GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research

Venables, W. N.; Dichmont, Cathy

doi:10.1016/j.fishres.2004.08.011

Cited by 355 publications

(247 citation statements)

References 45 publications

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“…Proportion data, such as change metrics or land cover metrics, do not exhibit the same variance for the mean across the entire range of possible values. As the mean value approaches 0% or 100% cover, variance tends to zero, while it is maximum near a mean of 50% [60,61]. Classic tests, such as Student's t-test or ANOVA, assume equal within-group variances and their independence of the group mean, whereas the logit link function naturally models the change in variance across the range of the mean values (i.e., 0% and 100% [58,59]).…”

Section: Comparison Methods For Cumulative Impact Of Oil and Hurricanmentioning

confidence: 99%

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

et al. 2017

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Abstract:Coastal ecosystems are greatly endangered due to anthropogenic development and climate change. Multiple disturbances may erode the ability of a system to recover from stress if there is little time between disturbance events. We evaluated the ability of the saltmarshes in Barataria Bay, Louisiana, USA, to recover from two successive disturbances, the DeepWater Horizon oil spill in 2010 and Hurricane Isaac in 2012. We measured recovery using vegetation indices and land cover change metrics. We found that after the hurricane, land loss along oiled shorelines was 17.8%, while along oil-free shorelines, it was 13.6% within the first 7 m. At a distance of 7-14 m, land loss from oiled regions was 11.6%, but only 6.3% in oil-free regions. We found no differences in vulnerability to land loss between narrow and wide shorelines; however, vegetation in narrow sites was significantly more stressed, potentially leading to future land loss. Treated oiled regions also lost more land due to the hurricane than untreated regions. These results suggest that ecosystem recovery after the two disturbances is compromised, as the observed high rates of land loss may prevent salt marsh from establishing in the same areas where it existed prior to the oil spill.

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Section: Comparison Methods For Cumulative Impact Of Oil and Hurricanmentioning

confidence: 99%

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

et al. 2017

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“…Generalized linear modeling (GLM) was used to explore the contribution and role of environmental variables and spatial metrics in explaining fish presence and abundance at two spatial scales, based on the framework suggested by Austin (2002). GLM was used because it can deal with several families of probability distributions for ecological data (Guisan et al 2002;Venables & Dichmont 2004). At the sample scale, the most probable Figure 2.…”

Section: Discussionmentioning

confidence: 99%

Using a continuous riverscape survey to examine the effects of the spatial structure of functional habitats on fish distribution

Pichon¹,

Talès²,

Gorges³

et al. 2015

Journal of Freshwater Ecology

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To fill the gap between microhabitat and landscape scale habitat models for freshwater fish, it is becoming increasingly common practice to adopt a continuous view of riverscapes, thus allowing a better understanding of the processes in place at the river management level (segments of 1À100 km). The aim of this study was to test the effects of the spatial structure of habitat on fish distribution at this scale. Inferred habitat relationships were generated using spatial metrics adapted from landscape ecology. These were calculated for two species of multi-habitat cyprinid fish in a 25-km long segment of the Seine River. A spatially continuous survey was then designed to acquire fish sampling data relating to the riverscape. Explanatory models were devised to quantify the extent to which environmental and spatial variables could describe fish distribution patterns. Spatially continuous sampling of feeding habitats at dawn and dusk provided greater understanding of the spatial distribution of common barbel (Barbus barbus, L.) and nase (Chondrostoma nasus, L.). Fish observations were aggregated longitudinally in neighboring feeding habitat patches, with the highest abundance found in patches with the best local conditions. The species were present for large feeding patches, as well as for a higher proximity index for those patches. This result emphasized the importance of the supplementation of feeding habitats. By quantifying spatial habitat relationships using spatial metrics, it was possible to identify the best suited configuration of functional habitats to the needs of shoals. At present, most conservation work focuses on restoring local habitats. There is also growing interest in large-scale fish management, which has been encouraged by the advent of metapopulation theory. This study highlights the need for greater work at a third, intermediate scale that is no less significant: restoring daily and seasonal movements between functional habitats.

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“…This is because the catch and effort data of each vessel are longitudinal data, which means that several measurements are made on the same experimental units over time. In fisheries science, in these cases the use of GLMMs is recommended (VENABLES and DICHMONT, 2004). Moreover, the random terms do not contribute to the fixed part of the mean, but the variance components associated with them inflate the variability of predictions in an appropriate way (VENABLES and DICHMONT, 2004;CRAWLEY, 2007).…”

Section: Discussion Model Selectionmentioning

confidence: 99%

“…In fisheries science, in these cases the use of GLMMs is recommended (VENABLES and DICHMONT, 2004). Moreover, the random terms do not contribute to the fixed part of the mean, but the variance components associated with them inflate the variability of predictions in an appropriate way (VENABLES and DICHMONT, 2004;CRAWLEY, 2007). The analysis of abundance indices by GLM may underestimate the level of variability since it ignores the grouped nature of tows within vessels (HELSER et al, 2004;BAUM and BLANCHARD, 2010).…”

Section: Discussion Model Selectionmentioning

confidence: 99%

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Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?

2015

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Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?The catch per unit of effort (CPUE) data of cockfish, Callorhinchus callorynchus, during 1986-2011 was evaluated for the bottom trawl fishery of the San Matías gulf (Patagonia, Argentina). The objective of this work was to detect what are the factors related to fishery dynamic that affect catch rate of cockfish and to assess standardized CPUE by General linear models (GLMs) and General linear mixed models (GLMMs) as a relative abundance index. The annual trend of the catch rate indicated an increase during the evaluated period. The nominal CPUE and the indices standardized by the Delta-GLM and Delta-GLMM showed the same annual trend, with increases of 57%, 61.1% and 60.7%, respectively. The LogNormal models have the best-fit model and explained 23.5% of the total variability. The factors year, month, depth and hake CPUE explained the highest variability. The analysis of the models indicated that the catch rate of cockfish is subject to the lack of an homogeneous distribution of the fishing effort during different years, months and vessels. This was related to the fleet dynamic searching the targets species. The increasing trend of CPUE and landings would be indicating an increase in effort directionality to the cockfish in the last decade. Descriptors: Standardized CPUE, Holocephali, Chondrichtyans bycatch, GLM, GLMM, Chimaeroids. AbstrActDados de captura por unidade de esforço (CPUE) do peixe-galo Callorhinchus callorynchus foram avaliados para a pesca de arrasto de fundo no Golfo de San Matías (Patagonia, Argentina), durante o período 1986-2011. Os objetivos do trabalho foram identificar quais os fatores relacionados à dinâmica pesqueira que afetam a taxa de captura do peixe-galo e verificar a viabilidade em utilizar dados de CPUEs padronizadas pelos Modelos Lineares Gerais (GLM) e Modelos Lineares Gerais Mistos (GLMMs) como índices de abundância relativa. A tendência anual da taxa de captura indicou um aumento durante o período avaliado. A CPUE nominal e os índices padronizados pelo Delta-GLM e Delta-GLMM apresentaram a mesma tendência anual, com aumentos de 57%, 61.1% e 60.7%, respectivamente. Os modelos LogNormal foram os de melhor ajuste e explicaram 23,5% da variabilidade total dos dados. Ano, mês, profundidade e "hake" CPUE foram os fatores mais explicativos. A análise dos modelos indicou que a taxa de captura do peixe-galo está ligada à falta de distribuição homogênea do esforço de pesca durante os diferentes anos, meses e navios. Este fato esteve relacionado à dinâmica das frotas na busca das espécies alvo. A tendência crescente no aumento das CPUE e descargas em terra estaria indicando a mudança do esforço de pesca direcionado ao peixe-galo na última década.

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GLMs, GAMs and GLMMs: an overview of theory for applications in fisheries research

Cited by 355 publications

References 45 publications

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana

Using a continuous riverscape survey to examine the effects of the spatial structure of functional habitats on fish distribution

Standardized catch rate of cockfish, Callorhinchus callorynchus, in a bottom trawl fishery of Patagonia: Is it possible its use as a predictor of abundance trend?

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