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@@ -52,7 +52,7 @@ 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 spatially-explicit surplus production models (SSPM) simpler to estimate and apply to any spatially structured stock. The basic model it 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 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.
 
 Although it was developed in a fisheries context, the package is suited to model spatially-structured population dynamics in general.