Simulation

R code of the simulation stud

Data from: How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems

Aim: Biodiversity prediction becomes increasingly important in the face of global diversity loss, whereas substantial challenges still exist in both conceptual and technical aspects. There exist many predictive models, and an integrative evaluation can help understand their performance in handling the multifacets of biodiversity. This study aims to evaluate the performance of these modelling approaches to predict both α‐ and β‐diversity in diverse ecological contexts. Location: North Yellow Sea, China. Methods: The biodiversity models follow three strategies, “assemble first, predict later”, “predict first, assemble later” and “assemble and predict together”. Hill diversity profile, Fisher's log‐series parameter and the distance decay of similarity are used to measure α‐ and β‐diversity. The evaluation study is conducted based on seasonal bottom trawl surveys from October 2016 to August 2017 in North Yellow Sea, China, allocated to coastal and offshore areas. We evaluate the predictive power of the models using cross‐validation. Results: Following the “assemble first, predict later” approach, macroecological model (MEM) provided the most accurate prediction overall, whereas stacked species distribution model (SSDM) and joint species distribution model (JSDM), following the second and third modelling approaches, tended to overestimate α‐diversity and underestimate β‐diversity. The performances of SSDM and JSDM could be improved by moderately down‐weighting rare species. The relative performances of the three modelling approaches were consistent among seasons and spatial regions. Main conclusions: The superior performances of MEM in a range of temporal and spatial contexts favour the “assemble first, predict later” approach and imply a tight community assembly in the studied area. The overall predictive powers of varying models suggest that the spatial pattern of marine biodiversity could be fairly well predicted with commonly accessible hydrologic data in a mesoscales. The approach of multi‐model evaluations is applicable to a variety of ecosystems for biodiversity prediction

Age determination for whitespotted conger Conger myriaster through somatic and otolith morphometrics.

It is difficult to determine ages of eels via otoliths, because multiple alternating translucent and opaque zones in the otoliths are hard to identify. In this study, we developed an efficient age determination method for whitespotted conger (Conger myriaster), using random forest models with otolith weight and length, total body length, capture location and season as predictors. 409 specimens were collected from six locations in Yellow and East China Sea between October 2016 and December 2017. Overall OOB error rate was 17.36% compared with 16.26% for the external cross-validation dataset, and the error of age was within one year. Otolith weight and total length were the most important predictors, followed by otolith length, capture locations and seasons. There were no significant differences between the results derived from otolith/somatic morphometrics and otoliths annuli in the estimation of age composition and von Betalanffy Growth Functions growth curve. Our results demonstrated that random forest model with otolith and somatic morphometrics is an efficient and reliable approach for age determination of C. myriaster, which may also be applied to other eel species

Sampling effects on the effectiveness of ecological indicators in detecting fishery-induced community changes

Effective ecological indicators (EI) should reflect changes to ecosystem status in a timely manner to guide fishery management; however, the robustness of EIs in the face of sampling uncertainty is not well understood and sampling errors may result in delayed or even unhelpful actions for management. In this study, we use a size-spectrum model to evaluate the effectiveness of EIs in detecting fishery-induced ecosystem changes given various levels of sampling uncertainty. We demonstrate that there is a time-lag exists between changes in fishing pressure and EIs response. The selectivity of survey gears can strongly determine the level of EI responses within certain size ranges. EIs may lost statistical power once sampling errors exceed a certain level, implying that several decades of monitoring data may be needed to be sure of detecting even a large change. Multivariate methods can strengthen the statistical powers of EIs, but only when the level of sampling noises is low. This study suggests the need for considering the impact of sampling uncertainty on the use of ecological indicators in fisheries management.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Observation data for modelling

The file is in a form of R data, including all data used to fit the JSDMs, that is ,the biomass of multispecies and environmental variables of water temperature, salinity and depth

JSDM models

The file can be read in R, including the full structure of the fitted models, BaysComm, Boral, Mistnet, HMSC, and Gjam

Evaluating fisheries conservation strategies in the socio-ecological system: A grid-based dynamic model to link spatial conservation prioritization tools with tactical fisheries management.

Spatial conservation prioritization concerns trade-offs between marine conservation and resource exploitation. This approach has been increasingly used to devise spatial management strategies for fisheries because of its simplicity in the optimization model and less data requirement compared to complex dynamic models. However, most of the prioritization is based on static models or algorithms; whose solutions need to be evaluated in a dynamic approach, considering the high uncertainty and opportunity costs associated with their implementation. We developed a framework that integrates species distribution models, spatial conservation prioritization tools and a general grid-based dynamic model (Grid-DM) to support evaluation of ecological and economic trade-offs of candidate conservation plans. The Grid-DM is spatially explicit and has a tactical management focus on single species. We applied the Grid-DM to small yellow croaker (Larimichthys polyactis) in Haizhou Bay, China and validated its spatial and temporal performances against historical observations. It was linked to a spatial conservation prioritization tool Marxan to illustrate how the model can be used for conservation strategy evaluation. The simulation model demonstrated effectiveness in capturing the spatio-temporal dynamics of the target fishery as well as the socio-ecological effects of conservation measures. We conclude that the model has the capability and flexibility to address various forms of uncertainties, simulate the dynamics of a targeted fishery, and to evaluate biological and socioeconomic impacts of management plans. The modelling platform can further inform scientists and policy makers of management alternatives screening and adaptive conservation planning

Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay, China

<p>The predictive skill of species distribution models depends on the quality and quantity of input information. In addition to the physical environmental variables, prey availability is also one of the main drivers regulating spatial distribution of marine species. However, prey distribution data have rarely been considered in habitat models due to the lack of information on non-commercial prey species. This may lead to an incomplete view of species distributions and biased model predictions. In this study, we developed a new framework of two-phase generalized additive models (GAMs) based on the Tweedie distribution to incorporate the predicted prey abundance as covariates in habitat models, and applied this framework to juvenile slender lizardfish <i>Saurida elongata</i> in Haizhou Bay, China. This study demonstrated that the predictive skill of habitat models could be greatly improved through incorporating prey abundance as explanatory variables. The importance of prey distribution data in the habitat model confirms the essentiality of including prey data while modelling species distribution. Spatial overlap and GAM analysis demonstrated that not all dominant prey can be selected as potential explanatory variables and only those prey species showing high spatiotemporal occurrences with predators should be incorporated. The framework derived in this study could be extended to other marine organisms to improve the predictive skill of habitat models and enhance our understanding of the ecological mechanisms underlying the distribution of marine species.</p