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For sustainable use of marine-animal resources, preservation of endangered species, and conservation of ecosystems, it is very important to understand the biology of individual marine animal. From the viewpoints of physiology, ethology, and environmentology, marine animals have been studied by bioelectric measurement1,2,3,4, bio-logging5,6,7,8,9, and DNA (genome) analysis10,11,12,13,14,15, respectively. Recent technological innovations helped studies on bio-logging and DNA analysis advance rapidly, but advancement of bioelectric-measurement technology, which has existed for a long time, lags behind those of bio-logging and DNA analysis.
Now, aiming to obtain good harvests, the aquaculture industry requires bioelectric measurements to grasp the health condition of marine animals from pathophysiological viewpoints. Moreover, the electrocardiogram (ECG), which is a kind of bioelectric measurement, carries high expectations because it can evaluate psychological stress of marine animals just as it can evaluate that of humans16,17,18,19. Moreover, ECG can be used in fish ethological- and physiological studies2,4, so innovating techniques and devices for ECG measurement will contribute to developing these studies.
In regards to bioelectric measurement targeting marine animals, to prevent electric short-circuiting between the pair of bioelectrodes via seawater (which is conductive), one or multiple pairs of bioelectrodes are embedded inside the living body by incision surgery20,21, which can impose a heavy workload on inexperienced experimenters. Moreover, the animal can often become agitated without anesthesia and consume much physical energy when the electrodes are implanted into its body. To reduce these burdens, we propose a novel method of measuring bioelectric signals—which utilizes the conductivity of seawater surrounding the animal—by using only one bioelectrode attached at each measurement point (in contrast to the conventional method, which requires a pair of bioelectrodes). To the best of our knowledge, a similar method has not been reported.
In this paper, the proposed method of bioelectric measurement for marine animals under the seawater is first overviewed. Next, the bioelectric measurement system for the chosen experimental subjects, namely, fish, is described, and the availability of the proposed method is verified. Then, the experimental procedures and results of bioelectric measurements are presented. Finally, possible applications of the proposed method are discussed. More

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The decision by an individual to move from one area to another is mediated by a number of factors, such as resource quality and availability, predation risk and local environmental conditions, all of which will influence its survival and reproductive output1,4. The challenge for conservationists is understanding how these individual decisions can affect population dynamics, home ranges and ultimately species’ survival1.
Home ranges of carnivores should overlap and in some cases envelop those of their prey species. Southern Ground-hornbills feed on a variety of prey, ranging from snakes, rabbits and birds to invertebrates12,18. Through tracking Southern Ground-hornbill movements, we were able to show that group home ranges during the early and late dry seasons were larger than in the wet season. As the Southern Ground-hornbill breeding season in South Africa coincides with the warm, wet summer months, prey availability, especially that of invertebrates, is expected to be higher20,21, suggesting that individuals would not need to travel as extensively to find sufficient food. Furthermore, in the late dry season, groups used between 76 and 115% of their home ranges. This was likely a result of having to increase their search for food and relaxation of the central place foraging required around the nest during the breeding season.
Previous research on Southern Ground-hornbill home ranges has recorded group densities ranging from one group per 4000 ha (communal areas in Zimbabwe22), to one group every 10,000 ha (KNP14), with one group in the Limpopo Valley having a home range close to 20,000 ha21. These results were obtained by direct observations of active nest sites or using VHF radio transmitters. In our study using GPS data, we showed that home range sizes of Southern Ground-hornbills within KNP vary considerably. Despite this, our results confirmed the findings of Theron et al.21 and Zoghby et al.20, demonstrating a restricted and contracted home range during the breeding season, when group movements are concentrated around the nest site (central place foraging). Presumably, breeding success would influence the extent of wet seasonal home range for Southern Ground-hornbills, with groups abandoning their central place foraging behaviour when nests fail. Wyness19 reported that of four Southern Ground-hornbill groups studied in the Association of Private Nature Reserves (APNR) adjacent to the KNP, the three that bred successfully in the year of their study showed a breeding season range reduction to between 24–36% of their non-breeding home range. The unsuccessful group used 70% of their home range during this time19. Surprisingly, the groups within the KNP did not show such a definitive pattern in home range size reduction associated with breeding success, although all groups that attempted breeding did show a wet seasonal home range reduction. Of the six Southern Ground-hornbill groups monitored in our study, four groups bred successfully, one group’s attempt failed (Ngotso Camp), and the breeding status for the third group (Shingwedzi) was unknown. The groups that bred successfully used 21–97% of their respective home ranges, with the unsuccessful group using 85% of their home range (See Table 1).
Southern Ground-hornbills are known to favour more open habitats for foraging20,23. Our results supported this, with groups selecting the open woodland and grassland habitat types year-round, following their availability within the landscape.
Although Southern Ground-hornbill seasonal territory size differed significantly amongst the groups, they all showed a decrease in the amount of low shrubland and an increase in the amount of grassland habitat used with increased territory size. Similarly, as seasonal territory sizes increased, the amount of low-medium woody cover (25–50%) decreased. Thus, when selecting an area for a reintroduction of Southern Ground-hornbill groups, the ratio of low-medium woody cover (low shrubland) to grassland, calculated based on the national land cover datasets available, should be taken into account, as this will likely influence the home range size and the number of groups that could be supported in an area.
Although an understanding of the changes and restrictions in territory size is important for the management of a species, the types of movements adopted within a population will influence the management actions needed for their conservation, such as ensuring connectivity or access to certain resources1. Conservation policy and management actions are less effective when interventions do not integrate both the spatial and temporal changes in habitat use and the scale of species movements1,3. The results from the first-passage time analysis of Southern Ground-hornbill movements showed that the different groups did not consistently demonstrate seasonal patterns in the scale at which they concentrated their foraging efforts. The mean distances travelled for all trajectory paths, classified as active foraging behaviour, were similar and lower in the late wet and early dry seasons compared with the late dry and early wet seasons. Movement between foraging resource patches or mean relocation distances were highest in the wet season months, with the maximum mean distances travelled during the early wet season and the start of the breeding period. Overall prey abundance for Southern Ground-hornbills is generally higher in the wetter months, resulting in a decrease in relocation distances. Our results support the theory that Southern Ground-hornbill wet season movements are most likely influenced by the need to travel to and from the nest site to provision prey to the incubating female and growing nestling. Once resources closer to the nest are depleted, the distances travelled to access additional habitats and prey would likely increase.
Southern Ground-hornbills seemingly prefer nest sites surrounded by more open woodland habitat24,25. Habitat structure and the diversity of habitat types within a 3 km radius around the nest site positively influenced Southern Ground-hornbill nesting success. An increase in the density of woody habitat surrounding the nest site, however, had a negative impact on Southern Ground-hornbill breeding success24, possibly owing to decreased foraging opportunities, an increased risk of predation or an increase in foraging effort beyond a value which is beneficial.
Habitat structure will likely promote or inhibit the types of movement that can occur in an area. The results from the multinomial regression (Table 5) indicate that the likelihood of a movement behaviour being classified as “foraging” within the open woodland, grassland and dense thicket habitat types was higher than the behaviour being attributed to “relocating”. This is to be expected for open woodland, and grassland habitats as these are both ideal open foraging habitats for Southern Ground-hornbills20,23 and are used year-round in proportion to their availability. Southern Ground-hornbills spend around 70% of their day walking12 and have been shown to travel distances of up to 10.6 km in a day20. Having to navigate through dense thicket vegetation in an area may increase the amount of time spent there, possibly accounting for why this habitat type is predicted to be used more for “foraging”-type behaviour as opposed to “relocating” behaviour. Travel through areas of low shrubland habitat was considered “relocating” behaviour, suggesting that within this habitat type, it is more profitable for Southern Ground-hornbills to move further, and the corresponding chance of finding food greater, than conducting area-restricted searches and spending longer periods concentrated in one patch.
When comparing movements between habitats allocated to “resting” as opposed to “foraging”, the time spent in all habitats was most likely as a result of “foraging”. As GPS locations were only recorded during the day, switching off at dusk (~ 18h00) when Southern Ground-hornbills would roost for the night, habitat preferences for “resting” movements may not have been recorded. Moreover, during the day, Southern Ground-hornbills may not be actively selecting for specific habitat types in which to roost or rest. They may simply be roosting or resting at a chosen site to escape the midday heat within the habitat type in which they were “foraging” or “relocating”.
We were unable to explore differences in movement relating to specific characteristics of the tagged bird (age, sex, helper versus breeder status, etc.) in our study. However, future research should consider study designs able to account for these potential differences, as García-Jiménez et al.26 showed that both the breeding season and sex of the individual influence displacement and distance travelled in Pyrenean Bearded Vultures (Gypaetus barbatus). They found that all individuals travelled more in the breeding season, with females having greater cumulative and maximum distances regardless of the season. More

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Study region
The Barrow Peninsula (~1800 km2) is situated on the northern limit of the Arctic Coastal Plain (Fig. 1). The mean annual temperature, precipitation, and snowfall are −11.2 °C, 115 mm, and 958 mm, respectively (1981–2010)35 and the maximum thaw depth ranges from 30 to 90 cm36,37. This continuous permafrost region is characterized by meso-scale (tens to hundreds of square kilometers) drained thaw lake basins (DTLBs) and interstitial tundra9,38, which are composed of a mosaic of fine-scale polygonal tundra landforms (tens to hundreds of square meters). Excluding lakes and rivers, the dominant polygonal tundra landforms in this region includes low-center (LC) polygon, flat-center (FC) polygon, high-center (HC) polygon, coalescent LC polygon, drained slopes (DS), nonpatterned DTLB (nDTLB), and thermokarst ponds, which cover an estimated 34, 24, 16, 11, 11, 3, and 1% of the land surface area, respectively9,13. Due to the similarity in morphological and physiological characteristics of coalescent LC polygons and thermokarst ponds, they are rarely differentiated in field observations. Therefore, both these landforms are combined and referred to as Ponds in the proceeding analysis. Though multiple vegetation communities may be found on each tundra landform, communities typically assemble along a soil moisture gradient representative of each landform21. These community–landform associations are identified as follows: dry Salix heath–DS, dry Luzula heath–HC, moist–wet Carex–Oncophorus meadow–FC, moist–wet Carex–Eriophorum meadow–LC, wet Dupontia meadow–nDTLB, and wet Arctophila pond margin–Pond21.
Model parameterization and validation
We synthesized an extensive collection of field data measured on the Barrow Peninsula to parameterize and validate DOS-TEM (Supplementary Table 1 and Fig. 3). The majority of this data was acquired by scientific initiatives: (1) International Biological Research Program during the early 1970s21,38,39,40, (2) Next Generation Ecosystem Experiments between 2010 and 201641,42,43,44,45,46,47, and (3) Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) during 2011–201548. In addition, we leveraged key ancillary datasets including: soil carbon pedons (i.e., 100 cm soil cores)22,23,24,30,38,39,49,50,51, vegetation carbon and nitrogen21,38,39,40,52, eddy covariance measurements48, and polygonal tundra landform maps9,13.
Fig. 3: Validation of modeled carbon fluxes and carbon pools.

Monthly net ecosystem exchange (NEE) fluxes measured by the CARVE eddy covariance tower (71°19’22.72”N, 156°35’47.74”W, a) during 2011–2015, were compared with NEE fluxes simulated with DOS-TEM (dashed line in b). Negative NEE indicates carbon uptake, while positive NEE indicates loss. Footprint % indicates the accumulated percentage of measured NEE used to compare with modeled NEE, weighted by polygonal landform (DS drained slope, HC high center, FC flat center, LC low center, nDTLB nonpatterned drained thaw lake basins) using the Kormann and Meixner93 flux footprint model (e.g., a). Modeled carbon pools (colored circles; c) were compared to 44 pedons collected (solid gray circles with standard error bars) and validated against 11 independent random subset of soil carbon pedons (open circles) measured on each respective landform across the Barrow Peninsula.

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Modeled carbon fluxes were compared to net ecosystem exchange (NEE) measurements from the CARVE tower near Utqiaġvik (71°19′22.72′N, 156°35′47.74′W). The tower footprint (~250 m radius) was located in a heterogeneous tundra site composed of all dominant polygonal tundra landforms (exception of Ponds). Although we identified good correspondence with modeled and measured NEE for most of our observations, DOS-TEM underestimated respiratory losses during the zero-curtain seasonal freeze and thaw isothermal period (e.g., September and October)53, resulting in an underestimate of the 1 to 1 line (R2 = 0.46, p 4 km2), while random error became increasingly positive with scale, increasing by 1.4% for every 1 km2 coarsening of spatial scale. Random errors ranged from 3.9 to 22.8% associated with fine to coarse-scale representation of tundra landforms.
Fig. 6: Misrepresentation of polygonal tundra landforms with scale.

Differences in landform distribution across spatial scales are relative to the highest resolution (0.0009 km2; pie chart). Data are representative of mean landform distributions across six subregions on the Barrow Peninsula. Negative and positive values indicate an overestimate and underestimate of polygonal tundra landforms, respectively.

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Both the bias error and random error were significantly minimized at fine scales (Fig. 5a), as twenty-first century soil carbon was only overestimated by a maximum of 3.7 and ±7.4%, respectively. This is in contrast to coarser spatial scales as bias and random error sharply increased at 8, 16, and 25 km2 by −6.1% (±10.7%), −17.0% (±22.1%), and −12.6% (±35.5%), respectively (Fig. 5a). The increase in spatial scale led to the overestimation in the area of low productivity thermokarst lakes (1.1% for every 1 km2) and underestimated wet productive landforms such as Ponds (−0.5% for every 1 km2) and LC polygons (−0.5% for every 1 km2; Fig. 6). This underestimation of wet landforms was particularly concerning as wet landforms have been regionally identified as those most sensitive to change59,60,61, while representing a significant proportion of the regional carbon cycle9,60,62,63.
Influence of tundra heterogeneity and model spatial scale
To evaluate the causes, consequences, and mitigation strategies for twenty-first century errors of prediction (i.e., bias and random error), we examined the combined influence of both tundra heterogeneity and model spatial scale. Correlation matrices clarified the potential causes of variable prediction errors, while hierarchical cluster analysis implemented using Euclidean distance and McQuitty linkage methods were used for grouping tundra heterogeneity and model spatial scales with similar errors of prediction to identify potential mitigation strategies or recommendations for future modeling applications.
Correlation matrices supported our presumption that an overestimation of lakes and underestimation of productive wet landforms altered the quantification of landscape-level soil carbon stocks, as bias error was strongly negatively correlated with lake cover (r = −0.98) and positively correlated with wet landforms (r = 0.94; Fig. 7). We found an inverse correlation in bias error as the prevalence of lake cover increased with spatial scale at the expense of nearly all other landforms, but in particular the landforms in low abundance such as tundra ponds (Figs. 6 and 7). Similar to the identified influence of spatial scale on random error (Fig. 4), correlations were highly positively related with model spatial scale (r = 0.99; Fig. 7), reinforcing the impact of coarsening model scale on uncertainty propagation.
Fig. 7: Pearson’s correlations of uncertainty metrics (bias and random error) and spatial attributes.

The larger the bubble the greater the p value. Landform categories dry and wet include spatial data from “DS + HC” and “FC + LC + nDTLB+Pond”, respectively. See Supplementary Fig. 3 for correlation bubble plots of all clusters.

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Overall, bias error was linked with the misrepresentation of tundra landforms as spatial scale increased (Fig. 7 and Supplementary Fig. 4). Therefore, we next elucidated the influence of heterogeneity and scale on random error. Though random error was correlated with spatial scale, we explored the variability across tundra heterogeneity and scale. The lowest and highest random errors occurred at the finest (≤4 km2) and coarsest (≥16 km2) spatial scales, respectively (Fig. 8). Landform clusters include one or more landforms and landform groups needed to represent tundra heterogeneity on the Barrow Peninsula. Random error was constrained to ±4.5% by considering 5 or 6 tundra landform groups at fine scales. However, at coarse scales these heterogeneous groups also showcased the greatest errors (±28.9%) due to the high number of landforms parameterized within increasingly uncertain landform distributions as scale increased (Figs. 5 and 8). The lowest error among clusters was identified in landform cluster 2 (i.e., ±3.4%; dry and wet), likely due to biogeophysical similarities (i.e., soil anaerobicity, soil available nitrogen, productivity gradients) between dry versus wet landforms (Supplementary Table 1) and similar responses to climate change (e.g., Fig. 4a). Interestingly, even at coarse scales the error found in cluster 2 remained lower than all other landform clusters. Although the “tundra-biome” cluster 1 had a relatively low random error across spatial scales (Fig. 8), this result would not be directly transferable to other modeling applications as we leveraged (i) a robust dataset for model parameterization and (ii) high-resolution polygonal tundra landform maps, currently unavailable across the Arctic for initializing and weighting model parameterization data. The importance of our data assimilation and landform weighting protocol was confirmed by testing the performance of a single unweighted landform parameterization (i.e., HC polygon) extrapolated across the Barrow Peninsula. We found random error to double (±15%) that of cluster 1 at fine scales (≤4 km2) and nearly triple (±45%) at coarse scales ( >8 km2). Therefore, to best simulate dynamically changing carbon pools in permafrost soils, our analysis recommends a minimum of two landform groups (i.e., dry and wet) at a maximum model spatial scale of ≤4 km2 (Fig. 8).
Fig. 8: Heat-map of random error for all tundra heterogeneity and model spatial scales.

Warm to cool colors represent high to low random error (transformed to improve visualization). Hierarchical clustering grouped random error for all landform clusters (i.e., landforms and groups to represent the heterogeneity on the Barrow Peninsula) using a ~50% similarity cut-off for group membership. Mean random errors (transparent white circles) are presented for each landform cluster and model spatial scale.

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Implications for modeling soil carbon dynamics in Arctic tundra
Current uncertainties among Pan-Arctic model projections reflect inadequate spatial and temporal data needed to initialize, parameterize, and validate key Arctic ecosystem processes55,56,64. This study overcame many of these limitations by leveraging a legacy of data (1973–2016) collected from the data-rich Barrow Peninsula to constrain parameter, climate, and model uncertainties, to improve the representation of Arctic tundra heterogeneity across model spatial scales. We identify a scale-dependent balance between tundra heterogeneity and model spatial scale, linked with the decoupling of actual and simulated tundra landform distributions as spatial scales increased (Figs. 5 and 6). The scale-dependency of model process representation is supported by ground-based assessments, as the drivers of carbon dynamics vary across local (e.g., drainage conditions affecting aerobic/anaerobic processes), regional (e.g., vegetation distribution), and landscape scales (e.g., climate variability). Though we identified relatively minimal differences in carbon accumulation rates between polygonal tundra landforms, this was not necessarily surprising as Arctic coastal tundra landforms are relatively young ( More

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