use for exploring occupancy model designs. This third module will demonstrate the use of the Virtual Ecologist method for performing a statistical power analysis for occupancy models. Specifically, we will explore how the number of sampling site and replicate samples per site trade-off and influence the statistical power of a sampling design.
Some years back I was challenged with the questions of how to track fish species richness through time in a selection of freshwater lakes in Florida. I first started experimenting with some of the simpler nonparametric species richness estimators as I had seen them commonly used to answer questions about patterns in species richness in the ecological literature. However, my preliminary analyses revealed quite unrealistic results. My annual estimates of fish species richness could be quite variable through time in a given lake, giving the appearance that fish species become extirpated and recolonize lakes at high rates from year to year. Well this cannot be because the processes driving extirpation and recolonization must happen at a slower rate for fish in isolated lakes across the state of Florida. So, why do these richness estimators perform so poorly for describing these annual trends? It’s because all of these estimators in one way or another rely on the relative numbers or detections of species in your samples to infer the number of species present that you did not observe, which is a function of the true species abundance distribution and our imperfect ability to observe it. The logic behind this is that the more rare things you observe, the more likely it is that there or rare things out there that you haven’t observed. Thus, richness estimators tend to estimate a high number of unobserved species for communities dominated by species that are rare and will estimate a lower number of unobserved species for communities that have few observed rare species. There is a serious problem with this behavior of richness estimators because the true species abundance distribution of a community can be highly variable through time and across space while the members of the community stays constant. Continue reading
The Virtual Ecologist method is a form of stochastic simulation where the process of collecting, analyzing, and interpreting data are simulated. This post is the second module of the Virtual Ecologist blog series. The first module was an introduction to using the method for evaluating experimental designs. This module will introduce occupancy models and how to use the Virtual Ecologist approach to explore the design trade-off between the number of sample sites and the number of replicate samples per site. Future posts will extend this to formal power analysis and design optimization.
Occupancy models are used to describe species distributional patterns. The major virtue of these models is that they account for the detection process separately from the occurrence process, thereby producing unbiased descriptions of species distributions. The sampling design used for fitting occupancy models has two components. Firstly, there is a spatial component which consists of multiple locations or sites. The occupancy model serves to describe the pattern in species occurrence across these sites. If the sites are selected in some systematic way to represent a larger spatial region (e.g. randomly), the pattern in occurrence can be inferred to the this region. Secondly, there is replicated sampling at each site. This replication provides the information needed to estimate the detection probability of the species.
Posted in Ecological statistics, Ecology, Experimental design, population modeling
Tagged ecological monitoring, ecological statistics, environmental monitoring, Population model, simulations, Statistical power, virtual ecologist, wildlife management
A procedure for model selection has been recently showing up in the ecological literature that seems to have followed the increasing use of Bayesian hierarchical models. I was first introduced to the procedure by the book, “Hierarchical modeling and inference in ecology” (Royle and Dorazio 2008) a few years back and have seen the procedure used for multi-species occupancy models (e.g. Burton et al. 2012) and N-mixture models (e.g. Graves et al. 2012) in the peer reviewed literature, and have used it myself in Coggins et al. (2014). The procedure shifts the perspective of model selection from one that aims to determine the “best” model by using a criterion such as AIC or BIC to one that aims to determine the support of each parameter evaluated in the full model by modeling the probability of parameter inclusion directly with a mixture-modeling approach. Continue reading
Our new paper shows a divergent response between native and non-native floodplain wetland fish of the Murray River to different temporal scales of wetland connectivity to the river. This finding has implications for the management of native fish communities with environmental flows. For a copy, click this link or send me an email (email@example.com).
Flow alteration is one of the most common threats to rivers around the world. Common causes of river flow alteration are flow obstructions such as weirs and dams and water withdrawal for human and agricultural uses. These changes to rivers tend to suppress the natural flood regime and create a disconnect between the river and its flood plain. This disconnection can have detrimental effects for fish communities because many species of fish evolved to rely on periodic access to floodplain wetlands for food, refuge and nursery habitat. Continue reading