Insights on integrating habitat preferences in process-oriented ecological models – a case study of the southern North Sea

@article{Püts2020,
  Title = {Insights on integrating habitat preferences in process-oriented ecological models – a case study of the southern North Sea},
  Author = {Püts, Miriam and Taylor, Marc and Núñez-Riboni, Ismael and Steenbeek, Jeroen and Stäbler, Moritz and Möllmann, Christian and Kempf, Alexander},
  Editor = {},
  Year = {2020},
  Pages = {109189},
  Volume = {431},
  Doi = {10.1016/j.ecolmodel.2020.109189},
  Abstract = {One of the most applied tools to create ecosystem models to support management decisions in the light ofecosystem-basedfisheries management is Ecopath with Ecosim (EwE). Recently, its spatial routine Ecospace hasevolved due to the addition of the Habitat Foraging Capacity Model (HFCM), a spatial-temporal dynamic nichemodel to drive the foraging capacity to distribute biomass over model grid cells. The HFCM allows for con-tinuous implementation of externally derived habitat preference maps based on single species distributionmodels. So far, guidelines are lacking on how to best define habitat preferences for inclusion in process-orientedtrophic modeling studies. As one of thefirst studies, we applied the newest Ecospace development to an existingEwE model of the southern North Sea with the aim to identify which definition of habitat preference leads to thebest modelfit. Another key aim of our study was to test for the sensitivity of implementing externally derivedhabitat preference maps within Ecospace to different time-scales (seasonal, yearly, multi-year, and static). Forthis purpose, generalized additive models (GAM) werefit to scientific survey data using either presence/absenceor abundance as differing criteria of habitat preference. Our results show that Ecospace runs using habitatpreference maps based on presence/absence data compared best to empirical data. The optimal time-scale forhabitat updating differed for biomass and catch, but implementing variable habitats was generally superior to astatic habitat representation. Our study hence highlights the importance of a sigmoidal representation of habitat(e.g. presence/absence) and variable habitat preferences (e.g. multi-year) when combining species distributionmodels with an ecosystem model. It demonstrates that the interpretation of habitat preference can have a majorinfluence on the modelfit and outcome},
}

TY  - JOUR
AU  - Püts, Miriam
AU  - Taylor, Marc
AU  - Núñez-Riboni, Ismael
AU  - Steenbeek, Jeroen
AU  - Stäbler, Moritz
AU  - Möllmann, Christian
AU  - Kempf, Alexander
TI  - Insights on integrating habitat preferences in process-oriented ecological models – a case study of the southern North Sea
PY  - 2020
SP  - 109189
VL  - 431
DO  - 10.1016/j.ecolmodel.2020.109189
AB  - One of the most applied tools to create ecosystem models to support management decisions in the light ofecosystem-basedfisheries management is Ecopath with Ecosim (EwE). Recently, its spatial routine Ecospace hasevolved due to the addition of the Habitat Foraging Capacity Model (HFCM), a spatial-temporal dynamic nichemodel to drive the foraging capacity to distribute biomass over model grid cells. The HFCM allows for con-tinuous implementation of externally derived habitat preference maps based on single species distributionmodels. So far, guidelines are lacking on how to best define habitat preferences for inclusion in process-orientedtrophic modeling studies. As one of thefirst studies, we applied the newest Ecospace development to an existingEwE model of the southern North Sea with the aim to identify which definition of habitat preference leads to thebest modelfit. Another key aim of our study was to test for the sensitivity of implementing externally derivedhabitat preference maps within Ecospace to different time-scales (seasonal, yearly, multi-year, and static). Forthis purpose, generalized additive models (GAM) werefit to scientific survey data using either presence/absenceor abundance as differing criteria of habitat preference. Our results show that Ecospace runs using habitatpreference maps based on presence/absence data compared best to empirical data. The optimal time-scale forhabitat updating differed for biomass and catch, but implementing variable habitats was generally superior to astatic habitat representation. Our study hence highlights the importance of a sigmoidal representation of habitat(e.g. presence/absence) and variable habitat preferences (e.g. multi-year) when combining species distributionmodels with an ecosystem model. It demonstrates that the interpretation of habitat preference can have a majorinfluence on the modelfit and outcome
ER  -