Many methods have been developed in the last 70 years to predict the natural mortality rate, M, of a stock based on empirical evidence from comparative life history studies. These indirect or empirical methods are used in most stock assessments to (i) obtain estimates of M in the absence of direct information, (ii) check on the reasonableness of a direct estimate of M, (iii) examine the range of plausible M estimates for the stock under consideration, and (iv) define prior distributions for Bayesian analyses. The two most cited empirical methods have appeared in the literature over 2500 times to date. Despite the importance of these methods, there is no consensus in the literature on how well these methods work in terms of prediction error or how their performance may be ranked. We evaluate estimators based on various combinations of maximum age (tmax), growth parameters, and water temperature by seeing how well they reproduce >200 independent, direct estimates of M. We use tenfold cross-validation to estimate the prediction error of the estimators and to rank their performance. With updated and carefully reviewed data, we conclude that a tmax-based estimator performs the best among all estimators evaluated. The tmax-based estimators in turn perform better than the Alverson–Carney method based on tmax and the von Bertalanffy K coefficient, Pauly’s method based on growth parameters and water temperature and methods based just on K. It is possible to combine two independent methods by computing a weighted mean but the improvement over the tmax-based methods is slight. Based on cross-validation prediction error, model residual patterns, model parsimony, and biological considerations, we recommend the use of a tmax-based estimator (M=4.899tmax−0.916, prediction error = 0.32) when possible and a growth-based method (M=4.118K0.73L∞−0.33 , prediction error = 0.6, length in cm) otherwise.

Three common cross‐sectional catch‐curve methods for estimating total mortality rate (Z) are the Chapman–Robson, regression, and Heincke estimators. There are five unresolved methodological issues: (1) which is the best estimator, (2) how one should determine the first age‐group to use in the analysis, (3) how the variance estimators perform; and, for regression estimators, (4) how the observations should be weighted, including (5) whether and how the oldest ages should be truncated. We used analytical methods and Monte Carlo simulation to evaluate the three catch‐curve methods, including unweighted and weighted versions of the regression estimator. We evaluated four criteria for specifying the first age‐class used. Regression estimators were evaluated with four different methods of right data truncation. Heincke's method performed poorly and is generally not recommended. The two‐tailed χ2 test and one‐tailed z‐test for full selectivity described by Chapman and Robson did not perform as well as simpler criteria and are not recommended. Estimates with the lowest mean square error were generally provided by (1) the Chapman–Robson estimator with the age of full recruitment being the age of maximum catch plus 1 year and (2) the weighted regression estimator with the age of full recruitment being the age of maximum catch and with no right truncation. Differences in performance between the two methods were small (<6% of Z). The Chapman–Robson estimator of the variance of had large negative bias when not corrected for overdispersion; once corrected, it performed as well as or better than all other variance estimators evaluated. The regression variance estimator is generally precise and slightly negatively biased. We recommend that the traditional Chapman–Robson approach be corrected for overdispersion and used routinely to estimate Z. Weighted linear regression may work slightly better but is completely ad hoc. Unweighted linear regression should no longer be used for analyzing catch‐curve data. Received November 30, 2011; accepted July 4, 2012

No abstract

Coastal cetaceans in Southeast Asia are poorly studied and are particularly vulnerable to anthropogenic threats, especially in intensive fishing grounds. To investigate the distribution and habitat characteristics of cetaceans in the productive coastal waters of Matang, Perak, Malaysia, boat‐based line transect surveys were conducted between 2013 and 2016. The Irrawaddy dolphin (Orcaella brevirostris) was most frequently encountered at 3.87 sightings per 100 km, followed by the Indo‐Pacific finless porpoise (Neophocaena phocaenoides) at 1.72 sightings per 100 km, and the Indo‐Pacific humpback dolphin (Sousa chinensis) at 0.66 sightings per 100 km. The mean group size was largest for humpback dolphins (8.4 individuals), followed by Irrawaddy dolphins (6.4 individuals), and finless porpoises (2.8 individuals). Humpback dolphins exhibited a clustered distribution concentrated mainly in shallow estuarine waters (<10 m deep and <5 km from river mouths), whereas Irrawaddy dolphins were more widely distributed in farther coastal waters (<15 m deep and <15 km from river mouths), and finless porpoises were mostly found farthest from the shore in coastal waters (10–25 m deep and >15 km from river mouths). The spatial distribution of the three cetaceans overlapped minimally, and this is likely to reflect the distribution of preferred prey resources, species interactions, and their differential responses to anthropogenic activities and species dominance. The results from our study serve as baseline information for future research, conservation, and habitat management of these vulnerable and endangered coastal cetaceans. Conservation actions are recommended for the Matang area.

No abstract

No abstract

Length-based methods for estimating the total mortality rate, Z, are appealing due to their potential application in data-poor situations, particularly when assessing tropical and invertebrate fisheries where age composition data are lacking. We evaluated two length-based estimators attributed to Beverton and Holt (1956) and to Ehrhardt and Ault (1992) for precision and accuracy when applied to simulated length data generated under varying combinations of Z rates, growth rates, variability in length at age, and the degree of length truncation imposed by the data analyst. The Beverton-Holt method generally overestimated Z, with bias ranging from ¡5% to C40%, when the abundance of the oldest age-groups is less than that associated with a constant mortality rate. The bias in the Ehrhardt-Ault method ranged from ¡80% to C140%, depending on the combinations of Z and the von Bertalanffy growth coefficient K, the degree of imposed length truncation, and the method for mean length calculation. In general, the Ehrhardt-Ault estimator exhibited complex behavior, which made it difficult to summarize the direction and magnitude of the bias and the mean square error. The best length truncation to impose on the length samples to apply the Ehrhardt-Ault method often did not coincide with the "true" length of truncation especially with more realistic scenarios of variability in length at age. The Beverton-Holt method has the advantage of having known directional biases and predictable behavior. Use of the Ehrhardt-Ault estimator should be accompanied by a case-specific evaluation of its likely performance.Length-based methods for assessing stock status are valuable tools for data-poor stocks where age-based methods are typically not applicable. The idea behind these methods is to use length information as a proxy for age. One of the earliest of such methods to estimate total instantaneous mortality rate, Z, from length distribution was developed by Beverton and Holt (1956). The Beverton-Holt (BH) estimator of Z is based on the mean length of a sample of fully recruited fish as well as knowledge of the von Bertalanffy growth parameters. The estimator is applied under the assumption of equilibrium conditions; i.e.,

Despite a growing interest in interdisciplinary research, systematic ways of how to integrate data from different disciplines are still scarce. We argue that successful resource management relies on two key data sources: natural science data, which represents ecosystem structure and processes, and social science data, which describes people's perceptions and understanding. Both are vital, mutually complementing information sources that can underpin the development of feasible and effective policies and management interventions. To harvest the added value of combined knowledge, a uniform scaling system is needed. In this paper, we propose a standardized methodology to connect and explore different types of quantitative data from the natural and social sciences reflecting temporal trends in ecosystem quality. We demonstrate this methodology with different types of data such as fisheries stocks and mangrove cover on the one hand and community's perceptions on the other. The example data are collected from three United Nations Educational Scientific and Cultural Organization (UNESCO) Biosphere reserves and one marine park in Southeast Asia. To easily identify patterns of convergence or divergence among the datasets, we propose heat maps using colour codes and icons for language- and education-independent understandability. Finally, we discuss the limitations as well as potential implications for resource management and the accompanying communication strategies. This article is part of the theme issue ‘Nurturing resilient marine ecosystems’.