Estimating fishing and natural mortality rates, and catchability coefficient, from a series of observations on mean length and fishing effort

Gedamke and Hoenig (2006) (Transactions of the American Fisheries Society, 135: 476-487) developed a non-equilibrium version of the Beverton and Holt estimator of total mortality rate, Z, based on mean length and thereby increased the usefulness of length-based methods. In this study, we extend their model by replacing period-specific Z parameters with the year-specific parameterization Z(y) = qf(y) + M where q is the catchability coefficient, f(y) is the fishing effort in year y, F (= qf) is the fishing mortality rate, and M is the natural mortality rate. Thus, the problem reduces to estimating just three parameters: q, M and residual variance. We used Monte Carlo simulation to study the model behaviour. Estimates of q and M are highly negatively correlated and may or may not be reliable; however, the estimates of corresponding Z\u27s are more precise than estimates of F and are generally reliable, even when uncertainty about the mean lengths is high. This length-based method appears to work best for stocks with rapid growth rate. Contrast in effort data may not be necessary for reliable estimates of Z\u27s. This approach forms a bridge between data-limited models and more complex models. We apply the method to the Norway lobster Nephrops norvegicus stock in Portugal as an example

Correcting for effective area fished in fishery-dependent depletion estimates of abundance and capture efficiency

Depletion methods are widely used to estimate capture efficiency and abundance. However, they are highly dependent on the depletion area assumed. In open-ocean depletion studies, it is difficult to determine the true area of depletion. Satellite vessel monitoring systems (VMS) offer the potential to determine the area effectively fished. Observer-collected catch-and-effort data from the 1999 Atlantic sea scallop fishery in Georges Bank Closed Area II were used to obtain spatially-explicit DeLury depletion estimates of dredge efficiency and abundance, with corrections for fished area made using VMS data. Non-area-corrected efficiency estimates often had theoretically impossible values, indicating that the naively assumed fished area was likely too big. Fine-scale spatial analyses on individual depletion cells confirmed this result. Corrected-area efficiency estimates exhibited reduced variability and more plausible efficiencies, with 70% of 289 individual depletion estimates failing between 20% and 55%, with a mean of 46%. Abundance estimates from individual depletion studies matched maps of abundance from a preseason survey. Results indicated a total abundance of similar to 17 million pounds of scallop meat weight in the fished area, of which 6 million pounds were landed, providing an overall exploitation rate of 35%

Evaluating the predictive performance of empirical estimators of natural mortality rate using information on over 200 fish species

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 (t(max)), growth parameters, and water temperature by seeing how well they reproduce \u3e200 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 t(max)-based estimator performs the best among all estimators evaluated. The t(max)-based estimators in turn perform better than the Alverson-Carney method based on t(max) and the von Bertalanffy K coefficient, Pauly\u27s 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 t(max)-based methods is slight. Based on cross-validation prediction error, model residual patterns, model parsimony, and biological considerations, we recommend the use of a t(max)-based estimator (M = 4.899t(max)(-0.916), prediction error = 0.32) when possible and a growth-based method (M = 4.118K(0.73)L(infinity)(-0.33), prediction error = 0.6) otherwise

A novel tag-recovery model with two size classes for estimating fishing and natural mortality, with implications for the southern rock lobster (Jasus edwardsii) in Tasmania, Australia

Multi-year tag-recovery models can be used to derive estimates of age- and year-specific annual survival rates and year-specific instantaneous fishing and natural mortality rates. The latter, which are often of interest to fisheries managers, usually can only be estimated when the tag-reporting rate (lambda) and the short-term tag-induced mortality and tag-shedding rate (phi) are known a priori. We present a new multi-year tagging model that permits estimation of instantaneous mortality rates independently of philambda provided tagged animals from two adjacent size groups are released simultaneously. If the two size groups comprise animals just above and below the minimum harvestable size limit, then it is possible to estimate year-specific instantaneous fishing and natural mortality rates after 2 yr of tagging and tag-recovery. In addition to the standard assumptions of multi-year tag-recovery models, it is necessary to assume that recruited animals have equal selectivity, pre-recruited animals become fully recruited in 1 or 2 yr, and the size groups experience the same natural mortality rate. Applicability of the model to the Tasmania southern rock lobster (Jasus edwardsii) fishery is evaluated using a simulation model and parameters based on data from the lobster fishery; assumptions are likely to be met and precision should be adequate if at least 1000 animals are tagged per year in each size group. (C) 2003 International Council for the Exploration of the Sea

Rising Temperatures, Molting Phenology, and Epizootic Shell Disease in the American Lobster

Phenological mismatchmaladaptive changes in phenology resulting from altered timing of environmental cuesis an increasing concern in many ecological systems, yet its effects on disease are poorly characterized. American lobster (Homarus americanus) is declining at its southern geographic limit. Rising seawater temperatures are associated with seasonal outbreaks of epizootic shell disease (ESD), which peaks in prevalence in the fall. We used a 34-year mark-recapture data set to investigate relationships between temperature, molting phenology, and ESD in Long Island Sound, where temperatures are increasing at 0.4 degrees C per decade. Our analyses support the hypothesis that phenological mismatch is linked to the epidemiology of ESD. Warming spring temperatures are correlated with earlier spring molting. Lobsters lose diseased cuticle by molting, and early molting increases the intermolt period in the summer, when disease prevalence is increasing to a fall peak. In juvenile and adult male lobsters, September ESD prevalence was correlated with early molting, while October ESD prevalence was correlated with summer seawater temperature. This suggests that temperature-induced molting phenology affects the timing of the onset of ESD, but later in the summer this signal is swamped by the stronger signal of summer temperatures, which we hypothesize are associated with an increased rate of new infections. October ESD prevalence was approximate to 80% in years with hot summers and approximate to 30% in years with cooler summers. Yearly survival of diseased lobsters i

The logic of comparative life history studies for estimating key parameters, with a focus on natural mortality rate

There are a number of key parameters in population dynamics that are difficult to estimate, such as natural mortality rate, intrinsic rate of population growth, and stock-recruitment relationships. Often, these parameters of a stock are, or can be, estimated indirectly on the basis of comparative life history studies. That is, the relationship between a difficult to estimate parameter and life history correlates is examined over a wide variety of species in order to develop predictive equations. The form of these equations may be derived from life history theory or simply be suggested by exploratory data analysis. Similarly, population characteristics such as potential yield can be estimated by making use of a relationship between the population parameter and bio-chemico-physical characteristics of the ecosystem. Surprisingly, little work has been done to evaluate how well these indirect estimators work and, in fact, there is little guidance on how to conduct comparative life history studies and how to evaluate them. We consider five issues arising in such studies: (i) the parameters of interest may be ill-defined idealizations of the real world, (ii) true values of the parameters are not known for any species, (iii) selecting data based on the quality of the estimates can introduce a host of problems, (iv) the estimates that are available for comparison constitute a non-random sample of species from an ill-defined population of species of interest, and (v) the hierarchical nature of the data (e.g. stocks within species within genera within families, etc., with multiple observations at each level) warrants consideration. We discuss how these issues can be handled and how they shape the kinds of questions that can be asked of a database of life history studies

Properties of age compositions and mortality estimates derived from cohort slicing of length data

Cohort slicing can be used to obtain catch-at-age data from length frequency distributions when directly measured age data are unavailable. The procedure systematically underestimates the relative abundance of the youngest age groups and overestimates abundance at older ages. Cohort-sliced catch-at-age data can be used to estimate total mortality rate (Z) using a regression estimator or the Chapman-Robson estimator for right truncated data. However, the effect of cohort slicing on accuracy and precision of resulting Z estimates remains to be determined. We used Monte Carlo simulation to estimate the per cent bias and per cent root mean square error of the unweighted regression, weighted regression, and Chapman-Robson mortality estimators applied to cohort-sliced data. Incompletely recruited age groups were truncated from the cohort-sliced catch-at-age data using previously established recommendations and a variety of plus groups was used to combine older age groups. The sensitivity of the results to a range of plausible biological combinations of Z, growth parameters, recruitment variability, and length-at-age error was tested. Our simulation shows that cohort slicing can work well in some cases and poorly in others. Overall, plus group selection was more important in high K scenarios than it was in low K scenarios. Surprisingly, defining the plus group to start at a high age worked well in some cases, although length and age are poorly correlated for old ages. No one estimator was uniformly superior; we therefore provide recommendations concerning the appropriate estimator and plus group to use, depending on the parameters characterizing the stock. We further recommend that simulations be performed to determine exactly which plus group would be most appropriate given the scenario at hand

A novel approach to quantify random error explicitly in epidemiological studies

The most frequently used methods for handling random error are largely misunderstood or misused by researchers. We propose a simple approach to quantify the amount of random error which does not require solid background in statistics for its proper interpretation. This method may help researchers refrain from oversimplistic interpretations relying on statistical significance

Management Impacts on Forest Floor and Soil Organic Carbon in Northern Temperate Forests of the US

<p>Abstract</p> <p>Background</p> <p>The role of forests in the global carbon cycle has been the subject of a great deal of research recently, but the impact of management practices on forest soil dynamics at the stand level has received less attention. This study used six forest management experimental sites in five northern states of the US to investigate the effects of silvicultural treatments (light thinning, heavy thinning, and clearcutting) on forest floor and soil carbon pools.</p> <p>Results</p> <p>No overall trend was found between forest floor carbon stocks in stands subjected to partial or complete harvest treatments. A few sites had larger stocks in control plots, although estimates were often highly variable. Forest floor carbon pools did show a trend of increasing values from southern to northern sites. Surface soil (0-5 cm) organic carbon content and concentration were similar between treated and untreated plots. Overall soil carbon (0-20 cm) pool size was not significantly different from control values in sites treated with partial or complete harvests. No geographic trends were evident for any of the soil properties examined.</p> <p>Conclusions</p> <p>Results indicate that it is unlikely that mineral soil carbon stocks are adversely affected by typical management practices as applied in northern hardwood forests in the US; however, the findings suggest that the forest floor carbon pool may be susceptible to loss.</p

Providing education on evidence-based practice improved knowledge but did not change behaviour: a before and after study

BACKGROUND: Many health professionals lack the skills to find and appraise published research. This lack of skills and associated knowledge needs to be addressed, and practice habits need to change, for evidence-based practice to occur. The aim of this before and after study was to evaluate the effect of a multifaceted intervention on the knowledge, skills, attitudes and behaviour of allied health professionals. METHODS: 114 self-selected occupational therapists were recruited. The intervention included a 2-day workshop combined with outreach support for eight months. Support involved email and telephone contact and a workplace visit. Measures were collected at baseline, post-workshop, and eight months later. The primary outcome was knowledge, measured using the Adapted Fresno Test of Evidence-Based Practice (total score 0 to 156). Secondary outcomes were attitude to evidence-based practice (% reporting improved skills and confidence; % reporting barriers), and behaviour measured using an activity diary (% engaging/not engaging in search and appraisal activities), and assignment completion. RESULTS: Post-workshop, there were significant gains in knowledge which were maintained at follow-up. The mean difference in the Adapted Fresno Test total score was 20.6 points (95% CI, 15.6 to 25.5). The change from post-workshop to follow-up was small and non-significant (mean difference 1.2 points, 95% CI, -6.0 to 8.5). Fewer participants reported lack of searching and appraisal skills as barriers to evidence-based practice over time (searching = 61%, 53%, 24%; appraisal 60%, 65%, 41%). These differences were statistically significant (p = 0.0001 and 0.010 respectively). Behaviour changed little. Pre-workshop, 6% engaged in critical appraisal increasing to 18% post-workshop and 18% at follow-up. Nearly two thirds (60%) were not reading any research literature at follow-up. Twenty-three participants (20.2%) completed their assignment. CONCLUSION: Evidence-based practice skills and knowledge improved markedly with a targetted education intervention and outreach support. However, changes in behaviour were small, based on the frequency of searching and appraisal activities. Allied health educators should focus more on post-workshop skill development, particularly appraisal, and help learners to establish new routines and priorities around evidence-based practice. Learners also need to know that behaviour change of this nature may take months, even years