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Workshop 144-1: Assessing the dynamic demographic resilience of animal populations
Time:
Thursday, 20/June/2024:
2:30pm - 4:00pm
Location:Room L - Belmeloro Complex
Via Beniamino Andreatta, 8, 40126 Bologna
Global change presents wildlife with an unprecedented number and variety of challenges, e.g., climate change, novel diseases, urbanization, and hunting. In this context it is important to assess how resilient populations, species, and ecosystems are to disturbances. Such assessments require strong quantitative skills. Resilience is a central concept in ecological theory, and diverse methods have been developed to quantify it using empirically-collected data.
Studies of resilience have been limited mainly to higher levels of organization, such as communities or ecosystems. However, understanding the resilience of populations is at least as important because many management actions target this level of organization, and populations are best suited for common conservation actions such as restocking or translocation and reintroduction. Recently, Capdevilla and colleagues (2020) introduced the term "demographic resilience" to define population resilience and suggested quantifying it using methods developed for transient dynamics analysis that are applied to the matrix population model for the species in question.
Over time, the nature and intensity of disturbances may change, affecting demographic rates. Because demographic rates are used to calculate demographic resilience, we expect that demographic resilience also changes over time. However, so far demographic resilience has been assumed to be static. The assumption that resilience is static means that only a single demographic resilience value is calculated, which does not allow pinpointing the points or periods in time when the population was affected by the disturbance and, in turn, impairs our ability to suggest effective mitigation and conservation measures.
In this workshop, participants will learn about the theory of demographic resilience and the different metrics that are used to quantify it. We will introduce the commonly used 'bivariate approach' for quantifying resilience, which is based on measuring two resilience components: (i) the ability of a system to withstand disturbance (‘resistance’) and (ii) the ability of the system to recover from a disturbance, i.e., to return to its original state after the disturbance ('recovery'). The core of the workshop will focus on introducing the concept of dynamic demographic resilience (i.e. varying over time). We will present our newly developed R package for quantifying dynamic demographic resilience. We will demonstrate how our package can be used to measure dynamic demographic resilience and to compare it to the static demographic resilience.
Presentations
Resilience: history of use in community ecology
Viktoriia Radchuk, Julie Louvrier
Leibniz Institute for Zoo and Wildlife Research, Germany
This lecture will focus on the historical development of the terms “stability” and “resilience” in community ecology. Although both terms are central concepts to the field of ecology, their definition, meaning and applications have generated multiple controversies over the past decades. Indeed, resilience is often used in its narrow meaning as the ability of the system to recover to its pre-disturbed state following a disturbance (“engineering resilience”), which is centered on the existence of a single stable equilibrium. However, when defined sensu Holling, resilience is not limited to a single stable equilibrium and is more broadly applicable to the systems with multiple stable states, which are much more likely to be found in nature. Much confusion around the terms stability and resilience and the fact they were defined in multiple ways demonstrate the multidimensional character of both stability and resilience. This lecture will highlight different dimensions that can be measured for both stability and resilience and clarify the relation between these two concepts.
Theory of demographic resilience
Ella Worthington White1,2, Julie Louvrier1
1Leibniz Institute of Zoo and Wildlife Research, Germany; 2Freie Universität, Germany
This lecture will introduce the framework of demographic resilience (DR) that was recently proposed (Capdevila et al. 2020) to quantify resilience of populations to disturbances, inspired by a similar concept from community ecology. As with resilience in community ecology, DR is a multidimensional concept that consists of several different metrics. These metrics can be broadly grouped under two main resilience components: resistance and recovery. In addition to resistance that is measured analogously as at the community level (as the extent of the decline in population size after disturbance), compensation can be measured as part of DR (measured as the extent of increase in population size after disturbance) as some populations may exhibit overcompensatory dynamics . Similarly to how community matrices can be used to quantify resilience of communities, population matrix models can be subjected to the analysis of transient dynamics to compute DR metrics. This lecture will explain how to compute a set of such DR metrics, including: damping ratio, reactivity, maximal attenuation and maximal amplification. Further, we will introduce the transient envelope, which encompasses the most extreme possible increases and decreases of the population size after disturbance, and can thus be especially useful in comparative studies.
Practical and package walk-through: Assessing Time-Varying Demographic Resilience
Julie Louvrier, Viktoriia Radchuk
Lebniz Institute for Zoo and Wildlife Research Berlin, Germany
After the previous lectures in which participants will learn about (i) the historical development of resilience and stability as concepts in the field of community ecology and (ii) the theory of demographic resilience (DR), a recently formally defined concept, the participants will learn the importance of accounting for temporal variation when assessing DR. Until now, DR has been assumed to be static, which prevents pinpointing the periods in time when disturbances occurred. Yet, DR can vary over time as a response to changes in demographic rates of a population.
In this third part of the workshop, we will present “demres”, the R package we have developed for applying the time-varying approach. This third part will follow the format of a practical, and participants will be guided through R scripts on how to apply both time-invariant and time-varying approaches using our package to quantify DR metrics. We will demonstrate the functionality of the available functions as well as the figures that are possible to plot, in order to visualize the different metrics values over time.
