In EffetsCumulatifsNavigation/ceanav: Research compendium for project on the assessment cumulative effects of marine vessel activities in the St. Lawrence

Commercial fishing {#pechecommerciale}

We thank Marc Mingelbier, Paschale Noël Bégin et Marie-Josée Gagnon from the Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs for the discussions and support in characterizing commercial fisheries in the fluvial sector.

meta <- read_yaml("../data/data-metadata/int_st_peche_commerciale.yml")
show_source(meta$rawData, lang = "en")

Marine sector

The intensity of commercial fishing activity within the study area was assessed using data from the Fisheries and Oceans Canada logbook program [@mpo2021; @mpo2021b; @mpo2021c]. Although logbooks are not mandatory for all fisheries, they still provide a sufficiently exhaustive assessment of commercial fishing activity distribution and intensity in the study area. Although there is a commercial fishery in the fluvial sector and in the Saguenay River, we were unable to obtain data enabling us to characterize its distribution and intensity. The lack of data in these areas should therefore not be interpreted as an absence of commercial fishing; this gap should be addressed for a future assessment.

We used data between r meta$dataDescription$temporal$start and r meta$dataDescription$temporal$end to characterize commercial fishing distribution and intensity in the study area. This period is characterized by r meta$dataDescription$observations$total fishing activities (r meta$dataDescription$observations$moyenne $\pm$ r meta$dataDescription$observations$sd observations per year). During this period, a total of r length(meta$dataDescription$especes$cible) species were targeted for commercial fishing and a total of r length(meta$dataDescription$especes$capture$ID) different species were caught (Table \@ref(tab:pecheEspece)).

tab <- as.data.frame(meta$dataDescription$especes$capture)
tab <- arrange(tab, desc(Freq))
tab$Freq <- as.character(tab$Freq)
uid <- tab$ID %in% meta$dataDescription$especes$cible
tab[uid,] <- apply(tab[uid, ], 2, function(x) glue("**{x}**"))
tab$Scientific <- glue("*{tab$Scientific}*")

tab[,-1] %>%
  knitr::kable(
    col.names = c("Scientific name", "French name","English name","Frequency"),
    caption = "Species captured by commercial fisheries in the study area [@mpo2021]. Species in bold are the species targetted by fisheries. The unique identifiers come from a dataset providing an index of species for commercial fisheries [@mpo2021c]"
  )

Commercial fishing is carried out with a wide variety of fishing gear such as traps, trawls, dragging, gillnets and longlines. Each type of fishing gear may have different effects on the ecosystems where fishing activities take place. For example, fishing with traps has very different effects from fishing with trawls. We thus divided fishing based on their environmental effects according to the categories suggested by @halpern2008a and repeated by @beauchesne2020 for the Estuary and Gulf of St. Lawrence (Tables \@ref(tab:pecheEnginFreq) and \@ref(tab:pecheEngin)): demersal, destructive, high bycatch (DD) demersal, non-destructive, high bycatch (DNH) demersal, non-destructive, low bycatch (DNL) pelagic, high bycatch (PHB) pelagic, low bycatch (PLB)

Gear can also be categorized based on its mobility. Fixed gear such as traps are left in place and have a more localized effect. Mobile gear such as trawls can be towed for several kilometres during fishing activities. We used the type of mobility to generate an area of effects for fishing activities. This approach allows for the potential uncertainty associated with fishing activity coordinates, gear mobility, and the absence of start and end coordinates for mobile gear. We used an area with a radius of 200 and 2000 metres for fixed and mobile gear fishing activities, respectively [@beauchesne2020]. Types of gear were characterized using a commercial fisheries fishing gear index database together with the logbook database [@mpo2021b].

tab <- as.data.frame(meta$dataDescription$categories) %>%
       filter(source == "0033,0034,0035") %>%
       select(english, description_en, frequence)
beg <- meta$dataDescription$temporal$start
end <- meta$dataDescription$temporal$end

tab %>%
  knitr::kable(
    col.names = c("Category", "Description", "Frequency"),
    caption = paste("Description of gear type categories with the frequency of commercial fishing activities recorded in logbooks between", beg, "and", end, "[@mpo2021; @mpo2021b; @mpo2021c].")
  )

tab <- data.frame(
  gear = c("Trap","Bottom trawl","Drag","Gillnet","Line fishing",
           "Longline","Diving","Purse seine","Danish or Scottish seine",
           "Shoreline seine","Trap","Jigger"),
  class = c("DNH","DD","DD","PHB","PLB","PHB","DNL","PLB","DNH","DNH","DNH","PLB"),
  mob = c("Fixed","Mobile","Mobile","Fixed","Fixed","Fixed","Fixed","Fixed","Fixed","Fixed","Fixed","Fixed")
)

tab %>%
  knitr::kable(
    col.names = c("Type of gear", "Category", "Mobility"),
    caption = "Classification of gear types according to their environmental effects and mobility. DD: demersal, destructive, high bycatch; DNH: demersal, non-destructive, high bycatch; DNL: demersal, non-destructive, low bycatch; PHB: pelagic, high bycatch; PLB: pelagic, low bycatch. Adapted from @beauchesne2020."
  )

To characterize fishing intensity ($I_{peche}$), we assessed the total frequency of fishing activities during the period considered (r meta$dataDescription$temporal$start - r meta$dataDescription$temporal$end) overlapping each study grid cell:

$$I_{fishing,j} = \sum_{k=1}^{n_j} P_{j,k}$$

where $j$ is a study grid cell, $k$ is a fishing activity, and $P_k$ is the buffer zone surrounding each $k$ fishing activity. This formula calculates fishing intensity expressed as the number of identified fishing activities. Since we have calculated the intensity within cells of 1 $km^2$, the units of this formula are in number of activities ($n$) per $km^2$, or $n * km^{-2}$. Since buffer zones were used around each activity, it is possible that a single fishing activity is identified for more than one cell in the study grid. The total sum of activities in the study grid will therefore not equal the actual total sum of fishing activities identified in the study area.

knitr::include_graphics("./figures/figures-integrated/peche_commerciale-DD.png")

knitr::include_graphics("./figures/figures-integrated/peche_commerciale-DNH.png")

knitr::include_graphics("./figures/figures-integrated/peche_commerciale-DNL.png")

knitr::include_graphics("./figures/figures-integrated/peche_commerciale-PHB.png")

knitr::include_graphics("./figures/figures-integrated/peche_commerciale-PLB.png")

Fluvial sector

Data characterizing commercial fisheries in the fluvial sector were included to the cumulative effects assessment during the 2023 assessment update. Those data come from a description of the main pressures observed in the St. Lawrence in 2012 [@mingelbier2012]. Commercial fisheries activities using fyke nets during the spring, the summer and the fall before 2011 were characterized in segments of 5 km, and dividing the St. Lawrence between the southern and northern shores using the waterway. The intensity of commercial fisheries was measured as the mean number of fyke nets deployed par day in each segment dividing the fluvial sector of the study area; data were then given a value of 1, 3 or 5, corresponding to low, moderate and high fisheries intensity. Those values were integrated in the study grid for the assessment. It should be noted that those data are not exhaustive or representative of all commercial fisheries activities in the fluvial sector; they are, however, the most up to date information available at this time.

knitr::include_graphics("./figures/figures-integrated/peche_commerciale-peche_fleuve.png")

EffetsCumulatifsNavigation/ceanav documentation built on April 17, 2023, 1:02 p.m.