Trait groups as management entities in a complex,multispecies reef fishery

Localized stressors compound the ongoing climate-driven decline of coral reefs, requiring natural resource managers to work with rapidly shifting paradigms. Trait-based adaptive management (TBAM) is a new framework to help address changing conditions by choosing and implementing management actions specific to species groups that share key traits, vulnerabilities, and management responses. In TBAM maintenance of functioning ecosystems is balanced with provisioning for human subsistence and livelihoods. We first identified trait-based groups of food fish in a Pacific coral reef with hierarchical clustering. Positing that trait-based groups performing comparable functions respond similarly to both stressors and management actions, we ascertained biophysical and socioeconomic drivers of trait-group biomass and evaluated their vulnerabilities with generalized additive models. Clustering identified 7 trait groups from 131 species. Groups responded to different drivers and displayed divergent vulnerabilities; human activities emerged as important predictors of community structuring. Biomass of small, solitary reef-associated species increased with distance from key fishing ports, and large, solitary piscivores exhibited a decline in biomass with distance from a port. Group biomass also varied in response to different habitat types, the presence or absence of reported dynamite fishing activity, and exposure to wave energy. The differential vulnerabilities of trait groups revealed how the community structure of food fishes is driven by different aspects of resource use and habitat. This inherent variability in the responses of trait-based groups presents opportunities to apply selective TBAM strategies for complex, multispecies fisheries. This approach can be widely adjusted to suit local contexts and priorities.     

Meter-scale variation within a single transect demands attention to taxon accumulation curves in riverine microbiome studies

● Riverine microbiomes exhibited hyperlocal variation within a single transect. ● Certain family-level taxa directionally associated with river center and bank. ● Taxon accumulation curves within a transect urges more nuanced sampling design. Microbial communities inhabiting river ecosystems play crucial roles in global biogeochemical cycling and pollution attenuation. Spatial variations in local microbial assemblages are important for detailed understanding of community assembly and developing robust biodiversity sampling strategies. Here, we intensely analyzed twenty water samples collected from a one-meter spaced transect from the near-shore to the near-center in the Meramec River in eastern Missouri, USA and examined the microbial community composition with 16S rRNA gene amplicon sequencing. Riverine microbiomes across the transect exhibited extremely high similarity, with Pearson’s correlation coefficients above 0.9 for all pairwise community composition comparisons. However, despite the high similarity, PERMANOVA revealed significant spatial differences between near-shore and near-center communities (p = 0.001). Sloan’s neutral model simulations revealed that within-transect community composition variation was largely explained by demographic stochasticity (R² = 0.89). Despite being primarily explained by neutral processes, LefSe analyses also revealed taxa from ten families of which relative abundances differed directionally from the bank to the river center, indicating an additional role of environmental filtering. Notably, the local variations within a river transect can have profound impacts on the documentation of alpha diversity. Taxon-accumulation curves indicated that even twenty samples did not fully saturate the sampling effort at the genus level, yet four, six and seven samples were able to capture 80% of the phylum-level, family-level, and genus-level diversity, respectively. This study for the first time reveals hyperlocal variations in riverine microbiomes and their assembly mechanisms, demanding attention to more robust sampling strategies for documenting microbial diversity in riverine systems.     

IAFSA-GRNN在油田集输管道CO2腐蚀速率预测中的应用

为提高油田集输管道CO₂腐蚀速率预测的准确性,针对原始广义回归神经网络(GRNN)预测精度低的问题,提出改进的群智能算法优化原始GRNN的预测模型;分别使用GRNN模型、人工鱼群算法(AFSA)优化的GRNN(AFSA-GRNN)模型和自适应改进的AFSA-GRNN(IAFSA-GRNN)模型预测X65管线钢的CO₂腐蚀速率。结果表明:采用AFSA和IAFSA优化光滑因子S后,能大大提高GRNN模型的预测精度,预测结果的平均相对误差由36.09%分别减小至7.20%和6.90%;与AFSA相比,IAFSA优化的GRNN不仅具有更高的预测精度,还具有更快的收敛速度。AFSA-GRNN在第164次迭代计算时收敛,而IAFSA-GRNN在第109次迭代计算时收敛,说明AFSA经自适应优化能提高优化过程的收敛速度和GRNN的预测精度。     

Longitudinal monitoring of neutral and adaptive genomic diversity in a reintroduction

Restoration programs in the form of ex-situ breeding combined with reintroductions are becoming critical to counteract demographic declines and species losses. Such programs are increasingly using genetic management to improve conservation outcomes. However, the lack of long-term monitoring of genetic indicators following reintroduction prevents assessments of the trajectory and persistence of reintroduced populations. We carried out an extensive monitoring program in the wild for a threatened small-bodied fish (southern pygmy perch, Nannoperca australis) to assess the long-term genomic effects of its captive breeding and reintroduction. The species was rescued prior to its extirpation from the terminal lakes of Australia's Murray-Darling Basin, and then used for genetically informed captive breeding and reintroductions. Subsequent annual or biannual monitoring of abundance, fitness, and occupancy over a period of 11 years, combined with postreintroduction genetic sampling, revealed survival and recruitment of reintroduced fish. Genomic analyses based on data from the original wild rescued, captive born, and reintroduced cohorts revealed low inbreeding and strong maintenance of neutral and candidate adaptive genomic diversity across multiple generations. An increasing trend in the effective population size of the reintroduced population was consistent with field monitoring data in demonstrating successful re-establishment of the species. This provides a rare empirical example that the adaptive potential of a locally extinct population can be maintained during genetically informed ex-situ conservation breeding and reintroduction into the wild. Strategies to improve biodiversity restoration via ex-situ conservation should include genetic-based captive breeding and longitudinal monitoring of standing genomic variation in reintroduced populations.