• Energy Research

Climate change will increasingly affect the natural habitat and diet of polar bears (Ursus maritimus). Understanding the energetic needs of polar bears is therefore important. We developed a theoretical method for estimating polar bear food consumption based on using the highly recalcitrant polychlorinated biphenyl (PCB) congener, 2,2',4,4',55-hexaCB (CB153) in bear adipose tissue as an indicator of food intake. By comparing the CB153 tissue concentrations in wild polar bears with estimates from a purposely designed individual-based model, we identified the possible combinations of field metabolic rates (FMR) and CB153 deposition efficiencies in East Greenland polar bears. Our simulations indicate that if 30% of the CB153 consumed by polar bear individuals were deposited into their adipose tissue, the corresponding FMR would be only two times the basal metabolic rate. In contrast, if the modelled CB153 deposition efficiency were 10%, adult polar bears would require six times more energy than that needed to cover basal metabolism. This is considerably higher than what has been assumed for polar bears in previous studies though it is similar to FMRs found in other marine mammals. An implication of this result is that even relatively small reductions in future feeding opportunities could impact the survival of East Greenland polar bears.

AbstractIn marine environments, noise from human activities is increasing dramatically, causing animals to alter their behavior and forage less efficiently. These alterations incur energetic costs that can result in reproductive failure and death and may ultimately influence population viability, yet the link between population dynamics and individual energetics is poorly understood. We present an energy budget model for simulating effects of acoustic disturbance on populations. It accounts for environmental variability and individual state, while incorporating realistic animal movements. Using harbor porpoises (Phocoena phocoena) as a case study, we evaluated population consequences of disturbance from seismic surveys and investigated underlying drivers of vulnerability. The framework reproduced empirical estimates of population structure and seasonal variations in energetics. The largest effects predicted for seismic surveys were in late summer and fall and were unrelated to local abundance, but instead were related to lactation costs, water temperature, and body fat. Our results demonstrate that consideration of temporal variation in individual energetics and their link to costs associated with disturbances is imperative when predicting disturbance impacts. These mechanisms are general to animal species, and the framework presented here can be used for gaining new insights into the spatiotemporal variability of animal movements and energetics that control population dynamics.

AbstractAimTheArctic has experienced marked climatic differences between glacial and interglacial periods and is now subject to a rapidly warming climate. Knowledge of the effects of historical processes on current patterns of diversity may aid predictions of the responses of vegetation to future climate change. We aim to test whether plant species and genetic diversity patterns are correlated with time since deglaciation at regional and local scales. We also investigate whether species richness is correlated with genetic diversity in vascular plants.LocationCircumarctic.MethodsWe investigated species richness of the vascular plant flora of 21 floristic provinces and examined local species richness in 6215 vegetation plots distributed across theArctic. We assessed levels of genetic diversity inferred from amplified fragment length polymorphism variation across populations of 23 commonArctic species. Correlations between diversity measures and landscape age (time since deglaciation) as well as variables characterizing current climate were analysed using spatially explicit simultaneous autoregressive models.ResultsRegional species richness of vascular plants and genetic diversity were correlated with each other, and both showed a positive relationship with landscape age. Plot species richness showed differing responses for vascular plants, bryophytes and lichens. At this finer scale, the richness of vascular plants was not significantly related to landscape age, which had a small effect size compared to the models of bryophyte and lichen richness.Main conclusionOur study suggests that imprints of past glaciations inArctic vegetation diversity patterns at the regional scale are still detectable today. SinceArctic vegetation is still limited by post‐glacial migration lag, it will most probably also exhibit lags in response to current and future climate change. Our results also suggest that local species richness at the plot scale is more determined by local habitat factors.

SummaryAgent‐based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use inABMs should be constructed.Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically.Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate.If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern‐oriented modelling, artificial evolution or from allometric equations.The development ofABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. SuchABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests.

# Drivers of plant community composition and species richness in Western Greenland Dataset DOI: [10.5061/dryad.8sf7m0d17](10.5061/dryad.8sf7m0d17) **Principal Investigator Contact Information** ``` Name: Jacob Nabe-Nielsen Institution: Aarhus University Email: jnn@ecos.au.dk ``` ## Description of the data and file structure This dataset contains the data required to replicate analyses in Nabe-Nielsen et al. (2025), studying the potential drivers of vegetation composition and species richness along coast to inland and altitudinal gradients by the Nuuk fjord in western Greenland using Hierarchical Modelling of Species Communities (HMSC) and linear mixed models. Data originate from five study sites along the Nuuk fjord in western Greenland. At each site three groups of six plots were selected for every 100 m increase in altitude. Plot centres were placed exactly 10 m apart along the isoclines, and plot groups were placed exactly 500 m apart. The permanently marked plots were circular, with a diameter of 2 m. For each plot a complete inventory of all species of vascular plants was conducted in the period 2011–2013. Positions of individual plots are provided in the UTM zone 22 N projection. **Nuuk_plant_data_231114.xlsx** (located in the "data" folder in the file "HMSC_models_and_data.zip") is an Excel-file with four sheets containing all the field data used in the publication. The sheet **Species data** contains a list of all plant species recorded in the 414 vegetation plots. Species: Scientific name of the studied plant species N plots: Number of plots where the species was found P001–P414: Presence of the species in each of the 414 study plots (presence recorded with an 'x') The sheet **Positions and inclin:** plot: Plot numbers (P001–P414), corresponding to the numbers on the metal tags used in the field site: Study site number (1–5) alt: Altitude of the plot (in m) grp.no: Name of the plot group number within the specific site and altitude band (a, b, or c) plot.grp: Name of the plot group (combination of site, altitude and grp.no) long.garmin: Longitude, measured using Garmin GPS (blank means missing) lat.garmin: Latitude, measured using Garmin GPS (blank means missing) x.garmin: East-west position, measured using Garmin GPS (UTM zone 22N; blank means missing) y.garmin: North-south position, measured using Garmin GPS (UTM zone 22N; blank means missing) x.trimble: East-west position, measured using Trimble differential GPS (UTM zone 22N; blank means missing) y.garmin: North-south position, measured using Trimble differential GPS (UTM zone 22N; blank = missing) inclin.down: Average plot steepness (degrees) inclin.dir: Direction of slope (i.e., direction downhill), in degrees soil.water.x: Soil water, with x taking the values 1-3 (pct. water; missing values shown as NA) date1 and date.2: the dates where the plot was visited / variables measured The sheet **Hgt and cover 2021-22**: plot: Plot numbers (P001-P414) site: Study site (1-5) Taxon: Name of the taxonomic group (or 'Rock') Max hit: Maximum height of the taxon observed within the 2-m plot (cm) Max dia: Maximum diameter (mm) of different taxa within the 2-m plots Cover: How much of the 2-m plot that was covered by the taxon (in percent; average of two estimates). ## Code/Software The file "HMSC_models_and_data.zip" is a compressed archive contaning the field data and the code required to run the HMSC analyses presented in the publication, along with directories required for handling input and output from the analyses. The archive contains the folders "data", "models", and "results", and the R code files "S1_define_and_fit_models.R", "S2_evaluate_convergence.R", "S3_show_parameter_estimates.R". The first file contains code for reading in and rearranging the species data stored in "data/Nuuk plant data 231114.xlsx". It also reads in the file "data/b for Hmsc 231114.RData" **b for Hmsc 231114.RData**: stores covariates used in the analysis (rearranged from data stored in the tabs "Species data" and "Hgt and inclination 2021-22" in the Excel data sheet, and summer temperatures and summer precipitation data from the Chelsa database version 2.1 (Karger et al. 2017)). One line per plot: plot.grp: Plot group number (see above) plot: Plot number (one line per plot, numbered P001 to P414) lon: Longitude of the plot lat: Latitude of the plot site: Study site (1-5) alt: Altitude (m) grp.no: Name of the plot group number within the specific site and altitude band (a, b, or c) date.1: Date of first visit to the plot; collection of vegetation composition data (yyyy-mm-dd) date.2: Date of second visit to the plot; collection of plant height and cover (yyyy-mm-dd) inclin.down: Plot steepness (degrees) inclin.dir.true: Direction of slope (degrees) soil.water.mean: Average of three soil water measures (percent soil water) Sri: Solar radiation index: Calculated from slope, direction and position (see paper) cover.p: Cover values from pin-point data; not used in the present data analyses (range 0–1) cover.c: Cover values estimated within 2-m circle (percent); used in the paper n.spp.per.plot: Number of species observed in the plot group (response variable used in the linear analyses) max.hgt: Maximum plant height (cm) summer.temp: Mean summer temperature (degrees Censius) summer.prec: Mean summer precipitation (mm) cont.idx: Continentality index; fraction of distance moved between outer coast and inland glacier **S1_define_and_fit_models.R**: Code used to define the spatial nesting used in the analysis, with plots nested within plot groups nested within sites, and specifies the number of replicates to use. Finally the models are fitted using the "sampleMcmc" function from the Hmsc package, and the model objects are stored in "models/unfitted_models.RData". **S2_evaluate_convergence.R:** code for plotting the fitted model objects to evaluate model convergence (outputs stored in the "results" directory). **S3_show_parameter_estimates.R:** code for producing the plots presented in the paper based on the fitted model objects. The **data** folder contains the file **unfitted_models.RData**; a list of model outputs used in code found in the files S2_evaluate_convergence.R and S3_show_parameter_estimates.R. The results folder contains a number of produced automatically when running the code in the files S2_evaluate_convergence.R and S3_show_parameter_estimates.R. The files are: Fig 4 - Hmsc parameter_estimates.csv, MCMC_convergence.pdf, MCMC_convergence.txt, parameter_estimates_Beta_plant model.xlsx, parameter_estimates_Omega_plant model_group.xlsx, parameter_estimates_Omega_plant model_plot.xlsx, parameter_estimates_Omega_plant model_site.xlsx, parameter_estimates_VP_plant model.csv, parameter_estimates_VP_R2T_Betaplant model.csv, parameter_estimates_VP_R2T_Yplant model.csv, parameter_estimates.pdf, parameter_estimates.txt, Prop raw var - VPr.csv. The values stored in the csv files and the plots in the pdf files make it possible to inspect how well the model fits and provides various summaries of the model outputs. Contents of all outputs are described in detail in the paper (Nabe-Nielsen et al. 2025) and the help files accompanying the Hmsc-package, which is available on CRAN ([https://cran.r-project.org](https://cran.r-project.org)). All statistical analyses were performed using R version 4.4.1 **Dates of Data Collection** ``` 2011-2013. ``` **Data Spatial Scope** The Nuuk fjord (Godthåbsfjorden) in western Greenland. **Sharing/Access** This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license. **Recommended Citation** Nabe-Nielsen, J., Nabe-Nielsen, L.I. & Ovaskainen, O. (2025). Drivers of plant community composition and species richness in Western Greenland. Ecography. The Arctic experiences rapid climate change, but our ability to predict how this will influence plant communities is hampered by a lack of data on the extent to which different species are associated with particular environmental conditions, how these conditions are interlinked, and how they will change in coming years. Increasing temperatures may negatively affect plants associated with cold areas due to increased competition with warm-adapted species, but less so if local temperature variability is larger than the expected increase. Here we studied the potential drivers of vegetation composition and species richness along coast to inland and altitudinal gradients by the Nuuk fjord in western Greenland using Hierarchical Modelling of Species Communities (HMSC) and linear mixed models. Community composition was more strongly associated with random variability at intermediate spatial scales (among plot groups 500 m apart) than with large-scale variability in summer temperature, altitude or soil moisture, and the variation in community composition along the fjord was small. Species richness was related to plant cover, altitude and slope steepness, which explained 42% of the variation, but not to summer temperature. Jointly, this suggests that the direct effect of climate change will be weak, and that many species are associated with microhabitat variability. However, species richness peaked at intermediate cover, suggesting that an increase in plant cover under warming climatic conditions may lead to decreasing plant diversity. The study was conducted at five different sites along the Nuuk fjord (Godthåbsfjorden) in western Greenland. In 2011–2013, 414 permanent plots were established across the five study sites. The plots were circular, with a diameter of 2 m. Three groups of six plots were selected for every 100 m increase in altitude. The position of the first plot at each altitude was selected by walking uphill straight towards a pre-selected point until the isocline was reached (measured using a hand-held GPS). Plot centres were placed exactly 10 m apart along the isoclines, or slightly more if needed to prevent plots from being entirely in water or having an average slope >45°. Plot groups were placed exactly 500 m apart. The lowest plots were moved to 20 m a.s.l. to avoid exposure to salt. The highest altitudes used in sites 1–5 were 200, 200, 400, 500 and 500 m a.s.l., respectively, reflecting that the mountains are higher further east. The study design was the exact same as previously used in Young Sund (Nabe-Nielsen et al. 2017). The vegetation survey included a complete inventory of all species of vascular plants in each plot. This inventory was conducted in 2011–2013. Maximum height and diameter were measured for the woody species in 2021–2022. The cover of woody species, graminoids, and herbaceous plants was assessed for each plot by two independent observers as percent cover <2 m from plot centres. Subsequently the two estimates were averaged. Plants were named following Böcher et al.(1978).

Abstract Climate change is modifying the structure of marine ecosystems, including that of fish communities. Alterations in abiotic and biotic conditions can decrease fish size and change community spatial arrangement, ultimately impacting predator species which rely on these communities. To conserve predators and understand the drivers of observed changes in their population dynamics, we must advance our understanding of how shifting environmental conditions can impact populations by limiting food available to individuals. To investigate the impacts of changing fish size and spatial aggregation on a top predator population, we applied an existing agent‐based model parameterized for harbour porpoises Phocoena phocoena which represents animal energetics and movements in high detail. We used this framework to quantify the impacts of shifting prey size and spatial aggregation on porpoise movement, space use, energetics and population dynamics. Simulated individuals were more likely to switch from area‐restricted search to transit behaviour with increasing prey size, particularly when starving, due to elevated resource competition. In simulations with highly aggregated prey, higher prey encounter rates counteracted resource competition, resulting in no impacts of prey spatial aggregation on movement behaviour. Reduced energy intake with decreasing prey size and aggregation level caused population decline, with a 15% decrease in fish length resulting in total population collapse Increasing prey consumption rates by 42.8 ± 4.5% could offset population declines; however, this increase was 21.3 ± 12.7% higher than needed to account for changes in total energy availability alone. This suggests that animals in realistic seascapes require additional energy to locate smaller prey which should be considered when assessing the impacts of decreased energy availability. Changes in prey size and aggregation influenced movements and population dynamics of simulated harbour porpoises, revealing that climate‐induced changes in prey structure, not only prey abundance, may threaten predator populations. We demonstrate how a population model with realistic animal movements and process‐based energetics can be used to investigate population consequences of shifting food availability, such as those mediated by climate change, and provide a mechanistic explanation for how changes in prey structure can impact energetics, behaviour and ultimately viability of predator populations.

We investigated the disturbance effects of offshore windfarm construction on harbour porpoises Phocoena phocoena using acoustic porpoise monitoring data and noise measurements during construction of the first 7 large-scale offshore wind farms in the German Bight between 2010 and 2013. At 6 wind farms, active noise mitigation systems (NMS) were applied during most piling events, and 1 was constructed without. Based on generalized additive modelling analyses, we describe a clear gradient in the decline of porpoise detections after piling, depending on noise level and distance to piling. Declines were found at sound levels exceeding 143 dB re 1 µPa 2s (the sound exposure level exceeded during 5% of piling time, SEL 05) and up to 17 km from piling. When only considering piling events with NMS, the maximum effect distance was 14 km. Compared to 24−48 h before piling, porpoise detections declined more strongly during unmitigated piling events at all distances: at 10−15 km declines were around 50% during piling without NMS, but only 17% when NMS were applied. Within the vicinity (up to about 2 km) of the construction site, porpoise detections declined several hours before the start of piling and were reduced for about 1−2 d after piling, while at the maximum effect distance, avoidance was only found during the hours of piling. The application of first generation NMS thus reduced the effect range of pile driving and led to a lower decline of porpoise detections over all distances. However, NMS were still under development and did not always work with equal efficiency. As NMS have further developed since, future investigations are expected to show additional reduction of disturbance effects.

AbstractArctic plant communities are altered by climate changes. The magnitude of these alterations depends on whether species distributions are determined by macroclimatic conditions, by factors related to local topography, or by biotic interactions. Our current understanding of the relative importance of these conditions is limited due to the scarcity of studies, especially in the High Arctic. We investigated variations in vascular plant community composition and species richness based on 288 plots distributed on three sites along a coast‐inland gradient in Northeast Greenland using a stratified random design. We used an information theoretic approach to determine whether variations in species richness were best explained by macroclimate, by factors related to local topography (including soil water) or by plant‐plant interactions. Latent variable models were used to explain patterns in plant community composition. Species richness was mainly determined by variations in soil water content, which explained 35% of the variation, and to a minor degree by other variables related to topography. Species richness was not directly related to macroclimate. Latent variable models showed that 23.0% of the variation in community composition was explained by variables related to topography, while distance to the inland ice explained an additional 6.4 %. This indicates that some species are associated with environmental conditions found in only some parts of the coast–inland gradient. Inclusion of macroclimatic variation increased the model's explanatory power by 4.2%. Our results suggest that the main impact of climate changes in the High Arctic will be mediated by their influence on local soil water conditions. Increasing temperatures are likely to cause higher evaporation rates and alter the distribution of late‐melting snow patches. This will have little impact on landscape‐scale diversity if plants are able to redistribute locally to remain in areas with sufficient soil water.