Perfect simulation from unbiased simulation

We show that any application of the technique of unbiased simulation becomes perfect simulation when coalescence of the two coupled Markov chains can be practically assured in advance. This happens when a fixed number of iterations is high enough that the probability of needing any more to achieve coalescence is negligible; we suggest a value of $10^{-20}$. This finding enormously increases the range of problems for which perfect simulation, which exactly follows the target distribution, can be implemented. We design a new algorithm to make practical use of the high number of iterations by producing extra perfect sample points with little extra computational effort, at a cost of a small, controllable amount of serial correlation within sample sets of about 20 points. Different sample sets remain completely independent. The algorithm includes maximal coupling for continuous processes, to bring together chains that are already close. We illustrate the methodology on a simple, two-state Markov chain and on standard normal distributions up to 20 dimensions. Our technical formulation involves a nonzero probability, which can be made arbitrarily small, that a single perfect sample point may have its place taken by a "string" of many points which are assigned weights, each equal to $\pm 1$, that sum to~$1$. A point with a weight of $-1$ is a "hole", which is an object that can be cancelled by an equivalent point that has the same value but opposite weight $+1$.Comment: 17 pages, 4 figures; for associated R scripts, see https://github.com/George-Leigh/PerfectSimulatio

Stock assessment of Queensland east coast prickly redfish, herrmanni curryfish and vastus curryfish with data to December 2023

Prickly redfish, herrmanni curryfish and vastus curryfish are species of sea cucumber from the family Stichopodidae. All three species have a broad Indo-Pacific distribution and are found in multiple countries with coral reef ecosystems. All three species occur across the GBR but at varying depth ranges: 10-30 m for prickly redfish, 0-25 m for herrmanni curryfish and 0-8 m for vastus curryfish. This is the first stock assessment conducted on Queensland east coast prickly redfish, herrmanni curryfish and vastus curryfish by Fisheries Queensland. All assessment inputs and outputs are referenced on a calendar year basis (that is, ‘2023’ means January 2023–December 2023). This assessment used a one-sex age-structured population model and a delay-difference model which led to similar results. The outputs of the age-structured model are presented as the main results for all three species in this assessment. The assessment incorporated commercial catch and effort data spanning 1995 to 2023 as well as length composition data and estimates of absolute abundance from recent surveys undertaken in 2023. No recreational or Indigenous catch data were available and catches from these sectors are considered negligible. There are no discards due to the highly selective nature of the fishery. Several Stock Synthesis scenarios were run to examine the implications of different fixed model parameters such as steepness (h) and natural mortality (M) on model outcomes. All scenarios were optimised using Markov chain Monte Carlo (MCMC) to better explore the robustness of the models. From these exploratory scenarios a final base case was chosen for each species. The base case Stock Synthesis results indicated that the biomass ratio of prickly redfish at the beginning of 2024 was between 73% and 116% of unfished levels. The base case Stock Synthesis results indicated that the biomass ratio of herrmanni curryfish at the beginning of 2024 was between 81% and 141% of unfished levels. The base case Stock Synthesis results indicated that the biomass ratio of vastus curryfish at the beginning of 2024 was between 65% and 124% of unfished levels

Stock assessment of Moreton Bay bugs (Thenus australiensis and Thenus parindicus) in Queensland, Australia with data to December 2021

Moreton Bay bugs are distributed throughout tropical and subtropical coastal waters of Australia from northern New South Wales to Shark Bay in Western Australia. The Moreton Bay bug population on the east coast of Queensland is comprised of two species—Thenus australiensis, also known as the sand or reef bug, and Thenus parindicus, also known as the mud bug. Sand bug females reach 50% maturity at 82 mm carapace width (CW) or 59 mm carapace length (CL). Mud bug females reach 50% maturity at 75 mm carapace width or 53 mm carapace length. Both species spawn year-round with spawning peaks during the period between spring and mid-summer. This is the first stock assessment conducted on Queensland Moreton Bay bugs. This stock assessment includes input data through to December 2021. All assessment inputs and outputs were referenced on a calendar year basis (that is, ‘2021’ means January 2021–December 2021). For all stocks analysed, the assessment used a one-sex monthly delay-difference population model, fitted to catch rates. Age-structured models were also trialed, however these did not lead to outcomes that were considered plausible by the project team. For sand bugs, the data from 1968 to 2021 comprised of commercial catch and effort (1988—2021), historical commercial catch (1968–1981, 1974–1987), fishery independent survey data (2017-2021) and licence numbers (1968–2003). For mud bugs, the data from 1948 to 2021 comprised of commercial catch and effort (1988-–2021), historical commercial catch (1948–1981, 1974–1987) and licence numbers (1968–2003). The model split the fishery into two fleets to account for the rezoning of the Great Barrier Reef (GBR) in 2004—one for the commercial sector pre-July 2004, and one for commercial sector post-July 2004. The stock assessment was guided by a project team consisting of scientists, managers, and industry representatives. Thirteen model scenarios were run for the sand bug stock, covering a range of modelling assumptions and sensitivity tests. All scenarios were optimised using Markov chain Monte Carlo (MCMC) to better explore the robustness of the models. Project team preferred scenario results suggested that the sand bug stock experienced a decline in the period 1968 to 2000 to reach 67% of unfished biomass. The biomass has been generally increasing since, and in 2021 the stock level was estimated to be 78% (63—94% range across the 95% credible interval) of the unfished biomass. Thirteen model scenarios were run for the mud bug stock, covering a range of modelling assumptions and sensitivity tests. Seven scenarios had convergence problems, or diagnostics that indicated issues. The non-target nature of the fishery combined with fishery-dependent catch rates being the primary data set for model tuning makes assessment difficult. The status of the mud bug stock is undefined. The general trajectory across the thirteen scenarios shows the biomass experienced a decline from the period of 1968 until the mid 1980s, then slowly recovered since that time

Cost–benefit analysis of ecosystem modeling to support fisheries management

Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits

Cost–benefit analysis of ecosystem modeling to support fisheries management

Abstract Mathematical and statistical models underlie many of the world's most important fisheries management decisions. Since the 19th century, difficulty calibrating and fitting such models has been used to justify the selection of simple, stationary, single-species models to aid tactical fisheries management decisions. Whereas these justifications are reasonable, it is imperative that we quantify the value of different levels of model complexity for supporting fisheries management, especially given a changing climate, where old methodologies may no longer perform as well as in the past. Here we argue that cost-benefit analysis is an ideal lens to assess the value of model complexity in fisheries management. While some studies have reported the benefits of model complexity in fisheries, modeling costs are rarely considered. In the absence of cost data in the literature, we report, as a starting point, relative costs of single-species stock assessment and marine ecosystem models from two Australian organizations. We found that costs varied by two orders of magnitude, and that ecosystem model costs increased with model complexity. Using these costs, we walk through a hypothetical example of cost-benefit analysis. The demonstration is intended to catalyze the reporting of modeling costs and benefits

Spatial pattern analysis as a focus of landscape ecology to support evaluation of human impact on landscapes and diversity

The relation between landscape patterns and ecological processes forms a central hypothesis of landscape ecology. Three types of pattern analysis to assess anthropogenic impacts on landscape ecosystems and biodiversity are presented in this chapter. Firstly, the results of an analysis of Acanthaceae data in Central Africa are presented and compared with phytogeographic theories. Phytogeography data reflect the spatial variability of plant diversity, and constitute therefore a major tool in conservation policy development. We investigated if it was possible to proxy the phytogeographic classifications by the spatial distribution of Acanthaceae only. When combined with a classic landscape pattern analysis, this type of study could provide complementary information for the definition of conservation priorities. Secondly, we present an analysis of periodic vegetations in the Sudan. It can be accepted that through an understanding of the underlying mechanisms of the formation of this unique pattern geometry, the knowledge with regard to the functioning and vulnerability of these ecosystems can be deepened. Using high-resolution remote sensing imagery and digital elevation models, the relation between pattern symmetry and slope gradient was explored. In particular, slope gradients that could condition the transition between spotted and tiger bush pattern types were focused. The influence of other sources of anisotropy was also considered. Finally, a complementary approach to the calculation of landscape metrics to analyse landscape pattern is described, using the spatial processes themselves causing landscape transformation. Landscape ecologists agree that there appears to be a limited number of common spatial configurations that can result from land transformation processes. Ten processes of landscape transformation are considered: aggregation, attrition, creation, deformation, dissection, enlargement, fragmentation, perforation, shift, and shrinkage. A decision tree is presented that enables definition of the transformation process involved using patch-based data. This technique can help landscape managers to refine their description of landscape dynamics and will assist them in identifying the drivers of landscape transformation.SCOPUS: ch.binfo:eu-repo/semantics/publishe

Neurogenetics and Pharmacology of Learning, Motivation, and Cognition

Many of the individual differences in cognition, motivation, and learning—and the disruption of these processes in neurological conditions—are influenced by genetic factors. We provide an integrative synthesis across human and animal studies, focusing on a recent spate of evidence implicating a role for genes controlling dopaminergic function in frontostriatal circuitry, including COMT, DARPP-32, DAT1, DRD2, and DRD4. These genetic effects are interpreted within theoretical frameworks developed in the context of the broader cognitive and computational neuroscience literature, constrained by data from pharmacological, neuroimaging, electrophysiological, and patient studies. In this framework, genes modulate the efficacy of particular neural computations, and effects of genetic variation are revealed by assays designed to be maximally sensitive to these computations. We discuss the merits and caveats of this approach and outline a number of novel candidate genes of interest for future study