An individual behaviour‐based model can predict shorebird mortality using routinely collected shellfishery data

Abstract

Summary 1.The debate over the interaction between shellfishing and shorebirds is long-running. Behaviour-based models predict how animal populations are influenced by environmental change from the behavioural responses of individual animals to this change. These models are a potential tool for addressing shellfishery problems, but to be of value they must produce reliable predictions using data that are readily available or can be collected relatively quickly. 2. We parameterized a behaviour-based model for the oystercatcher population of the Wash, UK, for 1990-99 using data from shellfishery (mussels and cockles), shorebird and climate monitoring schemes. During the 1990s the overwinter mortality rates of Wash oystercatchers varied widely. The model correctly identified the years in which the observed overwinter mortality was either low (1-2%) or high (10-26%) from annual variation in the food supply, oystercatcher population size and temperature. 3. Many oystercatchers were observed and predicted to die when only a fraction of the available food was consumed. Within the model at least, this was because interference competition excluded the least dominant birds from part of the food supply and the least efficient foragers died before the food supply was fully depleted. A simplified model, which excluded interference and individual variation, incorrectly predicted that all birds survived in all years. Models that exclude these two components of behaviour will tend to underestimate the effect of mussel and cockle food shortage on oystercatchers. Shellfishery management based on such predictions may cause high oystercatcher mortality rates even though enough food would appear to be reserved for the birds. 4. Synthesis and applications . This study shows how a behaviour-based model can be parameterized and predict annual variation in oystercatcher mortality using data routinely collected from the Wash. The principle on which the model is based, that animals behave in order to maximize their chances of survival and reproduction, applies to any system, and the shellfishery, bird and climate data used to parameterize the model are widely available. The model can be used to advise how to manage shellfisheries, by predicting the proportion of the stock that needs to remain unfished in order to maintain low oystercatcher mortality rates.

Predicting site quality for shorebird communities: a case study on the Humber estuary, UK

“…Stillman et al (2000Stillman et al ( , 2001Stillman et al ( , 2003 give full details of the single-species version of the model. The model is individual-based and follows the behavioural decisions of each individual as it attempts to meet its daily energy requirements.…”

“…These models have also been termed behaviour-based models (e.g. Stillman et al 2000Stillman et al , 2001Stillman et al , 2003. The key feature of these models is that they are based on the assumption that individuals within animal populations always behave in order to maximise their own chances of survival and reproduction, no matter how much the environment changes.…”

Intertidal invertebrate harvesting: a meta-analysis of impacts and recovery in an important waterbird prey resource

“…To achieve an EBM approach to the management of intertidal ecosystems subjected to harvesting activities, it is therefore necessary to understand the consequences of harvesting on other components of the system. EBM has previously been implemented in this regard following collapses of Eurasian oystercatcher and common eider populations in the Dutch Wadden Sea (Camphuysen et al 2002, Verhulst et al 2004, and tools such as individual-based models can help predict population effects and inform management decisions (Atkinson et al 2003, Stillman et al 2003. The recent certification of the Ben Tre hand clam fishery in Vietnam by the Marine Stewardship Council demonstrates an example of sound EBM of an intertidal fishery (Marine Stewardship Council 2016).…”

Optimal Annual Routines: New Tools for Conservation Biology

“…The vast majority of existing examples have focused on depletion and interference (e.g., Pettifor et al 2000, Stillman et al 2000, Gill et al 2001). Both of these processes affect spatial and temporal variation in expected food intake, and so can predict distribution (Gill et al 2001), mortality (Stillman et al 2000(Stillman et al , 2003, and seasonal switches between different resources (Pettifor et al 2000).…”