Conservation biologists are increasingly facing the dilemma of how to provide the public with valuable information obtained from long-term historical datasets, both with the aim of driving future research by encouraging others to make use of existing data, and motivating data contributors to continue their activities. Indeed, such datasets (e.g. ringing datasets) are often collected voluntarily by field researchers and/or citizen scientists, and require extensive manpower. These efforts have regularly provided conservation scientists with valuable information, even if not always analysed within a strict probabilistic framework (e.g. Martinez et al., 2016; Clewley et al., 2018). The “conditional” framework for long time-series analysis, which we adopted to analyse one of these extensive datasets, provided sound conclusions in line with those of relevant scientific literature (e.g. Klaasen et al. 2014 ; Molina-López et al., 2011), supporting the value of our results to the scientific community and wider general public, thus making our results fully worth reporting and timely.
The possible issues that related to hidden processes when estimating mortality, highlighted by XXXX, along with concerns about the power of the results obtained from conservation studies on mortality that do not adopt a strict probabilistic framework are, of course, worthy of consideration. We strongly welcome and support the widespread invitation to use CMRR models for the estimation of population parameters, especially when appropriate data are available. However, opportunistic historical ringing datasets, collected without ad-hoc probabilistic sampling design, are intrinsically affected by a series of biases that go beyond the ones listed by XXXX, and that sometimes even CMRR model assumptions may struggle with (Lebreton et al. 1992; Thorup et al., 2014).
