Fitting stochastic predator-prey models using both population density and kill rate data

Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i.e., a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability. The Fisher Information Matrix suggests that models with and without kill rate data are all identifiable, although correlations remain between parameters that belong to the same functional form. However, our results show that if the attractor is a fixed point in the absence of stochasticity, identifying parameters in practice requires kill rate data as a complement to the time series of population densities, due to the relatively flat likelihood. Only noisy limit cycle attractors can be identified directly from population count data (as in inverse modelling), although even in this case, adding kill rate data - including in small amounts - can make the estimates much more precise. Overall, we show that under process stochasticity in interaction rates, interaction data might be essential to obtain identifiable dynamical models for multiple species. These results may extend to other biotic interactions than predation, for which similar models combining interaction rates and population counts could be developed

Dampening prey cycle overrides the impact of climate change on predator population dynamics : a long-term demographic study on tawny owls

Funded by ERA-Net BiodivERsA NERC. Grant Numbers: NE/E010660/1, NE/F021402/1, NE/G002045/1Peer reviewedPublisher PD

Wildlife in a politically divided world: insularism inflates estimates of brown bear abundance

-Political borders dictate how biological diversity is monitored and managed, yet wild animals often move freely between jurisdictions. We quantified bias in brown bear (Ursus arctos) abundance estimates introduced when analytical methods ignore that the same individuals may be accounted for in more than one jurisdiction. A spatially explicit population model revealed that up to 49% of female bears detected in Norway via microsatellite analysis of scat and hair samples have their center of activity in neighboring countries (Finland, Russia, and Sweden). Not accounting for detections of “foreign residents” resulted in abundance estimates that were inflated by as much as 119%. Like man- agement and conservation, monitoring of transboundary wildlife populations should take place at ecologically relevant scales to avoid biased abundance es- timates and a false sense of control. When political realities isolate jurisdictions from their neighbors, spatially explicit analytical approaches can allow local or national programs a glimpse beyond their borders. Jurisdiction; large carnivore management; natural resource policy; noninvasive genetic monitoring; spatially explicit capture-recapture; transboundary wildlife

Integrated Population Models: Achieving their Potential

Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies

Analysis of bio-anode performance through electrochemical impedance spectroscopy

In this paper we studied the performance of bioanodes under different experimental conditions using polarization curves and impedance spectroscopy. We have identified that the large capacitances of up to 1 mF·cm− 2 for graphite anodes have their origin in the nature of the carbonaceous electrode, rather than the microbial culture. In some cases, the separate contributions of charge transfer and diffusion resistance were clearly visible, while in other cases their contribution was masked by the high capacitance of 1 mF·cm− 2. The impedance data were analyzed using the basic Randles model to analyze ohmic, charge transfer and diffusion resistances. Increasing buffer concentration from 0 to 50 mM and increasing pH from 6 to 8 resulted in decreased charge transfer and diffusion resistances; lowest values being 144 Ω·cm2 and 34 Ω·cm2, respectively. At acetate concentrations below 1 mM, current generation was limited by acetate. We show a linear relationship between inverse charge transfer resistance at potentials close to open circuit and saturation (maximum) current, associated to the Butler–Volmer relationship that needs further exploration.The authors wish to acknowledge funding from the European Union Seventh Framework Programme (FP7/2012-2016) project ‘Bioelectrochemical systems for metal production, recycling, and remediation’ under grant agreement no. 282970. AtH is supported by a NWO VENI grant no. 13631. OS was supported by the French environmental agency ADEME, by the Region Bretagne and by Rennes Metropole when doing the experiments. This work was performed in the cooperation framework of Wetsus, Centre of Excellence for Sustainable Water Technology (www.wetsus.nl). Wetsus is co-funded by the Dutch Ministry of Economic Affairs and Ministry of Infrastructure and Environment, the European Union Regional Development Fund, the Province of Fryslân, and the Northern Netherlands Provinces

Final Report of the ModSysC2020 Working Group - Data, Models and Theories for Complex Systems: new challenges and opportunities

Final Report of the ModSysC2020 Working Group at University Montpellier 2At University Montpellier 2, the modeling and simulation of complex systems has been identified as a major scientific challenge and one of the priority axes in interdisciplinary research, with major potential impact on training, economy and society. Many research groups and laboratories in Montpellier are already working in that direction, but typically in isolation within their own scientific discipline. Several local actions have been initiated in order to structure the scientific community with interdisciplinary projects, but with little coordination among the actions. The goal of the ModSysC2020 (modeling and simulation of complex systems in 2020) working group was to analyze the local situation (forces and weaknesses, current projects), identify the critical research directions and propose concrete actions in terms of research projects, equipment facilities, human resources and training to be encouraged. To guide this perspective, we decomposed the scientific challenge into four main themes, for which there is strong background in Montpellier: (1) modeling and simulation of complex systems; (2) algorithms and computing; (3) scientific data management; (4) production, storage and archiving of data from the observation of the natural and biological media. In this report, for each theme, we introduce the context and motivations, analyze the situation in Montpellier, identify research directions and propose specific actions in terms of interdisciplinary research projects and training. We also provide an analysis of the socio-economical aspects of modeling and simulation through use cases in various domains such as life science and healthcare, environmental science and energy. Finally, we discuss the importance of revisiting students training in fundamental domains such as modeling, computer programming and database which are typically taught too late, in specialized masters

Nonparametric Estimation of Natural Selection on a Quantitative Trait using Mark-Recapture Data

Assessing natural selection on a phenotypic trait in wild populations is of primary importance for evolutionary ecologists. To cope with the imperfect detection of individuals inherent to monitoring in the wild, we develop a nonparametric method for evaluating the form of natural selection on a quantitative trait using mark-recapture data. Our approach uses penalized splines to achieve flexibility in exploring the form of natural selection by avoiding the need to specify an a priori parametric function. If needed, it can help in suggesting a new parametric model. We employ Markov chain Monte Carlo sampling in a Bayesian framework to estimate model parameters. We illustrate our approach using data for a wild population of sociable weavers (Philetairus socius) to investigate survival in relation to body mass. In agreement with previous parametric analyses, we found that lighter individuals showed a reduction in survival. However, the survival function was not symmetric, indicating that body mass might not be under stabilizing selection as suggested previously

Variations in band reporting rate and implications for kill rate in Greater Snow Geese

We assessed spatial and temporal variation in reporting probability of banded Greater Snow Geese (Chen caerulescens atlantica) shot by hunters in eastern North America and evaluated potential residual biases in kill rate estimation. Adult Greater Snow Geese were marked with reward (value: US$10,$20, $30,$50, and $100) and standard bands ($0, control) in the Canadian Arctic from 2003 to 2005. We used a spatially explicit multinomial model based on 200 direct recoveries from 4256 banded geese to estimate reporting rate and harvest rate. We found that reporting rate for standard bands varied over time whereas harvest rate was higher in Canada than in the U.S. The reporting probability increased from 0.40 ± 0.11 in the first year of the study to 0.82 ± 0.14 and 0.84 ± 0.13 the second and third years, respectively. Overall, these reporting rates are higher than two previous estimates for this population, which leads to lower estimates of kill rate. However, the large annual differences in reporting rates found in this study lead to uncertainty in the estimation of kill rate. We suggest that the increase in reporting rate in the last two year of the study may be due to the dissemination of information among hunters regarding the presence of reward bands on birds, resulting in increased reporting rate for all bands. This raises issues about the need to adequately inform the public in such large-scale studies to avoid undesirable temporal trends over the course of the study

A Dynamic Occupancy Model for Interacting Species with Two Spatial Scales

Occupancy models have been extended to account for either multiple spatial scales or species interactions in a dynamic setting. However, as interacting species (e.g., predators and prey) often operate at different spatial scales, including nested spatial structure might be especially relevant to models of interacting species. Here we bridge these two model frameworks by developing a multi-scale, two-species occupancy model. The model is dynamic, i.e. it estimates initial occupancy, colonization and extinction probabilities—including probabilities conditional to the other species’ presence. With a simulation study, we demonstrate that the model is able to estimate most parameters without marked bias under low, medium and high average occupancy probabilities, as well as low, medium and high detection probabilities, with only a small bias for some parameters in low-detection scenarios. We further evaluate the model’s ability to deal with sparse field data by applying it to a multi-scale camera trapping dataset on a mustelid-rodent predator–prey system. Most parameters are estimated with low uncertainty (i.e. narrow posterior distributions). More broadly, our model framework creates opportunities to explicitly account for the spatial structure found in many spatially nested study designs, and to study interacting species that have contrasting movement ranges with camera traps.Supplementary materials accompanying this paper appear online.publishedVersio

11-2001 Newsletter

Minnesota State University, Mankato, Library Services Newsletter for November 2001