Microhabitat patchiness structures benthic biodiversity in the Western Antarctic Peninsula

Microhabitat patchiness structures benthic biodiversity in the Western Antarctic Peninsula | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 6 August 2025 V1 Latest version Share on Microhabitat patchiness structures benthic biodiversity in the Western Antarctic Peninsula Authors : Lea Katz 0000-0001-5748-602X [email protected] , Emily Mitchell , Bruno Danis , and Huw Griffiths 0000-0003-1764-223X Authors Info & Affiliations https://doi.org/10.22541/au.175447826.65995956/v1 Published Ecology and Evolution Version of record Peer review timeline 265 views 151 downloads Contents Abstract Introduction Materials and Methods Image acquisition Image analysis Statistical analysis Results Hierarchical clustering and SIMPROF analysis Main contributing taxa to clusters Discussion Conclusion Bibliography Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In the accelerating global biodiversity crisis, it is imperative to document biodiversity patterns along with their underlying drivers. To support this, ecological studies must identify the processes driving species distributions. Our objective was to study how benthic community variability is structured across spatial scales, and how fine-scale patchiness contributes to larger scale biodiversity in the Western Antarctic Peninsula. Using underwater imagery, we quantified benthic community composition at different scales using a nested sampling design. We used cluster analyses (NMDS, SIMPROF and SIMPER analysis) to detect patterns in benthic community variability. NMDS showed that seemingly different stations had many similar images. Images were clustered according to their community composition into 31 signifcant clusters, or microhabitat types, which were not divided by geographic location, substrate type or macroalgal cover, like initially presumed. Instead, the differences in faunal assemblage seem to be influenced by the dominant macroalgae species and biogenic habitat complexity. All in all, we found that these microhabitats emerge as ecologically meaningful units that offer a practical scale at which to detect patterns and early ecological shifts. Changes in the distribution or frequency of these microhabitats may occur before shifts in species composition become detectable at site or station level, which is crucial for ecosystem monitoring. Introduction Marine biodiversity is important for maintaining ecological equilibrium of oceanic ecosystems, providing essential services (Goulletquer et al. 2014), but it is currently under significant threat (Duterte 2025). The threats to biodiversity are primarily attributed to anthropogenic pressure: direct pressures such as overfishing and destructive fishing, resource extraction, pollution and introduction of invasive species (Kent 2023, Duterte 2025); but also indirect pressures linked to climate change. In the face of global warming and an accelerating biodiversity crisis, it is imperative to document biodiversity patterns along with their underlying drivers. To support this, ecological studies must identify the processes driving species distributions, which is crucial for guiding practical conservation and management decisions (Rodil et al. 2021). Habitat complexity and heterogeneity are widely recognized as key drivers of biodiversity, enhancing abundance, taxonomic richness and community dissimilarity (Thomsen et al. 2022, Leite Jardim et al. 2025). While much of this complexity arises from physical factors such as substrate type and topography, habitat-forming organisms like macroalgae and sponges also contribute significantly to structural heterogeneity by creating 3D complexity that supports diverse associated communities (Gutt 2017, Rossi et al. 2017). Despite the growing evidence that spatial heterogeneity enhances biodiversity, most ecological studies and predictive models are constrained to single spatial or temporal scales (Teng et al. 2020). This limitation risks overlooking fine-scale variability and multi-scale processes that may be ecologically meaningful (Zelnik et al. 2024). Large scale analysis may either miss early signals of ecological change or obscure them though spatial averaging, masking the rich patterning of ecological responses (Tielens et al. 2019, Zelnik et al. 2024). These missing signals may be particularly problematic in rapidly changing systems where detecting subtle, early shifts can inform timely management responses. The Western Antarctic Peninsula (WAP) presents a unique framework to study biodiversity patterns in a system with practically no direct human disturbance, due to strict regulations by the Commision for the Conservation of Antarctic Marine Living Resources (CCAMLR). This protection allows ecological processes to be examined in relative isolation from confounding anthropogenic impacts. However, the region is undergoing rapid environmental change (Turner et al. 2014, Chown et al. 2022), particularly in sea-ice dynamics (Pörtner et al. 2019, Siegert et al. 2019, Eayrs et al. 2021), which are known to affect benthic communities (Smale 2008, Pasotti et al. 2015, Clark et al. 2017, Gutt et al. 2019, Amsler et al. 2023, Griffiths et al. 2024) . Along the WAP, the winter sea-ice cover has decreased by more than three months, with virtually no more winter sea-ice cover in the northern part of the WAP (extending from the South Shetland Islands to around 65 °S), and significantly longer ice-free conditions in the southern part (below 67°S) (Stammerjohn et al. 2008, 2012, Matsuoka et al. 2021). The northern part of the WAP is characterized by a shorter sea-ice season, a higher sea surface temperature, a maritime climate with more overall precipitations, important coastal near-shore meltwater runoff and all year-round light availability (although much reduced during the winter months) compared to its southern counterpart (Matsuoka et al. 2021). Nonetheless, the WAP is recognized as a hotspot for benthic organisms (Gutt et al. 2016), and despite the importance of fine-scale heterogeneity, benthic habitats of the WAP are poorly documented (Gutt et al. 2019), thus representing a key knowledge gap in understanding spatial biodiversity patterns in polar marine ecosystems. Furthermore, this lack of data poses challenges for community-driven programs such as the Scientific Committee on Antarctic Research - Antarctic Near-Shore and Terrestrial Observation System (SCAR-ANTOS) or the Southern Ocean Observation System (SOOS), which require robust information for ongoing conservation efforts aimed at the designation and establishment of Marine Protected Areas in the framework of CCAMLR and the Antarctic Treaty Consultative Meeting – Committee on Environmental Protection (ATCM-CEP). In this study, we investigate how benthic community variability is structured across spatial scales, and how fine-scale patchiness contributes to larger scale biodiversity in the WAP. Using underwater imagery, we quantified benthic community composition and structure. We used multiple cluster analyses (NMDS, SIMPROF and SIMPER analysis) to detect patterns in benthic community variability. Our nested sampling design provides a rare multi-scale perspective, effectively capturing fine-scale biodiversity patterns while allowing comparisons across more extensive spatial scales. Materials and Methods Sampling stations All sampling events were done during the second expedition of the TANGO project that took place between February and March 2024 in the Western Antarctic Peninsula (WAP). For this second cruise, the crew focused on the Northern part of the WAP aboard a sailing boat, the RV Australis (Danis et al. 2024). During this cruise, three stations of interest were picked for their contrasting oceanographic features: Melchior Islands, Hovgaard Islands and Foyn Harbor, with 50 to 100km between each station (Figure 1). For our sampling strategy we opted for a nested design to integrate the effect of sampling scale on the survey, with different sampling areas (referred to as “sites” from now on) within each station, each separated by maximum 1km (Figure 1). Each site was usually a pool-like area, and several transects in different directions were made to cover the most of each site (Figure 1). Melchior Islands (64°19.246 S, 62°55.375 W) The Melchior Islands are a complex of islands lying between Brabant Island and Anvers Island. Sampling took place around the biggest island, Omega Island (Figure 1). First, the northern part was sampled (Melchior Islands – North Omega or “MIN”). Then, several sites were sampled in the south (Melchior Islands – South Omega or “MIS”). Within MIS, there were 3 different sampling sites, named after their position in the bay: inner bay (“MIS-I”), middle of the bay (“MIS-m”) and just outside the bay (“MIS-o”). Hovgaard Island (65°06.057 S, 64°04.992 W) Sampling sites are evenly spaced out in the whole area around the island (Figure 1): one in the north (“HI-n”), another in the south (“HI-s”) and a third one in the middle of the channel (“HI-c”). Foyn Harbor (64°32.798 S, 61°59.885 W) Foyn Harbor, an active centre for whaling operations until the 1930s, is an area with a few islands including Entreprise Island and the Guvernøren wreck. Three sampling sites are within the area (Figure 1): our boat anchoring pool (“FH-b”), a more southern pool (“FH-s”) and the eastern “wall” enclosing the area (“FH-w”). A fourth site just outside the area was added, close to Entreprise Island and (“FH-e”). DD MMMM YYYY \acceptedDD MMMM YYYY Figure 1 – General map of sampling area (Northern Wap) and detailed maps of sampling stations (Melchior Islands Omega North and South, Hovgaard Islands and Foyn Harbor) with locations of sampling sites and transects. FH-e is not represented on the map but is situated in north-east from FH-b (“boat”). Image acquisition Underwater image sampling was done using a small Remotely Operated Vehicle (BlueROV2, Heavy configuration) equipped with a GoPro HERO10 camera capturing videos of the seafloor. The BlueROV was deployed from either the main vessel or a zodiac and flown in linear transects at a constant altitude (approximately 1 meter from the bottom, adjusted for visibility) and a very slow pace (<1 m/s) to maintain a high video quality. Depending on the topography and deployment vessel, transects were either evenly spaced out in the sampling site, in a zig-zag pattern, or started from the same point and went in different directions, but never overlapping in both cases (Figure 1). Transect length varied from 25 to 110 meters. Image analysis For each video transect, still images were extracted using FFmpeg (Bellard 2023). And a set of evenly spaced-out images was selected for annotation from each transect, with a minimum of 30 images per site. The selection of the images was done automatically based on time-separation, but if an image was blurry, the closest non-blurry image was manually selected. Image annotation was performed in BIIGLE (Langenkämper et al. 2017). In each image, all visible animals, algae and substrate features were recorded using the CATAMI label tree (Althaus et al. 2015) with supplementary branches specific to Antarctic biota (Katz 2024). Since the identification level varied among taxa (species, genus or family), so we use the term ”morphotaxa” to refer to the identified biota throughout this study. For more common morphotaxa, we were able to confidently assign species as some were hand-picked by SCUBA divers (Danis et al. 2023). However, when labels lacked the precision required for this study, we chose to omit the corresponding images. The resulting abundance dataset comprised 336 annotated photos which included 2410 individual annotations, 91 identified CATAMI categories (of which 13 were substrate features). Individual animals were kept as counts, but the abundance of macroalgae and colonial animals was converted into percentage (%) cover. To simplify the substrate types, they were classified into four categories: soft (which included CATAMI labels ”sand_mud”, ”fine_sand” and ”coarse_sand”), hard (including CATAMI labels ”cobbles”, ”gravel”, ”pebble”, ”consolidated”, ”boulder”, ”rock”), mix, and invisible (substrate was invisible in more than 70% of the image). To compare the different sampling scales, abundances were summarised per image, transect, site and station. Statistical analysis Abundance data was transformed to square root to suitably weigh the importance of the very abundant morphotaxa and to increase the effect of rarer morphotaxa. Similarity between images was calculated using the Bray-Curtis similarity coefficient. Three different Nonmetric MultiDimensional Scaling (NMDS) ordinations were performed in R using the vegan package (Oksanen J et al. 2022) at three different spatial scales, changing the sample unit each time (image, transect and site). For the image-scale NMDS, two outliers (1 image from HI and 1 image from MI) were removed from the graph for more clarity. Hierarchical clustering on the images was performed in R (version 4.3.1, R Core Team 2023) and a SIMPROF analysis was run in PRIMER (version 7, Clarke and Gorley 2015) to determine statistically significant clusters/groups (named alphabetically: a, b, c , …, z , aa, ab , …, ae ). A Similarity Percentage (SIMPER) analysis was run in R between the most common groups from the SIMPROF analysis ( e, h, i, j, n, s, t, x, ab, ac, ad ). For each group, top contributing morphotaxa were calculated, and a short description of the community was written in Supporting information. DD MMMM YYYY \acceptedDD MMMM YYYY Results Biodiversity At the station level, Melchior Islands (MI) has the highest number of morphotaxa (53) and microhabitats (19), but the individual sites are poorer with between 18 and 37 morphotaxa (SI Table 2, SI Figure 1). This community composition results in MI having the lowest Shannon-Wiener diversity and evenness scores at both station (1.611 and 0.405) and site level (between 0.625-1.693 and between 0.216-0.533) (SI Table 2, SI Figures 1a & 1b). With a lower number of microhabitats (17), Hovgaard Islands (HI) has the lowest total number of morphotaxa (45) but hosts diverse communities at site and station level (SI Table 2, SI Figure 1) and the highest evenness of the three stations (0.611). Foyn Harbor (FH), with 12 microhabitats and 45 morphotaxa, is not the most biodiverse (2.117) or even (0.541) at the station scale but has the highest biodiversity metric for sites (between 1.775-2.072, SI Table 2, Figure 1). This is due to FH being dominated by the most diverse communities (SI Table 1, Figure 1). Substrate In both MI and FH, substrate composition was relatively balanced, meaning all substrate types were found and not one type dominated the area. This pattern was generally consistent across individual sites, except in MIS-i, where the bottom was composed of either unconsolidated sediment or obscured by macroalgae (45 and 55%, respectively), and no visible rocky substrate was observed. Similarly, FH-w and FH-b have a low proportion (60%), with only a limited proportion of hard substrate in the form of dropstones (approx.1%). Macroalgae MI exhibited the highest overall macroalgal cover among the stations (ranging from 40-65% across sites), closely followed by FH, which also showed consistently high values across sites (30-62% across sites). In contrast, HI had substantially lower total macroalgal cover (21-43% across sites). Sites such as MIN, MIS-i, MIS-m, and FH-w all recorded macroalgal cover exceeding 60%, frequently obscuring portions of the substrate and the underlying fauna. The lowest macroalgal cover was observed at FH-s and HI-s, with values below 30%. Across all sites, the macroalgal community was dominated by rhodophyta (notably Iridaea cordata, Trematocarpus antarcticus , and Plocamium hookeri ), while phaeophyceae such as Desmarestia antarctica and Himantothallus grandifolius were present in smaller proportions. NMDS Figure 2 shows the ordination of the sites, transects and images in two-dimensional NMDS plots. The site-scale NMDS (Fig. 2a) shows a clear distinction between stations and no overlap. The transect-scale NMDS (Fig. 2b) has a less distinct separation of the station hulls. There is a slight overlap between HI and FH. In the image-scale NMDS (Fig. 2c), the three stations overlap heavily, with no clear distinction between the three. DD MMMM YYYY \acceptedDD MMMM YYYY Figure 2 - NMDS plots of community data at 3 different scales: (a) unit=site, (b) unit=transect, (c) unit=image Hierarchical clustering and SIMPROF analysis From our data, 31 significant community composition clusters were identified (Figure 3). The separation between the clusters occurs at different similarity percentages and resulting groups of images are very uneven in size. The number of images per group varies between 1 and 46 (Figure 3). The groups with more than 10 images (12 groups in total) were kept for the SIMPER analysis and were the following (in alphabetical order): e, h, i, j, n, s, t, x, ab, ac, ad, ae. Figure 3 – (top) SIMPROF dendrogram showing significant groupings (p < 0.05) of benthic images based on community composition, using Bray-Curtis similarity. Groups are color-coded, with colored branches indicating different clusters, and main groups (+10 images) are highlighted. Horizontal bar below indicates sampling station of each image (Melchior Islands, Foyn Harbor, Hovgaard Islands). (bottom) Histogram showing the number of images per SIMPROF group, color-coded by station. Main contributing taxa to clusters The heatmaps in Figure 4 illustrate how the most influential morphotaxa contribute to the uniqueness of each station and each (main) image group, with their higher abundance (in pink) or their lower abundance (in blue). The branches on the left side of each heatmap represent how the top defining morphotaxa cluster together in each SIMPER analysis. Melchior Islands’ benthic communities differentiate themselves from the other two stations by their higher abundance of H. grandifolius (himantothallus_msp1), coralline algae (pink_encr_algae_msp1), Desmarestia antarctica (desmarestia_msp1), Desmarestia anceps (desmarestia_msp2), P. hookeri (branching_red_algae_msp1), and other macroalgae (Figure 4a). On the opposite, Hovgaard Islands has less of these algae, but its top contributing taxa are Odontaster validus (pink_star_msp1), Edwardsiella andrillae (edwardsiella_msp4) and diatom mats (filamentous_filiform_algae) (Figure 4a). The absence of Margarella antarctica (white_snail) and I. cordata (red_sheet_msp1) are also notable. Foyn Harbor’s communities are characterised by a high abundance in T. antarcticus (branching_red_algae_msp2) and I. cordata , and some very abundant taxa are Laternula elliptica and M. antarctica (Figure 4a). Groups that are well separated in the dendrogram (Figure 3, groups e to n ) don’t have much influential morphotaxa in common, whereas hierarchically close clusters in the dendrogram, such as group s and t , or group x , ab , ac , ad and ae , do share some important defining morphotaxa (Figure 4b). A table with descriptions of each group with top taxa and key images can be found in Supporting information. Figure 4 – Heatmaps of top contributing taxa to the SIMPER analysis: (a) between stations, (b) between main SIMPROF groups. Color of the cells represent the standardized abundance of morphotaxa in the group with relative higher abundance in pink and relative lower abundance in blue. DD MMMM YYYY \acceptedDD MMMM YYYY Discussion Our results show that the three stations are very different in terms of community compositions and patchiness, although they are not that far apart geographically (Figure 1) and are similar in depth and substrate. From this coarse spatial scale, it is evident that the three stations are ecologically distinct. Yet, a finer-scale analysis reveals a more nuanced picture of community similarity and variation across scales. The NMDS plots (Figure 2) highlight a strong influence of spatial resolution on community differentiation. When using “site” as the sampling unit—aggregating all images from a given site—there is a clear separation among the three stations. However, this distinction diminishes with finer resolution. At the “transect” level, some overlap emerges between stations, in particular FH and HI. At the finest scale, where “image” is the sampling unit, the separation between stations becomes indistinct (Figure 2). In fact, many images from different stations cluster closely together, suggesting substantial similarity in community composition across these images. These findings suggest that, in the Northern WAP, averaging at the site—or even transect—level may obscure substantial underlying heterogeneity and similarity between smaller spatial units. This supports the view that ecological patterns are scale-dependent (Kraan et al. 2015) and aligns with previous findings that emphasize the importance of multi-scale approaches or nested designs (Smale 2008, Kraan et al. 2015, Zelnik et al. 2024). The SIMPROF analysis identified 31 significant clusters of images across all stations, grouped by their similar benthic communities. Upon further investigation, this clustering is not solely driven by geographic separation (Figure 3). From now on, we define these groups of images with distinct communities as “microhabitats”, characterised by similar abundances of and co-occurrence patterns of key taxa (Figure 4b). The schematic representation in Figure 5 shows us that some of these microhabitats are widespread, occurring in the three stations at the same frequency (e.g. group ab ), others are more localised (group e only found in site HI-n). Figure 5 – Schematic representation of the effect of spatial scale on benthic community interpretation across our 3 stations (a) Station-level average community composition, shown as a solid color. (b) Microhabitat composition at the station-level. (c) Microhabitat composition at the site-level. Our results also reflect a substantial patchiness between and within sites. Different sites have different proportions and types of microhabitats (Figure 5). These different proportions could imply that the types, abundance and combination of microhabitats are driving the observed differences between sites and between stations. Each station can be considered a mosaic of microhabitats or “ecological units” occurring in different proportions (Figure 5). The presence and combination of microhabitats within a station or site has a profound influence on their biodiversity metrics and evenness of taxon distribution (SI Table 2). Many images from FH, which has the lowest number of microhabitats, are qualified as microhabitat x , which hosts the most biodiverse community and a high 3-dimensional structural complexity with dense red branching macroalgae and large branching sponges (Figure 5). The fact that all sites in FH include this microhabitat makes the station less heterogenous but does provide more connectivity for this microhabitat’s fauna. In contrast, MI has a high diversity of microhabitats (19) and includes microhabitats that are dominated by single species of macroalgae with high percentage cover and lacking in habitat forming animals ( s , t and n ). This high algal cover could exclude sessile invertebrates (Clark et al. 2013) and lead to an underestimation of abundance and richness of mobile taxa due to the limitations of underwater imagery (that we don’t see under the macroalgal canopy). In our study area, having a higher number of microhabitats doesn’t necessarily equate to higher diversity (SI Table 2). Indeed, other factors are important too, such as habitat complexity, connectivity and faunal similarity between microhabitats (SI Table 1). While these patterns at the microhabitat level are informative, they also raise important questions about ecological dynamics operating across multiple scales. The finer scale patterns that we highlighted influence biodiversity patterns at wider scales, while wider scale processes can influence small-scale variability (Teng et al. 2020, Zelnik et al. 2024, Johnston 2024). Another aspect emerging from the SIMPROF clustering is that similar communities can occur across physically distant areas. Much like island systems, microhabitat features such as dropstones have been shown to adhere to the principles of island biogeography (Post et al. 2017, Ziegler et al. 2017). This adherence supports the idea that microhabitat connectivity may be more ecologically relevant than geographical distance alone and likely affects biodiversity and ecosystem resilience (Tielens et al. 2019, Fourcade et al. 2021).This connectivity is important to consider when modelling species distributions or responses of organisms under changing environmental conditions (Keeley et al. 2018, Solà et al. 2024). The temporal dimension is also particularly relevant, as microhabitats may be short lived, shaped by rapid turnover processes such as algal growth cycles or physical disturbances (Momo 2020, Quartino 2020, Amsler et al. 2023). Our results align with several emerging studies, from benthic ecosystems to terrestrial landscapes, that emphasize the importance of cross-scale interactions and habitat heterogeneity in shaping biodiversity patterns (Thomsen et al. 2022, Zelnik et al. 2024, Barton et al. 2024, Johnston 2024). Having defined the distinct microhabitats and how they influence biodiversity, we now turn to explore the factors driving their composition and the extent of their differences. In the Western Antarctic Peninsula, a region undergoing rapid environmental changes, disruptions in sea-ice dynamics are known to have increased in the past years (Pörtner et al. 2019, Siegert et al. 2019, Eayrs et al. 2021) and have been proven to affect benthic life (Smale 2008, Pasotti et al. 2015, Amsler et al. 2023). Ice-related factors, such as sea-ice concentration and iceberg scouring, were considered in our interpretation of the drivers behind our observations. The North-South sea-ice gradient in the WAP and associated differences in light availability throughout the year (Amsler et al. 2023) could in part explain why Hovgaard Islands, which is located slightly more to the south (Figure 1) has a significantly lower macroalgae cover than the other two stations. However, field observations nor available sea-ice data could explain the meter-scale variations we observed. Moreover, iceberg scouring was not directly observed or quantified on our transects during our study. Another factor likely influencing the observed patterns is substrate type. Given its fundamental role in shaping benthic communities (Post et al. 2017, Almond et al. 2021, Romoth et al. 2023, Katz et al. 2025), it is reasonable to expect that differences in substrate composition contribute significantly to the microhabitat variation we documented. Areas with hard substrate such as rocky platforms or dropstones, are generally associated with higher biodiversity (Post et al. 2017, Ziegler et al. 2017, Almond et al. 2021). While substrate likely plays a key role, it does not fully account for the clustering of our microhabitats, as some share substrate types yet differ in benthic composition ( s and t ), and others (e.g. groups c , e , x , ab , ac , ad , ae ) span multiple substrate types (SI Figure 2). This was further supported by an ANOSIM test (not shown), which yielded a low R statistic (R=0.126, p=0.001). The SIMPER analysis (Figure 4b) suggests that macroalgae play a significant role in microhabitat differentiation. A primary hypothesis was that total macroalgal cover would be the most important factor in shaping these shallow benthic communities (SI Figure 2) however our results (Figure 4b) align more with the assumption that faunal communities are structured by the dominant algae morphotypes that are locally present (Amsler et al. 2015). This interpretation is further supported by the ANOSIM results based on macroalgae cover (not shown), which yielded a low R statistic (R=0.23, p =0.001). Some of our microhabitats (e.g. groups x , ab , ae ) are associated with complex algal structures (dense forests of T. antarcticus, I. cordata, or D. antarctica ) and the high diversity and abundance of fauna observed in these microhabitats may be linked to the 3D complexity, refugia and varied food source these algae provide (Duarte et al. 2020). Habitat complexity in general is known to increase diversity (Rodil et al. 2021, Thomsen et al. 2022, Leite Jardim et al. 2025), so it is important to also consider all habitat forming taxa such as sponges, as they are widely known as foundation species in the Antarctic, both in coastal and deep ecosystems (Downey et al. 2012, Gutt 2017, Mitchell et al. 2020). The high abundance of Dendrilla antarctica (spiky_yellow_msp7) in group x (Figure 4b), in addition to the complex algal beds, is likely behind this group being the most biodiverse type of microhabitat we found. In the context of a warming climate, the relevance of these findings becomes even more apparent. The West Antarctic Peninsula (WAP) is one of the most rapidly changing regions on Earth (Turner et al. 2014), and shallow marine ecosystems there have witnessed the most observed changes recently and are predicted to continue to change over coming decades (Griffiths et al. 2024). Increasing temperatures and winds are causing disruptions in sea ice dynamics, sea ice loss and the associated shifts in light availability have been shown to have an extensive impact on benthic communities (Cummings et al. 2006, Clark et al. 2013, 2017, Gutt et al. 2019). Macroalgal cover has been shown to increase with less sea ice cover (Clark et al. 2013, Amsler et al. 2023), suggesting that future changes in ice regimes could significantly alter the presence and identity of dominant algal species. These changes could, in turn, reshape the associated faunal communities (Clark et al. 2013). The northern parts of the WAP – such as the sites included in this study – already experience less consistent and shorter sea ice cover compared to more southern regions (Stammerjohn et al. 2008, 2012). As sea-ice continues to decline, these northern locations may offer a preview of conditions likely to develop further south of the Peninsula (Clark et al. 2013, Amsler et al. 2023). The benthic community patterns we observe in this study could foreshadow broader shifts in community structure and habitat compositions as similar conditions expand southward. On the other hand, habitat diversity and microhabitat mosaics could be crucial for providing connectivity, redundancy and thus resilience under rapid environmental changes (O’Leary et al. 2017, Pearson et al. 2021). Because our study sites exhibit no direct human disturbance – due to CCAMLR’s restrictions on nearshore fishing and dredging (CEMP) and low tourism activity (IAATO reports) – they provide an ideal setting to monitor indirect effects of global environmental change. Studying microhabitat structure in such undisturbed systems offers a sensitive lens to identify subtle ecological shifts that may precede larger-scale changes. Conclusion Our study shows that the shallow benthic communities of the West Antarctic Peninsula cannot be considered uniform habitats but rather mosaics of microhabitats, each characterized by distinct macroalgal communities and associated faunal assemblages. This patchwork effect demonstrates the importance of scale, with connectedness and similarity within and between locations being different at different resolutions. Results show that habitat forming species, including macroalgae and sponges, were more important than physical environmental factors in determining microhabitat assemblages. Microhabitats were found to be distinct and could be location specific or shared across multiple locations. The number and types of microhabitats present in a given region drives the overall biodiversity and biogeographic signature of each location. 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Keywords antarctic peninsula benthos biodiversity microhabitats underwater imagery Authors Affiliations Lea Katz 0000-0001-5748-602X [email protected] Université Libre de Bruxelles View all articles by this author Emily Mitchell University of Cambridge View all articles by this author Bruno Danis Université Libre de Bruxelles View all articles by this author Huw Griffiths 0000-0003-1764-223X British Antarctic Survey View all articles by this author Metrics & Citations Metrics Article Usage 265 views 151 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lea Katz, Emily Mitchell, Bruno Danis, et al. Microhabitat patchiness structures benthic biodiversity in the Western Antarctic Peninsula. Authorea . 06 August 2025. DOI: https://doi.org/10.22541/au.175447826.65995956/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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