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Globally, many freshwater species are depleting and require population-level assessments. Many species distribution modelling frameworks are available for such assessments, but comparisons are needed to understand their predictive performance under different settings. K-fold cross-validation techniques were employed to compare the performance of three commonly used frameworks: machine learning, spatiotemporal modelling, and Gaussian process (GP) modelling. Through application to New Zealand populations of longfin eel (Anguilla dieffenbachii) and shortfin eel (Anguilla australis), area under the receiver operating characteristic curve (AUC) and true skill statistic (TSS) model performance metrics were estimated. All modelling frameworks produced approximately consistent distribution maps but differed in predictive performance. AUC and TSS results indicated that model predictions from the spatiotemporal modelling framework were the most accurate, followed by GP modelling. However, all modelling frameworks performed similarly when training and test data were spatially independent. In addition to having the best predictive performance, the spatiotemporal modelling framework showed the greatest promise for advancement in population-level assessment and is therefore recommended. The results are useful for freshwater ecologists and resource managers to make informed decisions on the appropriateness of a modelling framework for their research objective. Key words: spatiotemporal model, regularized random forests, Gaussian random field model, species distribution modelling, model comparison, freshwater environments, 1. Introduction Species distribution models (SDMs) are often used for species conservation and management. There is a diverse range of SDMs available for organisms living in terrestrial (e.g., Buckley et [...]

Sharks (class Chondrichthyes, subclass Elasmobranchii) typically have a long lifespan, slow growth rate and low fecundity, leading to low productivity and hence relatively high vulnerability to fishing. Information for managing fisheries catches of elasmobranchs is often lacking, because elasmobranchs are usually non‐target species, often with low abundance. It is more feasible to develop management reference points for elasmobranchs based on their life‐history information than through traditional stock assessments. The natural mortality rate (M) is the leading life‐history parameter (LHP) required by many methods for developing reference points and is itself often derived indirectly from other LHPs. In this paper, we evaluate nine M estimators, using 15 shark stocks in the Western and Central Pacific Ocean as examples. We then compare four methods for developing fishing mortality reference points Fmsy, including empirical modelling, the Euler‐Lotka equation, an M‐productivity‐based method and the spawning potential rate converted method. Our analyses show that popular M estimators developed mainly from teleost data resulted in large deviations from the average and were not suitable for elasmobranchs. All four methods for estimating Fmsy performed similarly. However, the empirical method is very simple and cost‐effective and tended to produce smaller deviations from the average than the other three methods. Nonetheless, it is recommended that multiple methods should be used to minimize possible bias and reflect uncertainty, if the required LHPs are available. [ABSTRACT FROM AUTHOR]

The American eel (Anguilla rostrata) occupies a vast range in the West Atlantic Ocean and inflowing waters. Despite its presumed panmictic status, management of this species is geographically fragmented. There have been widespread calls for internationally coordinated efforts towards a range-wide stock assessment, but such an objective faces obstacles of a high degree of heterogeneity in major life history characteristics and the near-absence of such data in the northern, western, and southern parts of the species' range. This paper reviews novel and underutilized methods and data sources that may aid progress to an eventual range-wide assessment. Methods for obtaining information on distribution and abundance include mining of extant data and field surveys by glass bottom boat, electrofishing boat, net enclosures, environmental DNA (eDNA) and ocean larval tows. Analytic resources and tools include environmental databases, fetch as a covariate of abundance, accounting for the net effects of small ponds, GIS-oriented habitat modeling, glass eel-oriented population modeling, estimation of age structure from length structure, use of life history parameter clines to fill gaps in assessment input values, and life cycle modeling. eDNA is a cost-effective technique that has the potential to clarify American eel distribution, and possibly relative abundance, over the very large areas where data for this species are sparse. Most other techniques reviewed in this paper have substantial costs, which may constrain their use in the southern part of the American eel range where resources of aquatic science are often limited. [ABSTRACT FROM AUTHOR]

The western and central Pacific Ocean supports the world's largest tuna fisheries. Since the 1990s, the purse‐seine fishery has increasingly fished in association with fish aggregating devices (FADs), which has increased catches of juvenile bigeye and yellowfin tunas and vulnerable bycatch species (e.g., sharks). This has raised concerns regarding the sustainability of these species' populations and the supporting ecosystem, but may provide improved food security of Pacific Island nations through utilisation of FAD‐associated byproduct species (e.g., wahoo). An ecosystem model of the western Pacific Warm Pool Province was used to explore the potential ecological impacts of varying FAD fishing effort (±50% or 100%) over 30 years. The ecosystem has undergone a significant change in structure since 1980 from heavy exploitation of top predators (e.g., tunas) and "fishing up the food web" of high‐trophic‐level non‐target species. The ecosystem appeared resistant to simulated fishing perturbations, with only modest changes (<10%) in the biomass of most groups, although some less productive shark bycatch species decreased by up to 43%, which had a subsequent positive effect on several byproduct species, the prey of sharks. Reduction of FAD effort by at least 50% was predicted to increase the biomass of tuna species and sharks and return the ecosystem structure to a pre‐industrial‐fishing state within 10 years. Spatial disaggregation of the model and integration of economic information are recommended to better capture ecological and economic changes that may result from fishing and/or climate impacts and to develop appropriate management measures in response. [ABSTRACT FROM AUTHOR]