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Fix typo #143

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Jun 20, 2023
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Fix typo #143

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748ea9f
Fix typo
Bisaloo Jun 19, 2023
24a5fe6
Fix capitalization in species name
Bisaloo Jun 19, 2023
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2 changes: 1 addition & 1 deletion paper/paper.bib
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Expand Up @@ -341,7 +341,7 @@ @book{wood_generalized_2017
@techreport{dfo_assessment_2019,
type = {techreport},
institution = {Canadian Science Advisory Secretariat (CSAS)},
title = {An Assessment of {Northern} {Shrimp} (Pandalus borealis) in {Shrimp} {Fishing} {Areas} 4–6 and of {Striped} {Shrimp} (Pandalus montagui) in Shrimp Fishing Area 4 in 2018},
title = {An Assessment of {Northern} {Shrimp} ({Pandalus borealis}) in {Shrimp} {Fishing} {Areas} 4–6 and of {Striped} {Shrimp} ({Pandalus montagui}) in Shrimp Fishing Area 4 in 2018},
number = {2019/027},
author = {{DFO}},
year = {2019},
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2 changes: 1 addition & 1 deletion paper/paper.md
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Expand Up @@ -51,7 +51,7 @@ We have chosen a statistical approach to fitting SPMs. Statistical models allow

In this paper, we use a statistical approach to fitting SPMs using Generalized Additive Models (GAMS), estimated using the `mgcv` R package [@wood_generalized_2017] as the backend. We apply this approach to the population of Northern Shrimp of the Newfoundland and Labrador Shelves, leveraging the smoothing properties of GAMs to account for varying productivity across time and space. The resulting model is a spatial SPM (SSPM), implemented via an R package: `sspm`.

The R package `sspm` is designed to make SSPMs simpler to estimate and apply to any spatially structured stock. The basic model this packages implements was first used to model time-varying production in Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021]. However, the general modelling approach used here will work for any spatially structured fishery with sufficient data. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow} and in the next section.
The R package `sspm` is designed to make SSPMs simpler to estimate and apply to any spatially structured stock. The basic model this package implements was first used to model time-varying production in Newfoundland and Labrador Northern Shrimp stocks [@pedersenNewSpatialEcosystembased2021]. However, the general modelling approach used here will work for any spatially structured fishery with sufficient data. It includes a range of features to manipulate harvest and biomass data. Those features are organized in a stepwise workflow, whose implementation is described in more detail in \autoref{fig:workflow} and in the next section.

Although it was developed in a fisheries context, the package is suited to model spatially-structured population dynamics in general.

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