Abstract

[Keynote] Simulating how changing gear selectivity changes the future

Mollie E. Brooks (Technological University of Denmark)

Management strategy evaluation (MSE) is a framework to evaluate the possible outcomes of future scenarios in a fishery. Here I present two studies that used MSE.

For a short-lived small pelagic species, we used MSE to simulate the potential yield and sustainability of different harvest control rules. We showed that an escapement strategy gives a higher sustainable yield than a fixed F strategy because it takes advantage of rare years of high recruitment. 

To improve sustainability and yield, fishing gear and fleet behavior can be modified to select for what species and sizes of fish are captured. Experiments are conducted to evaluate gear modifications and the data can be analyzed using the selfisher pacakge in R. We did multi-species catch-comparison analyses to quantify the effects of an escape panel designed to reduce cod catches. We put the results into a MSE framework to simulate what would happen if fleets used the cod reduction gear. We found that, with status-quo fishing, cod biomass in the North Sea projected in the year 2030 could increase from 125,000 to nearly 500,000 if the gear were adopted by all fleets. 

Site occupancy model for environmental DNA metabarcording

Keiichi Fukaya (NIES)

Environmental DNA (eDNA) metabarcoding is an emerging technology for gauging the distribution and diversity of species. Species detection by eDNA metabarcoding is sensitive but still imperfect, owing to various factors that can cause false negatives in the inherent multistage workflow and thus complicate the identification of the ideal allocation of resources among the different stages in optimizing research efficiency. In this talk, I introduce a variant of multispecies site occupancy model that accounts for the multistage workflow and data format specific to eDNA metabarcoding. With some particular applications, I illustrate how this model can estimate the detectability of species at different stages of the workflow and help identify the study design that optimizes the effectiveness of species detection. I also present the R package occumb, which was developed to perform Bayesian statistical inference on this model in a convenient manner.

Red fish, pink fish, white fish: modeling stationary and non-stationary process simultaneously in fish population dynamics

Hiroshi Okamura, Toshihiro Yamamoto, and Masanori Takahashi (FRA)

The sandeel in Japan have recently declined to a very low level. We predict the future population status for sandeel using the state-space model. Since sandeel is showing a decline continuously even without fishing, we need to model a negative impact on the population caused by the environmental changes through the survey time series data. While the deterministic component of population dynamics model is a stationary density-dependent process, the external effect from any environmental factor is a non-stationary random-walk process. The fact that a non-stationary random-walk process is very flexible nonparametric smoothing makes coexistence of both stationary and non-stationary process in a model a difficult modelling issue. We propose a method of regularization to simultaneously deal with a stationary and non-stationary stochastic process in the model. We then illustrate the difference in future projection and population diagnostics of sandeel by with or without the external effect using the developed model optimized by Template Model Builder. 

Contributions of alpha and beta diversity to ecosystem stability under climate change in the deep-sea ecosystem of the western North Pacific

Yuki Kanamori (FRA) 

Distributional shifts under climate change are a well-known biotic change around the world recently, while their effects on ecosystem functions, such as ecosystem variability, through changes in community structure, such as biodiversity, are still unresolved. In this study, I investigated the temporal changes in alpha and beta diversity, contributions of individual species with distributional shifts to those changes in alpha and beta diversity, and the changes in the relationship between biodiversity and ecosystem variability using 25 years' data including ca. 700 species. I found that ecosystem variability was largely influenced to be stable by beta diversity, especially in the current (2012 to 2021) because beta diversity increased owing to the invasion of species that mainly distribute in tropic and subtropic areas. Despite the effect of beta diversity, ecosystem variability increased in the current. This increase in ecosystem variability was caused by the spatially synchronized temporal variation in sea bottom temperature, which resulted in synchronized temporal variation in species abundance among local communities, and by the increased impact of alpha variability on ecosystem variability. These results indicate that the impact of alpha diversity on ecosystem variability is more changeable than the impact of beta diversity on ecosystem variability under climate change, suggesting that it is important to focus on changes in alpha diversity due to local colonization from other habitats and local extinction from existing habitats.

Scalable phylogenetic Gaussian process models improve the detectability of environmental signals on extinction risks for many Red List species

Misako Matsuba (NIES) 

Conservation biologists have a daunting task of understanding the causes of species decline associated with anthropogenic factors and predicting the extinction risk of a growing number of endangered species. By stabilizing estimates with information on closely related species, phylogenetic information among species can bridge gaps in information on species with small sample sizes when modelling large numbers of endangered species. However, modelling many species with the Gaussian process (GP) remains a challenge owing to the computational burden in estimating the large variance–covariance matrix.

Here, we applied a phylogenetic generalized mixed model to 1,010 endangered vascular plant taxa in Japan following phylogenetic GPs implemented by nearest neighbor GP (NNGP) approximation. NNGP enables flexibility in changing the proximity on the phylogenetic tree of species from which information is borrowed to stabilize parameter estimates with a realistic computational burden. We found that the model with phylogenetic information had better prediction performance than the model without phylogenetic information. The results showed that across all explanatory variables, the phylogenetic model could detect interspecific differences in response to environmental factors in all species more clearly. In future analyses, we will apply the models developed in this study to predict extinction probabilities under future climate-land use scenarios and proceed with a comprehensive assessment of biodiversity degradation and threats to endangered species.

Overlooked individual-level density dependence in fisheries management: incorporating somatic growth dynamics in a state-space model

Shota Nishijima (FRA) 

Density dependence (DD), or an inverse relationship between per capita population growth rate and population density due to intraspecific competition, is one of the most fundamental components of population dynamics. The strength of DD in fisheries resources is closely related to sustainable stock size and exploitation rate, and thus affects a reference point for maximum sustainable yield (MSY). While fisheries stock assessments typically assume a density-dependent survival rate from eggs to recruits in early life stages, density-dependent processes in late life stages, such as reduced growth rates of body size and maturation as abundance increases, are rarely explicitly considered for management advice. In this presentation, I illustrate how individual-level DD in somatic growth of body weight and maturation changes population-level DD, and an MSY reference point using the Pacific stock of chub mackerel (Scomber japonicus). I incorporated the growth dynamics of mean body weight and maturation rate into a state-space age-structured model (SAM) and found that including physical DD improved model performance. A model with physical DD estimated much stronger density dependence in the per capita population growth rate than a model without physical DD, leading to lower spawning biomass and higher exploitation rate that produce MSY. Thus, the inclusion of this DD changed the recent status of exploitation rate from overfishing to underfishing. This highlights the importance of post-recruitment DD in fisheries assessment and management.