Description: The pelagic ecosystem of the Arctic Ocean is threatened by severe changes such as the reduction in sea‐ice coverage and increased inflow of warmer Atlantic water. The latter is already altering the zooplankton community, highlighting the need for monitoring studies. It is therefore essential to accelerate the taxonomic identification to speed up sample analysis, and to expand the analysis to biomass and size assessments, providing data for modeling efforts. Our case study in Fram Strait illustrates that image‐based analyses with the ZooScan provide abundance data and taxonomic resolutions that are comparable to microscopic analyses and are suitable for zooplankton monitoring purposes in the Arctic. We also show that image analysis allows to differentiate developmental stages of the key species Calanus spp. and Metridia longa and, thus, to study their population dynamics. Our results emphasize that older preserved samples can be successfully reanalyzed with ZooScan. To explore the applicability of image parameters for calculating total mesozooplankton and Calanus spp. biomasses, we used (1) conversion factors (CFs) translating wet mass to dry mass (DM), and (2) length–mass (LM) relationships. For Calanus spp., the calculated biomass values yielded similar results as direct DM measurements. Total mesozooplankton biomass ranged between 1.6 and 15 (LM) or 2.4 and 21 (CF) g DM m², respectively, which corresponds to previous studies in Fram Strait. Ultimately, a normalized biomass size spectra analysis provides 1st insights into the mesozooplankton size structure at different depths, revealing steep slopes in the linear fit in communities influenced by Atlantic water inflow.

Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347

Description: Passive acoustic monitoring is an important tool in marine mammal studies. However, logistics and finances frequently constrain the number and servicing schedules of acoustic recorders, requiring a trade-off between deployment periods and sampling continuity, i.e., the implementation of a subsampling scheme. Optimizing such schemes to each project's specific research questions is desirable. This study investigates the impact of subsampling on the accuracy of two common metrics, acoustic presence and call rate, for different vocalization patterns (regimes) of baleen whales: (1) variable vocal activity, (2) vocalizations organized in song bouts, and (3) vocal activity with diel patterns. To this end, above metrics are compared for continuous and subsampled data subject to different sampling strategies, covering duty cycles between 50% and 2%. The results show that a reduction of the duty cycle impacts negatively on the accuracy of both acoustic presence and call rate estimates. For a given duty cycle, frequent short listening periods improve accuracy of daily acoustic presence estimates over few long listening periods. Overall, subsampling effects are most pronounced for low and/or temporally clustered vocal activity. These findings illustrate the importance of informed decisions when applying subsampling strategies to passive acoustic recordings or analyses for a given target species.

Description: Aim: Species distribution models (SDMs) are essential tools in ecology and conservation. However, the scarcity of visual sightings of marine mammals in remote polar areas hinders the effective application of SDMs there. Passive acoustic monitoring (PAM) data provide year-round information and overcome foul weather limitations faced by visual surveys. However, the use of PAM data in SDMs has been sparse so far. Here, we use PAM-based SDMs to investigate the spatiotemporal distribution of the critically endangered Antarctic blue whale in the Weddell Sea. Location: The Weddell Sea. Methods: We used presence-only dynamic SDMs employing visual sightings and PAM detections in independent models. We compared the two independent models with a third combined model that integrated both visual and PAM data, aiming at leveraging the advantages of each data type: the extensive spatial extent of visual data and the broader temporal/environmental range of PAM data. Results: Visual and PAM data prove complementary, as indicated by a low spatial overlap between daily predictions and the low predictability of each model at detections of other data types. Combined data models reproduced suitable habitats as given by both independent models. Visual data models indicate areas close to the sea ice edge (SIE) and with low-to-moderate sea ice concentrations (SIC) as suitable, while PAM data models identified suitable habitats at a broader range of distances to SIE and relatively higher SIC. Main Conclusions: The results demonstrate the potential of PAM data to predict year-round marine mammal habitat suitability at large spatial scales. We provide reasons for discrepancies between SDMs based on either data type and give methodological recommendations on using PAM data in SDMs. Combining visual and PAM data in future SDMs is promising for studying vocalized animals, particularly when using recent advances in integrated distribution modelling methods.