The Ethics of a Co-regulatory Model for Farm Animal Welfare Research

Standards for farm animal welfare are variously managed at a national level by government-led regulatory control, by consumer-led welfare economics and co-regulated control in a partnership between industry and government. In the latter case the control of research to support animal welfare standards by the relevant industry body may lead to a conflict of interest on the part of researchers, who are dependent on industry for continued research funding. We examine this dilemma by reviewing two case studies of research published under an Australian co-regulated control system. Evidence of unsupported conclusions that are favourable to industry is provided, suggesting that researchers do experience a conflict of interest that may influence the integrity of the research. Alternative models for the management of research are discussed, including the establishment of an independent research management body for animal welfare because of its public good status and the use of public money derived from taxation, with representation from government, industry, consumers, and advocacy groups.

Hazard Function Models to Estimate Mortality Rates Affecting Fish Populations with Application to the Sea Mullet (Mugil cephalus) Fishery on the Queensland Coast (Australia)

Fisheries management agencies around the world collect age data for the purpose of assessing the status of natural resources in their jurisdiction. Estimates of mortality rates represent a key information to assess the sustainability of fish stocks exploitation. Contrary to medical research or manufacturing where survival analysis is routinely applied to estimate failure rates, survival analysis has seldom been applied in fisheries stock assessment despite similar purposes between these fields of applied statistics. In this paper, we developed hazard functions to model the dynamic of an exploited fish population. These functions were used to estimate all parameters necessary for stock assessment (including natural and fishing mortality rates as well as gear selectivity) by maximum likelihood using age data from a sample of catch. This novel application of survival analysis to fisheries stock assessment was tested by Monte Carlo simulations to assert that it provided unbiased estimations of relevant quantities. The method was applied to the data from the Queensland (Australia) sea mullet (Mugil cephalus) commercial fishery collected between 2007 and 2014. It provided, for the first time, an estimate of natural mortality affecting this stock: 0.22±0.08 year −1 .