利用贝叶斯多物种模型评估巴伊亚格兰德高盐度潮汐盆地中鱼类和无脊椎动物的探测概率和分布情况

IF 1.8 3区 农林科学 Q2 FISHERIES
Roy M. Ulibarri, Catherine Eckert, David Hicks, Mike Montagne, Brandon Jones, David R. Stewart
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引用次数: 0

摘要

目标 2000 年,阿塔斯科萨湖国家野生动物保护区获得了巴伊亚格兰德(得克萨斯州)管理单元,该区域 70 年来一直荒芜干旱。一个大型合作项目启动了一个恢复项目,以补充盆地水量并恢复其原有的潮汐水文。2005 年,试点河道的建设成功恢复了整个流域的水量,并制定了最终拓宽河道的计划。我们的研究旨在通过评估生态驱动因素及其对物种多样性的影响来评估河口生境恢复情况。 方法 我们评估了物种丰富度、检测/占用率以及物种与栖息地的关系,并估算了如果使用相对丰度而不是占用率来指导未来的取样工作,那么要达到一定的相对精度水平所需的取样工作量。取样工具包括用于捕获幼体的袋式围网和用于捕获亚成体和成体的刺网。在分析中,我们使用贝叶斯负二项线性混合效应模型来评估物种丰富度与栖息地的关系,并使用分层贝叶斯多物种模型来评估单个物种与栖息地的关系,我们还按渔具和取样年份计算了捕获的鱼类总数和相对标准误差,以得出相对丰度的精确估算值。 结果 在 2018 年至 2021 年期间,共捕获了 29 个物种。盐度显然是大巴伊亚河的一个驱动因素,因为物种丰富度和个体水平反应都与高盐度值呈负相关。我们发现,以相对丰度估算的渔获量具有很大的可变性,这在大多数假定可探测性恒定的调查项目中很典型,而且达到特定相对精度水平所需的套网或围网拖网数量也因物种、季节、年份和渔具类型的不同而有很大差异。采集到最多的物种是在其盐度耐受能力的上极值发现的--这可能是对这一低盐系统的独特适应。 结论 基线数据表明,要想成功拓宽河道,必须明显增加整个流域的适宜栖息地特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Using Bayesian multispecies models to evaluate fish and invertebrate detection probability and distribution in the hypersaline Bahia Grande tidal basin

Using Bayesian multispecies models to evaluate fish and invertebrate detection probability and distribution in the hypersaline Bahia Grande tidal basin

Objective

In 2000, the Laguna Atascosa National Wildlife Refuge acquired the Bahia Grande (Texas) management unit, a space that had lain barren and arid for 70 years. A large cooperative partnership launched a restoration project to replenish the basin and recover its original tidal hydrology. In 2005, the construction of a pilot channel successfully restored water throughout the basin, and plans to eventually widen the channel were developed. Our study aims to evaluate an estuarine habitat restoration by assessing ecological drivers and the impacts on species diversity.

Methods

We evaluated species richness, detection/occupancy rates, and species–habitat relationships, and we estimated the sampling effort required to achieve a given level of relative precision if relative abundance was used instead of occupancy to inform future sampling. Sampling gear included bag seines for juvenile life stages and gill nets for capturing subadult and adult life stages. For analysis, we used a Bayesian negative binomial linear mixed-effects model to evaluate richness–habitat relationships and a hierarchical Bayesian multispecies model to evaluate individual species–habitat relationships, and we calculated the total number of fish captured and relative standard error by gear and sample year to produce a precise estimate of relative abundance.

Result

Overall, 29 species were caught between 2018 and 2021. Salinity emerged as a clear driver in the Bahia Grande, as both species richness and individual-level responses were negatively associated with high salinity values. We found that catch estimated as relative abundance had much variability, as is typical of most survey programs assuming constant detectability, and the number of net sets or seine hauls required to achieve a given level of relative precision varied considerably depending on the species, season, year, and gear type. The most collected species were found in the upper extremes of their salinity tolerances—potentially a unique adaptation to this hypersaline system.

Conclusion

Baseline data suggest that for the channel widening to be successful, there must be a noticeable increase in suitable habitat characteristics throughout the basin.

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来源期刊
Marine and Coastal Fisheries
Marine and Coastal Fisheries FISHERIES-MARINE & FRESHWATER BIOLOGY
CiteScore
3.40
自引率
5.90%
发文量
40
审稿时长
>12 weeks
期刊介绍: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science publishes original and innovative research that synthesizes information on biological organization across spatial and temporal scales to promote ecologically sound fisheries science and management. This open-access, online journal published by the American Fisheries Society provides an international venue for studies of marine, coastal, and estuarine fisheries, with emphasis on species'' performance and responses to perturbations in their environment, and promotes the development of ecosystem-based fisheries science and management.
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