<p><strong>Abstract.</strong> Neogloboquadrina pachyderma is the dominant species in the polar regions. In the northern high latitude ocean, it makes up more than 90 % of the total planktonic foraminifera assemblages, making it the dominant pelagic calcifier and carrier of paleoceanographic proxies. To assess the reaction of this species to future climate change and to be able to interpret the paleoecological signal contained in its shells, its habitat depth must be known. Previous work showed that <i>N. pachyderma</i> in the northern polar regions has a highly variable depth habitat, ranging from the surface mixed layer to several hundreds of metres below the surface, and the origin of this variability remained unclear. In order to investigate the factors controlling the habitat depth of <i>N. pachyderma</i>, we compiled new and existing population density profiles from 104 stratified plankton tow hauls collected in the Arctic and the North Atlantic Oceans during 14 oceanographic expeditions. For each vertical profile, the Depth Habitat (DH) was calculated as the abundance-weighted mean depth of occurrence. We then tested to what degree environmental factors (mixed layer depth, sea surface temperature, sea surface salinity, Chlorophyll a concentration and sea ice concentration) and ecological factors (synchronised reproduction and daily vertical migration) can predict the observed DH variability and compared the observed DH behaviour with simulations by a numerical model predicting planktonic foraminifera distribution. Our data show that the DH of <i>N. pachyderma</i> varies between 25 m and 280 m (average ~ 100 m). In contrast with the model simulations, which indicate that DH is associated with the depth of chlorophyll maximum, our analysis indicates that the presence of sea-ice together with the concentration of chlorophyll at the surface have the strongest influence on the vertical habitat of this species. <i>N. pachyderma</i> occurs deeper when sea-ice and chlorophyll concentrations are low, suggesting a time transgressive response to the evolution of (near) surface conditions during the annual cycle. Since only surface parameters appear to affect the vertical habitat of <i>N. pachyderma</i>, light or light-dependant processes might influence the ecology of this species. Our results can be used to improve predictions of the response of the species to climate change and thus to refine paleoclimatic reconstructions.</p>

Abstract. Species of planktonic foraminifera exhibit specific seasonal production patterns and different preferred vertical habitats. The seasonality and vertical habitats are not constant throughout the range of the species and changes therein must be considered when interpreting paleoceanographic reconstructions based on fossil foraminifera. Accounting for the effect of vertical and seasonal habitat tracking on foraminifera proxies at times of climate change is difficult because it requires independent fossil evidence. An alternative that could reduce the bias in paleoceanographic reconstructions is to predict species-5 specific habitat shifts under climate change using an ecosystem modeling approach. To this end, we present a new version of a planktonic foraminifera model, PLAFOM2.0, embedded into the ocean component of the Community Earth System Model, version 1.2.2. This model predicts monthly global concentrations of the planktonic foraminiferal species: Neogloboquadrina pachyderma, N. incompta, Globigerina bulloides, Globigerinoides ruber (white), and Trilobatus sacculifer throughout the world ocean, resolved in 24 vertical layers to 250 m depth. The resolution along the vertical dimension has been implemented by 10 applying the previously used spatial parameterization of biomass as a function of temperature, light, nutrition, and competition on depth-resolved parameter fields. This approach alone results in the emergence of species-specific vertical habitats, which are spatially and temporally variable. Although an explicit parameterization of the vertical dimension has not been carried out, the seasonal and vertical distribution patterns predicted by the model are in good agreement with sediment trap data and plankton tow observations. In the simulation, the colder-water species N. pachyderma, N. incompta, and G. bulloides show 15 a pronounced seasonal cycle in their depth habitat in the polar and subpolar regions, which appears to be controlled by food availability. During the warm season, these species preferably occur in the subsurface, while towards the cold season they ascend through the water column and are found closer to the sea surface. The warm-water species G. ruber (white) and T.sacculifer exhibit a less variable shallow depth habitat with highest biomass concentrations within the top 40 m of the water column. Nevertheless, even these species show vertical habitat variability and their seasonal occurrence outside the tropics 20 is limited to the warm surface layer that develops at the end of the warm season. The emergence in PLAFOM2.0 of speciesspecific vertical habitats that are consistent with observations indicates that the population dynamics of planktonic foraminifera species may be driven by the same factors in time, space, and with depth, in which case the model can provide a reliable and robust tool to aid the interpretation of proxy records.

Species of planktonic foraminifera exhibit specific seasonal production patterns and different preferred vertical habitats. The seasonal and vertical habitats are not constant throughout the range of the species and changes therein must be considered when interpreting paleoceanographic reconstructions based on fossil foraminifera. However, detecting the effect of changing vertical and seasonal habitat on foraminifera proxies requires independent evidence for either habitat or climate change. In practice, this renders accounting for habitat tracking from fossil evidence almost impossible. An alternative method that could reduce the bias in paleoceanographic reconstructions is to predict speciesspecific habitat shifts under climate change using an ecosystem modeling approach. To this end, we present a new version of a planktonic foraminifera model, PLAFOM2.0, embedded into the ocean component of the Community Earth System Model version 1.2.2. This model predicts monthly global concentrations of the planktonic foraminiferal species Neogloboquadrina pachyderma, N. incompta, Globigerina bulloides, Globigerinoides ruber (white), and Trilobatus sacculifer throughout the world ocean, resolved in 24 vertical layers to 250 m of depth. The resolution along the vertical dimension has been implemented by applying the previously used spatial parameterization of carbon biomass as a function of temperature, light, nutrition, and competition on depth-resolved parameter fields. This approach alone results in the emergence of species-specific vertical habitats, which are spatially and temporally variable. Although an explicit parameterization of the vertical dimension has not been carried out, the seasonal and vertical distribution patterns predicted by the model are in good agreement with sediment trap data and plankton tow observations. In the simulation, the colder-water species N. pachyderma, N. incompta, and G. bulloides show a pronounced seasonal cycle in their depth habitat in the polar and subpolar regions, which appears to be controlled by food availability. During the warm season, these species preferably occur in the subsurface (below 50 m of water depth), while towards the cold season they ascend through the water column and are found closer to the sea surface. The warm-water species G. ruber (white) and T. sacculifer exhibit a less variable shallow depth habitat with highest carbon biomass concentrations within the top 40 m of the water column. Nevertheless, even these species show vertical habitat variability and their seasonal occurrence outside the tropics is limited to the warm surface layer that develops at the end of the warm season. The emergence in PLAFOM2.0 of species-specific vertical habitats, which are consistent with observations, indicates that the population dynamics of planktonic foraminifera species may be driven by the same factors in time, space, and with depth, in which case the model can provide a reliable and robust tool to aid the interpretation of proxy records.

Species of planktonic foraminifera exhibit specific seasonal production patterns and different preferred vertical habitats. The seasonal and vertical habitats are not constant throughout the range of the species and changes therein must be considered when interpreting paleoceanographic reconstructions based on fossil foraminifera. However, detecting the effect of changing vertical and seasonal habitat on foraminifera proxies requires independent evidence for either habitat or climate change. In practice, this renders accounting for habitat tracking from fossil evidence almost impossible. An alternative method that could reduce the bias in paleoceanographic reconstructions is to predict speciesspecific habitat shifts under climate change using an ecosystem modeling approach. To this end, we present a new version of a planktonic foraminifera model, PLAFOM2.0, embedded into the ocean component of the Community Earth System Model version 1.2.2. This model predicts monthly global concentrations of the planktonic foraminiferal species Neogloboquadrina pachyderma, N. incompta, Globigerina bulloides, Globigerinoides ruber (white), and Trilobatus sacculifer throughout the world ocean, resolved in 24 vertical layers to 250 m of depth. The resolution along the vertical dimension has been implemented by applying the previously used spatial parameterization of carbon biomass as a function of temperature, light, nutrition, and competition on depth-resolved parameter fields. This approach alone results in the emergence of species-specific vertical habitats, which are spatially and temporally variable. Although an explicit parameterization of the vertical dimension has not been carried out, the seasonal and vertical distribution patterns predicted by the model are in good agreement with sediment trap data and plankton tow observations. In the simulation, the colder-water species N. pachyderma, N. incompta, and G. bulloides show a pronounced seasonal cycle in their depth habitat in the polar and subpolar regions, which appears to be controlled by food availability. During the warm season, these species preferably occur in the subsurface (below 50 m of water depth), while towards the cold season they ascend through the water column and are found closer to the sea surface. The warm-water species G. ruber (white) and T. sacculifer exhibit a less variable shallow depth habitat with highest carbon biomass concentrations within the top 40 m of the water column. Nevertheless, even these species show vertical habitat variability and their seasonal occurrence outside the tropics is limited to the warm surface layer that develops at the end of the warm season. The emergence in PLAFOM2.0 of species-specific vertical habitats, which are consistent with observations, indicates that the population dynamics of planktonic foraminifera species may be driven by the same factors in time, space, and with depth, in which case the model can provide a reliable and robust tool to aid the interpretation of proxy records.

Species of planktonic foraminifera exhibit specific seasonal production patterns and different preferred vertical habitats. The seasonal and vertical habitats are not constant throughout the range of the species and changes therein must be considered when interpreting paleoceanographic reconstructions based on fossil foraminifera. However, detecting the effect of changing vertical and seasonal habitat on foraminifera proxies requires independent evidence for either habitat or climate change. In practice, this renders accounting for habitat tracking from fossil evidence almost impossible. An alternative method that could reduce the bias in paleoceanographic reconstructions is to predict speciesspecific habitat shifts under climate change using an ecosystem modeling approach. To this end, we present a new version of a planktonic foraminifera model, PLAFOM2.0, embedded into the ocean component of the Community Earth System Model version 1.2.2. This model predicts monthly global concentrations of the planktonic foraminiferal species Neogloboquadrina pachyderma, N. incompta, Globigerina bulloides, Globigerinoides ruber (white), and Trilobatus sacculifer throughout the world ocean, resolved in 24 vertical layers to 250 m of depth. The resolution along the vertical dimension has been implemented by applying the previously used spatial parameterization of carbon biomass as a function of temperature, light, nutrition, and competition on depth-resolved parameter fields. This approach alone results in the emergence of species-specific vertical habitats, which are spatially and temporally variable. Although an explicit parameterization of the vertical dimension has not been carried out, the seasonal and vertical distribution patterns predicted by the model are in good agreement with sediment trap data and plankton tow observations. In the simulation, the colder-water species N. pachyderma, N. incompta, and G. bulloides show a pronounced seasonal cycle in their depth habitat in the polar and subpolar regions, which appears to be controlled by food availability. During the warm season, these species preferably occur in the subsurface (below 50 m of water depth), while towards the cold season they ascend through the water column and are found closer to the sea surface. The warm-water species G. ruber (white) and T. sacculifer exhibit a less variable shallow depth habitat with highest carbon biomass concentrations within the top 40 m of the water column. Nevertheless, even these species show vertical habitat variability and their seasonal occurrence outside the tropics is limited to the warm surface layer that develops at the end of the warm season. The emergence in PLAFOM2.0 of species-specific vertical habitats, which are consistent with observations, indicates that the population dynamics of planktonic foraminifera species may be driven by the same factors in time, space, and with depth, in which case the model can provide a reliable and robust tool to aid the interpretation of proxy records.