Finding regulation among seemingly unregulated populations: a practical framework for analyzing multivariate population time series for their interactions

Zhou, Can; Fujiwara, Masami; Grant, William E.

doi:10.1007/s10651-015-0334-7

Cited by 8 publications

(4 citation statements)

References 73 publications

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“…The co-integration method circumvents this problem. A more detailed description of the application of this method in population ecology is described in [23]. …”

Section: Methodsmentioning

confidence: 99%

“…Because the power of co-integration analysis is weak [23], we applied it to one shrimp time series and one fish time series when both of them were non-stationary. The analysis was done using the Engle-Granger co-integration test [20].…”

Section: Methodsmentioning

confidence: 99%

“…Although co-integration analysis is a relatively new statistical method in population time series analysis, the method is well established in econometrics [20], and its application to population time series has been described [23]. The approach is conceptually similar to minimum/maximum autocorrelation factor analysis, which seeks a weighted linear combination of time series (factor) that maximizes a lag-one autocorrelation [24].…”

Section: Methodsmentioning

confidence: 99%

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Interaction between Penaeid Shrimp and Fish Populations in the Gulf of Mexico: Importance of Shrimp as Forage Species

et al. 2016

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This study investigated the contribution of shrimp stocks in supporting the production of valuable predator species. Fishery-independent data on white shrimp, brown shrimp, and selected fish species (spotted seatrout, red drum, and southern flounder) were collected from 1986 to 2014 by the Texas Parks and Wildlife Department, and converted to catch-per-unit effort (CPUE). Here, the associations between the CPUEs of fish species as predators and those of shrimp species as prey in each sampled bay and sampling season were analyzed using co-integration analysis and Partial Least Squares Regression (PLSR). Co-integration analysis revealed significant associations between 31 of 70 possible fish/shrimp pairings. The analysis also revealed discernible seasonal and spatial patterns. White shrimp in August and brown shrimp in May were associated with fish CPUEs in bays located along the lower coast of Texas, whereas white shrimp in November was more strongly associated with fish CPUEs in bays located on the upper coast. This suggests the possible influence of latitudinal environmental gradient. The results of the PLSR, on the other hand, were not conclusive. This may reflect the high statistical error rates inherent to the analysis of short non-stationary time series. Co-integration is a robust method when analyzing non-stationary time series, and a majority of time series in this study was non-stationary. Based on our co-integration results, we conclude that the CPUE data show significant associations between shrimp abundance and the three predator fish species in the tested regions.

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“…The co-integration method circumvents this problem. A more detailed description of the application of this method in population ecology is described in [23]. …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Interaction between Penaeid Shrimp and Fish Populations in the Gulf of Mexico: Importance of Shrimp as Forage Species

et al. 2016

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“…The purpose of taking the residuals was to remove the smooth trends (seasonal and annual trends) from the environmental variables. The smooth trends in both response variables and explanatory variables can cause spurious statistical associations (Fujiwara et al, 2016; Pyper & Peterman, 1998; Zhou et al, 2016), especially when they all have potentially similar trends. For example, they are all known to have some seasonality and have monotonic annual trends.…”

Section: Methodsmentioning

confidence: 99%

Life‐history traits and temporal patterns in the incidence of coastal fishes experiencing tropicalization

et al. 2022

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Climate change is causing shifts in the geographic distributions of coastal fish species toward higher latitudes. However, local fish communities found in higher latitudes today are not simply those found in lower latitudes in the past because the sensitivity of species to various environmental conditions is different. Responses of fish to environmental conditions differ depending on their life-history traits such as the maximum population growth rate, age of maturity, and generation time because these traits are constrained by environmental conditions. Here, we investigate associations among temporal patterns in presence-absence (incidence) data, life-history traits of species, and environmental conditions to elucidate the potential mechanisms behind the effects of climate change on coastal fish communities. In this study, fish monitoring data collected semi-monthly between 1982 and 2019 from eight major bays in the northwestern Gulf of Mexico were analyzed with generalized additive models. Smooth patterns over seasons and years in the incidence data of 77 species were estimated, and the data were associated with environmental variables. Further, these temporal patterns were associated with the life-history traits of species. The results show that species that are more common in the summer months (non-winter spawners) tend to exhibit an increasing trend and that species common during the spring (winter spawners) tend to exhibit a decreasing trend, suggesting the importance of summer conditions in driving tropicalization. Salinity had significant effects on species in general, and longer-lived species tended to be affected by temperature. These results suggest seasonal differences in the effects of environmental conditions and variation in sensitivity to environmental conditions among different life-history strategies. These findings represent major steps toward understanding the mechanisms behind the tropicalization of estuarine communities.

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Environmental Stochasticity

Fujiwara

Takada

2017

Encyclopedia of Life Sciences

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Environmental stochasticity refers to unpredictable spatiotemporal fluctuation in environmental conditions. The term is often used in the literature on ecology and evolution. Unpredictability is defined as an inability to predict the future state precisely such that only its distribution can be known. The environment is typically defined as any set of abiotic (e.g. temperature and nutrient availability) and biotic (e.g. predator, competitor and food) conditions that organisms experience. Environmental stochasticity influences how population abundance fluctuates and affects the fate (e.g. persistence or extinction) of populations. In an evolutionary timescale, environmental stochasticity also affects the life history strategy of organisms. Environmental stochasticity is included in population models using univariate difference equations, stochastic matrix population models, stochastic differential equations and partial differential equations. Ecological data are analysed to determine the effect of environmental stochasticity using methods such as spectral analysis, capture–recapture analysis, state‐space analysis, generalised linear models and multivariate statistical analyses. Key Concepts Environmental stochasticity is unpredictable spatiotemporal fluctuations in environmental conditions. Observed population dynamics consist of fluctuation due to environmental stochasticity, but it is often confounded with other factors such as observational errors, deterministic fluctuation and demographic stochasticity. Environmental stochasticity is reflected in the fluctuations in ecological processes and affects their fate (e.g. extinction or persistence of populations). Environmental stochasticity plays an important role in the evolution of life history strategies of organisms by affecting their fitness. Stochastic discrete‐time models, stochastic matrix population models, stochastic differential equation models and partial differential equation models are the four basic population models that include environmental stochasticity. Spectral analysis, state‐space model analysis, capture–recapture analysis, generalised linear models and multivariate statistical analysis are commonly used for separating the effects of environmental stochasticity in data.

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Finding regulation among seemingly unregulated populations: a practical framework for analyzing multivariate population time series for their interactions

Cited by 8 publications

References 73 publications

Interaction between Penaeid Shrimp and Fish Populations in the Gulf of Mexico: Importance of Shrimp as Forage Species

Interaction between Penaeid Shrimp and Fish Populations in the Gulf of Mexico: Importance of Shrimp as Forage Species

Life‐history traits and temporal patterns in the incidence of coastal fishes experiencing tropicalization

Environmental Stochasticity

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