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Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

Herbivory assessments were made at the plant community and species levels. We focused on three plant species with a widespread occurrence across the temperature gradient: cuckooflower (Cardamine pratensis, Linnaeus), common mouse-ear (Cerastium fontanum, Baumgerten), and marsh violet (Viola palustris, Linnaeus). For assessments of invertebrate herbivory at the species level, thirty individuals per species of C. pratensis, C. fontanum, and V. palustris were marked in each of ten plots, using a stratified random sampling method where individuals were randomly selected, but the full range of within-plot soil temperatures was represented. For assessments of invertebrate herbivory at the community level, five 50 × 50 cm quadrats were marked at random points in eight of the plots that best captured the full temperature gradient. The community-level herbivory assessment was conducted on 19th June. The number of damaged plants was recorded out of 100 random individuals, selected using a 10 × 10 grid within each 50 × 50 cm quadrat. For the species-level herbivory assessment, individual marked plants were surveyed for signs of invertebrate herbivory every two weeks from 30th May to 2nd July, generating three time-points per species. At each survey, all marked individuals for each species were assessed within a 48-hour period. Plants were recorded as damaged or not damaged by invertebrate herbivores at each time-point. Further details of how phenological stage of development, vegetation community composition, soil temperature, moisture, pH, nitrate, ammonium, and phosphate were recorded are provided in the supporting documentation. This is a dataset of environmental data, vegetation cover, and community- and species-level invertebrate herbivory, sampled at 14 experimental soil plots in the Hengill geothermal valley, Iceland, from May to July 2017. The plots span a temperature gradient of 5-35 °C on average over the sampling period, yet they occur within 1 km of each other and have similar soil moisture, pH, nitrate, ammonium, and phosphate.

This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in the conterminous United States of America** 2\. Author Information A. First Author Contact Information Name: Adriana Parra Institution: University of Nevada, Reno Address: Reno, NV USA Email: adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018 4\. Geographic location of dataset: Conterminous United States 5\. Description: This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Additionally, the dataset includes a legend file for the identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: **Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)** 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was data derived from another source? Yes A. If yes, list source(s): "Vegetative Lifeform Cover from Landsat SR for CONUS" product publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) TerraClimate data catalog publicly available at the website https://www.climatologylab.org/terraclimate.html 5\. Recommended citation for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ## DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one csv file. There are 4 geotiff files per each of the lifeform classes evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The files corresponding to each lifeform class are indicated by the first two letters in the file name, HC indicates herbaceous cover, TC indicates tree cover, SC indicates shrub cover, and NC indicates non-vegetation cover. 1\. File List: a) Observed change: Trends of vegetation change between 1986 and 2018. b) Potential predict: Predicted rates of vegetation change form the climate limiting factor analysis. c) Potential observed difference: Difference between the potential and the observed vegetation rates of change. d) Limiting variable: Climate variable identified as the limiting factor for each pixel the conterminous United States. e) Legend of the Limiting variable raster All the geotiff files are stored as Float 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The csv file included in the dataset is the legend for the limiting variable geotiff files. This file includes the name of the climate variable corresponding to each number in the limiting variable files, as well as information on the variable type and the corresponding time lag. 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA Input data We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF). Preprocessing of vegetation fractional cover data We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992). Preprocessing of climate variables data To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018. The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags. Estimation of climate potential We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables). We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable. To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate. Identifying climate-limited areas We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class. In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor.

# Data on: Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China [https://doi.org/10.5061/dryad.vdncjsz1z](https://doi.org/10.5061/dryad.vdncjsz1z) This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. ## Description of the data and file structure The predicted annual incidence of national SFTS cases with or without human population reduction under four RCPs under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The value represents the annual incidence, and the unit is 105/year. The Dataset-1 file includes the predicted annual incidence of national SFTS cases with a fixed future human population under different climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in the 2030s, 2050s, and 2080s. The Dataset-2 file includes the predicted annual incidence of national SFTS cases in the 2030s, 2050s, and 2080s with human population reduction (SSP2) under four RCPs. ## Sharing/Access information Data was derived from the following sources: * https://doi.org/10.1111/gcb.16969 This dataset is the data used in the paper of Global change biology entitled "Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China". We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in the mainland of China. The SFTS incidence in three time periods (2030-2039, 2050-2059, 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. The projected spatiotemporal dynamics of SFTS will be heterogeneous across provinces. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.  See the Materials and methods section in the original paper. The code used in the statistical analyses are present in the paper and/or the Supplementary Materials.

Variable list for files: SW wind - Section table on Skomer (Standardised).csv / NW wind - Section table on Skomer (Standardised).csv / SE wind - Section table on Skomer (Standardised).csv /NE wind - Section table on Skomer (Standardised).csv and SW wind - Sections on Skokholm (Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanUMedian; MeanUIQR, MeanUSkewness, MeanUCV: Median, interquartile range,skewness and coefficient of variation of mean wind speed per section HorizontalMedian;HorizontalIQR,HorizontalSkewness,HorizontalCV: Median, interquartile range,skewness and coefficient of variation of horizontal wind speed per section PMedian;PIQR,PSkewness,PCV: Median, interquartile range,skewness and coefficient of variation of preessure per section TKEMedian;TKEIQR,TKESkewness,TKECV: Median, interquartile range,skewness and coefficient of variation of turbulent kinetic energy per section TIMedian;TIIQR,TISkewness,TICV: Median, interquartile range,skewness and coefficient of variation of turbulence intensity per section U_2Median;lU_2IQR;U_2Skewness;U_2CV: Median, interquartile range,skewness and coefficient of variation of vertical wind speed per section EpsilonMedian;EpsilonIQR,EpsilonSkewness,EpsilonCV: Median, interquartile range,skewness and coefficient of variation of turbulent dissipation rate per section NutMedian;NutIQR,NutSkewness,NutCV: Median, interquartile range,skewness and coefficient of variation of kinematic viscosity per section GustsMedian;GustsIQR,GustsSkewness,GustsCV: Median, interquartile range,skewness and coefficient of variation of instataneous gusts per section MeanSectorSlope: Mean slope per section ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: Section table on Skomer - with Mean cliff orientation and Slope (NOT-Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) X_Centre: X coordinate of the central point of each section Y_Centre: Y coordinate of the central point of each section Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section ApsectClass: Factor indicating whether the mean cliff orientation is lee- or windward to the SW wind ColPresence: Binomial variable, indicating whether a section has birds or not. This variable varies with classification, based on either the count of birds or the density per section Variable list for file: SW wind - Sections on Skokholm to predict colonies using cliff orientation and slope model from Skomer (NON - Standardised).csv FID: Row ID (for use in ArcGIs) Count: Number of guillemots per section Area: Total area of each section () Density: Density of guillemots per section (number of birds/ Area) Sector: Section ID MeanSectorSlope: Mean slope per section MeanSectorAspectCircular: Mean cliff orientation per section Wind is fundamentally related to shelter and flight performance: two factors that are critical for birds at their nest sites. Despite this, airflows have never been fully integrated into models of breeding habitat selection, even for well-studied seabirds. Here we use computational fluid dynamics to provide the first assessment of whether flow characteristics (including wind speed and turbulence) predict the distribution of seabird colonies, taking common guillemots (Uria aalge) breeding on Skomer island as our study system. This demonstrates that occupancy is driven by the need to shelter from both wind and rain/ wave action, rather than airflow characteristics alone. Models of airflows and cliff orientation both performed well in predicting high quality habitat in our study site, identifying 80% of colonies and 93% of avoided sites, as well as 73% of the largest colonies on a neighbouring island. This suggests generality in the mechanisms driving breeding distributions, and provides an approach for identifying habitat for seabird reintroductions considering current and projected wind speeds and directions. Methods detailed in manuscript: https://doi.org/10.1111/ecog.05733.

Flight trajectories of fruit flies and mosquitoes in a wind tunnel.This data file is a MySQL database file which must be uploaded to a MySQL database management system (DBMS) (e.g., via the MAMP installation: http://localhost:8888/MAMP/?language=English, as was used in the associated manuscript). Once you have installed a MySQL DBMS on your machine, make a new database called “wind_tunnel_db”. To populate this database using the data file, first download all of the data files and join them together using: cat wind_tunnel_db_* > wind_tunnel_db.sql Then run the following command to populate the wind_tunnel_db MySQL database with the result. /path/to/mysql -uroot -proot wind_tunnel_db < /path/to/wind_tunnel_db.sql replacing the paths and username/passwords as appropriate. It will take several minutes since it is a large file. The database contains several tables, which are mostly self explanatory. The key tables of interest are the “experiment” table, which lists the 4 experiments contained in this data set, the “timepoint” table, which contains the position, velocity, etc., of every fly/mosquito at every measured time point, and the “trajectory” table, which indicates which set of time points correspond to which individual trajectories. Other useful tables that have been pre-populated are the “crossing” table, which specifies trajectory segments corresponding to each plume crossing, and the “crossing_group” table, which groups sets of crossings together according to experiment and crossing identification criteria. The code that interacts with this database and recreates the figures in the associated manuscript is contained at https://github.com/rkp8000/wind_tunnel.wind_tunnel_db_aaPart 2wind_tunnel_db_abPart 3wind_tunnel_db_acPart 4wind_tunnel_db_adPart 5wind_tunnel_db_aePart 6wind_tunnel_db_afPart 7wind_tunnel_db_agPart 8wind_tunnel_db_ahPart 9wind_tunnel_db_aiInfotaxis databaseBase database for running infotaxis simulations. To see how to prepare and populate this database with simulated trajectory data, see the file _paper_auxiliary_code in the GitHub repository http://github.com/rkp8000/wind_tunnel.infotaxis_db.sql Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, “infotaxis”, in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking.

We monitored golden-cheeked warbler territories in 10 plots within an urban preserve to determine abundance, delineate territories, and document breeding success. We determined environmental conditions across the study period to examine temporal and landscape effects. We then used these data to estimate adult survival and productivity and relate these vital rates to environmental conditions experienced during our study period. We used supported covariates to predict potential effects on this population 25 years into the future. These data and code are associated with the publication in Ecosphere entitled "Urban land cover and El Nino events negatively impact population viability of an endangered North American songbird." We performed an integrated population model to evaluate the effect of climate patterns and urban land cover on the viability of an endangered wood-warbler breeding in central Texas. We used territory monitroing data from 2011–2019 to predict viability of the population 25 years into the future. We assembled and conducted the analysis in R.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MOHC.HadGEM3-GC31-MM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The HadGEM3-GC3.1-N216ORCA025 climate model, released in 2016, includes the following components: aerosol: UKCA-GLOMAP-mode, atmos: MetUM-HadGEM3-GA7.1 (N216; 432 x 324 longitude/latitude; 85 levels; top level 85 km), land: JULES-HadGEM3-GL7.1, ocean: NEMO-HadGEM3-GO6.0 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude; 75 levels; top grid cell 0-1 m), seaIce: CICE-HadGEM3-GSI8 (eORCA025 tripolar primarily 0.25 deg; 1440 x 1205 longitude/latitude). The model was run by the Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK (MOHC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.