Emily Dennis, Calliste Fagard-Jenkin and Byron Morgan have created an R package for fitting the generalized abundance index to seasonal count data. The work has been published in Ecology and Evolution in the paper “rGAI: An R package for fitting the generalized abundance index to seasonal count data”.

The paper can be found at: https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.9200

The R package is available at: https://github.com/calliste-fagard-jenkin/rGAI

Members of SE@K group attended ISEC from 27th June to 1st July. Some went to South Africa to attend in person others attended virtually.

Eleni, Alex and Ioannis gave a half day workshop on Modelling environmental DNA data.

Eleni presented work on Capture recapture models with heterogeneous temporary.

Alex gave a talk on A unifying modelling framework for metabarcoding data.

Rachel talked on Model selection for integrated population models: selecting age structure with multiple data types.

James’ presentation was on Accounting for varying spatial scales in the production of UK butterfly abundance estimates.

Diana talked about Bayesian Identifiability in Ecological Models.

Fay presented work on Assessing the success of reintroductions whilst accounting for multispecies populations.

Bryon talked about Fitting dynamic occupancy models to very large occurrence data sets using hidden Markov models.

Diana is co-author on the paper: A guide to state–space modeling of ecological time series, which was published in Ecological Monographs.

https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1470

Abstract: State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture–recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. We provide the knowledge for ecologists to create SSMs that are robust to common, and often hidden, estimation problems, and the model selection and validation tools that can help them assess how well their models fit their data. We present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.

Diana along with Wei Cai, Stephanie Yurchak and Laura Cowen have published the paper:

Parameter redundancy in Jolly‐Seber tag loss models

in Ecology and Evolution

Abstract: 

1. Capture–recapture experiments are conducted to estimate population parameters such as population size, survival rates, and capture rates. Typically, individuals are captured and given unique tags, then recaptured over several time periods with the assumption that these tags are not lost. However, for some populations, tag loss cannot be assumed negligible. The Jolly‐Seber tag loss model is used when the no‐tag‐loss assumption is invalid. Further, the model has been extended to incorporate group heterogeneity, which allows parameters to vary by group membership. Many mark–recapture models become overparameterized resulting in the inability to independently estimate parameters. This is known as parameter redundancy.
2. We investigate parameter redundancy using symbolic methods. Because of the complex structure of some tag loss models, the methods cannot always be applied directly. Instead, we develop a simple combination of parameters that can be used to investigate parameter redundancy in tag loss models.
3. The incorporation of tag loss and group heterogeneity into Jolly‐Seber models does not result in further parameter redundancies. Furthermore, using hybrid methods we studied the parameter redundancy caused by data through case studies and generated tag histories with different parameter values.
4. Smaller capture and survival rates are found to cause parameter redundancy in these models. These problems resolve when applied to large populations.

Emily and Byron, along with Marc Kery, Armin Coray, Michael Schaub and Bruno Baur have published the paper:

Integrated modelling of insect population dynamics at two temporal scales

in Ecological Modelling.

Abstract:

Population size of species with birth-pulse life-cycles varies both within and between seasons, but most population dynamics models assume that a population can be characterised adequately by a single number within a season. However, within-season dynamics can sometimes be too substantial to be ignored when modelling dynamics between seasons. Typical examples are insect populations or migratory animals. Numerous models for only between-season dynamics exist, but very few have combined dynamics at both temporal scales.

In a new approach, we extend appreciably the models of Dennis et al. (2016b): we show how to adapt them for a generation time  year and fit an integrated population model for multiple data types, by maximising a joint likelihood for population counts of unmarked individuals and capture–recapture data from a study with marked individuals. We illustrate the approach using annual monitoring data for the endangered flightless beetle Iberodorcadion fuliginator from 18 populations in the Upper Rhine Valley for 1998–2016, with a 2-year life cycle. Standard likelihood methods are used for model fitting and comparison, and a concentrated (profile) likelihood approach provides computational efficiency.

Additional information from the capture–recapture data makes the population model more robust and, importantly, enables true, rather than relative, abundance to be estimated. A dynamic stopover model provides estimates of both survival and phenology parameters within a season, and also of productivity between seasons. For  I. fuliginator, we demonstrate a population decline since 1998 and how this links with productivity, which is affected by temperature. A delayed mean emergence date in recent years is also shown.

A main point of interest is the focus on the two temporal scales at which perhaps most animal populations vary: in the short-term, a population is seldom truly closed within a single season, and in the long-term (between seasons) it never is. Hence our models may serve as a template for a general description of population dynamics in many species. This includes rare species with limited data sets, for which there is a general lack of population dynamic models, yet conservation actions may greatly benefit from this kind of models.