Sage-Grouse habitat areas divided into proposed management categories within Nevada and California project study boundaries.MANAGEMENT CATEGORY DETERMINATION The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was determined from a statewide resource selection function model and first categorized into 4 classes: high, moderate, low, and non-habitat. The standard deviations (SD) from a normal distribution of RSF values created from a set of validation points (10% of the entire telemetry dataset) were used to categorize habitat ‘quality’ classes. High quality habitat comprised pixels with RSF values < 0.5 SD, Moderate > 0.5 and < 1.0 SD, Low < 1.0 and > 1.5, Non-Habitat > 1.5 SD. Proposed Habitat Management Categories were then defined and calculated as follows.1) Core habitat: Defined as the intersection between all suitable habitat (high, moderate, and low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality falling outside the 85% SUI and all non-habitat falling within the 85% SUI. 3) General habitat: Defined as moderate and low quality habitat falling outside the 85% SUI. 4) Non habitat. Defined as non-habitat falling outside the 85% SUI. SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Presence and activity space data for Pristis pectinata monitored in the Caloosahatchee River from 2005 to 2007. Transmitter numbers with identical numbered superscripts indicate individuals that were recaptured and fitted with an additional transmitter at a later date. Size, detection and activity space data reflect the two periods of monitoring for these individuals. STL, stretch total length; tdet, number of days detected; tmax, number of days from first to last detection; tcon, maximum number of consecutive days present; RI, residence index; ASd, mean daily activity space; ASw, mean weekly activity space; ASm, mean monthly activity space. (DOC)

We used Maxent to model the distribution of Cypripedium acaule in North Carolina using records from 1) publicly available databases (GBIF and iNaturalist) and 2) herbaria. We compared distribution models made with the different sets of occurrence records to evaluate how spatial and temporal biases in records affect model results.

The data provided here include the original iNaturalist dataset (prior to cleaning as described in our methods) and the code for the evaluation of models based on ground-truthed populations. We cannot provide the original herbaria dataset because location records are kept in confidence due to poaching concerns.

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years. Pending leks comprised leks without consistent breeding activity during the prior 3 – 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was clipped by the SGMA polygon, and values were re-scaled between zero and one by dividing by the maximum pixel value.The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 – 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (isopleths) was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis.” Journal of Wildlife Management 70: 641-648.Silverman BW. 1986. Density estimation for statistics and data analysis. Chapman & Hall, London, United Kingdom.Vander Wal E, Rodgers AR. 2012. “An individual-based quantitative approach for delineating core areas of animal space use.” Ecological Modelling 224: 48-53.NOTE: This file does not include habitat areas for the Bi-State management area.

We performed replicated, repeated-measures data of height, diameter and vitality at tree level to allow analysis of spatial and temporal structure and diversity in a semi-natural mixed floodplain forest in Italy. Three inventories were performed in 1995, 2005 and 2016 in three ~1 ha plots with varying soil moisture regimes. The use of replicated, repeated-measures data rather than chronosequences allows the examination of true changes in spatial pattern processes through time in this forest type.

Human hunters are described as ‘superpredators’ with a unique ecology. Chronic Wasting Disease among cervids and African swine fever among wild boar are emerging wildlife diseases in Europe with huge economic and cultural repercussions. Understanding hunter movements at broad scales has implications for how to control their spread. Here we show, based on the analysis of the settlement patterns and movements of reindeer (n = 9,685), red deer (n = 47,845), moose (n = 60,365), and roe deer (n = 42,530) hunters from across Norway (2001-2017), that hunter density was more closely linked to human density than prey density, that hunters were largely migratory, aggregated with increasing regional prey densities and often used dogs. Hunter movements extended across Europe and to other continents. Our results provide extensive evidence that the broad-scale movements and residency patterns of post-industrial hunters relative to their prey differ from those of large carnivores.

Competitive intransitivity is mostly considered outside the main body of coexistence theories that rely primarily on the role of niche overlap and differentiation. How the interplay of competitive intransitivity and niche overlap jointly affects species coexistence has received little attention. Here, we consider a rock-paper-scissors competition system where interactions between species can represent the full spectra of transitive-intransitive continuum and niche overlap/differentiation under different levels of competition asymmetry. By comparing results from pair approximation that only considers interference competition between neighbouring cells in spatial lattices, with those under the mean-field assumption, we show that (1) species coexistence under transitive competition is only possible at high niche differentiation; (2) in communities with partial or pure intransitive interactions, high levels of niche overlap are not necessary to beget species extinction; and (3) strong spatial clustering can widen the condition for intransitive loops to facilitate species coexistence. The two mechanisms, competitive intransitivity and niche differentiation, can support species persistence and coexistence, either separately or in combination. Finally, the contribution of intransitive loops to species coexistence can be enhanced by strong local spatial correlations, modulated and maximised by moderate competition asymmetry. Our study, therefore, provides a bridge to link intransitive competition to other generic ecological theories of species coexistence.

Methods This dataset contains the analyses of pair approximationthe (PA) model, codes of drawing Figure 3 and numerical solution datas. It is performed by Matlab R2014a.

Species distribution models (SDMs) are becoming an important tool for marine conservation and management. Yet while there is an increasing diversity and volume of marine biodiversity data for training SDMs, little practical guidance is available on how to leverage distinct data types to build robust models. We explored the effect of different data types on the fit, performance and predictive ability of SDMs by comparing models trained with four data types for a heavily exploited pelagic fish, the blue shark (Prionace glauca), in the Northwest Atlantic: two fishery-dependent (conventional mark-recapture tags, fisheries observer records) and two fishery-independent (satellite-linked electronic tags, pop-up archival tags). We found that all four data types can result in robust models, but differences among spatial predictions highlighted the need to consider ecological realism in model selection and interpretation regardless of data type. Differences among models were primarily attributed ..., Please see the README document ("README.md") and the accompanying published article: Braun, C. D., M. C. Arostegui, N. Farchadi, M. Alexander, P. Afonso, A. Allyn, S. J. Bograd, S. Brodie, D. P. Crear, E. F. Culhane, T. H. Curtis, E. L. Hazen, A. Kerney, N. Lezama-Ochoa, K. E. Mills, D. Pugh, N. Queiroz, J. D. Scott, G. B. Skomal, D. W. Sims, S. R. Thorrold, H. Welch, R. Young-Morse, R. Lewison. In press. Building use-inspired species distribution models: using multiple data types to examine and improve model performance. Ecological Applications. Accepted. DOI: < article DOI will be added when it is assigned >,

Dataset for the endangered endemic cichlid Herichthys bartoni in the Media Luna spring, Mexico. This data includes occurrences records by species life stage (adult, juvenile and fry), in three field sessions corresponding to the summer period, in the years 1999, 2009 and 2019.

For more information about the codes where the previous datasets could be used, visit the following repository with URL: https://doi.org/10.5281/zenodo.7603557.

Likewise, the UC and WDp variables used to run the ecological niche and spatial distribution model, by summer period, can be found in the following repository wirh URL: https://doi.org/10.5281/zenodo.7603890.

This project corresponds to the scripts and data files necessary to replicate the analysis in the manuscript "Seasonality, density dependence and spatial population synchrony" by Pedro G. Nicolau, Rolf A. Ims, Sigrunn H. Sørbye & Nigel G. Yoccoz The folder structure is Data: important files used to reproduce the code. Raw files are .csv and processed files are in .rds Scripts: R scripts necessary for analysis, numbered by order of sequence (some with subnumbering). 0 contains the important functions to compute Bayesian R^2 and correlograms; 01 contains processing for 1; 03 contains processing for 3. Plots: diverse plots used (or not) in the manuscript; not needed for analysis

behaviour demography ecology ecopop environmental-monitoring-facilities evolution fauna habitats-and-biotopes pathogens population-ecology populations species-distribution species-populations-population-structure-by-age-size-class species-traits-demographic-traits species-traits-phenology species-traits-physiological-traits

Obtaining ecological and natural history data from cryptic squamates can be challenging, but is crucial to understanding species’ biology, particularly in the context of conservation. In the Greater Antilles, this challenge is especially apparent, particularly among the West Indian boas (genus Chilabothrus). Most species have had only minimal natural history study, with a few exceptions. The Turks & Caicos boa (C. chrysogaster) has been studied intensively for over 16 years on the small privately owned island of Big Ambergris Cay, Turks and Caicos Islands. We conducted a multi-year radio-tracking study on the species to generate information relevant to spatial habitat use and movement that will inform conservation decision-making in the face of increasing development pressure. We tracked a total of 19 female snakes using surgically implanted transmitters, enabling us to obtain between 16 and 40 location observations per boa over the lifetime of each transmitter. We estimated home ra..., Snake radio telemetry., R v. 4.2.3., # Data from: Spatial Ecology of the Turks & Caicos Boa, Chilabothrus c. chrysogaster Cope, 1871 (Serpentes: Boidae)

1) Data file (boas2.csv) and R code (R_Code_Reynolds_etal_2023.r) to run basic spatial statistics

This data file contains latitude and longitude for each snake for each encounter. The date, time, and snake ID are included as columns. The R code will allow a user to calculate distances traveled, as well as basic KUD calculations. Each row is a single observation of a snake, identified in the the name column.

2) Data files to run spatial statistics with the package ctmm. See the external CTMM package help file for column naming conventions.

This folder contains the following data files:

boa_telem_data.csv. This file contains the telemetry data for all boas, formatted into the input format for ctmm. Each row is a single snake observation, identified by the event-id. Other columns include visible (was the snake found), the timestamp in CTMM format, the lat/long coo...

Declining reptilian populations have been a growing concern over the last couple of decades. One such declining species of concern, the Blanding’s turtle (Emydoidea blandingii), occurs as isolated populations in North American prairie-wetlands and is at risk of extirpation due to habitat loss and fragmentation, and increased predator (e.g. racoons, coyotes) populations due to supplemented resources in urban environments. To help mitigate declining populations, wildlife managers have invested in the conservation of this species through head-starting (i.e. reared in ex-situ) and juvenile release programs to augment wild Blanding’s turtle populations. However, much of their spatial and winter/thermal ecology is understudied, and data for juveniles, and juveniles reared in ex-situ is especially scarce, yet this information is imperative to understanding shortfalls and improving head-starting efforts in the future. In spring 2016 (RR2016) and 2017 (RR2017) we released a cohort (n=12 each year) of head-started juvenile Blanding’s turtles equipped with radio transmitters and temperature dataloggers into a prairie-wetland in the greater Chicago region, North America. Using ground-based radio telemetry, we determined seasonal movement areas (SMAs; spring, summer, and fall) and annual home ranges (AHRs) for both RR2016 and RR2017 cohorts via Kernel Density (KD) estimates. We also investigated the thermal characteristics of overwintering for both juvenile cohorts. We found that SMAs for the RR2016 cohort, but not for the RR2017 cohort, significantly differed across seasons for most SMA estimators. We also found that juveniles in both cohorts not only survived overwintering but also displayed similar overwintering phenology (i.e. initiation: October-November; termination: April) and temperature variation as Blanding’s turtles adults in other studies. Overall, our results indicate that head-started juvenile Blanding’s turtles may be able to acclimatize quickly to their natural environment post-release. Our study provides evidence of the efficacy of well-developed head-starting programs that aim to augment and preserve imperiled turtle populations. Methods Collection Morphometric data Carapace length was measured using a pair of digital calipers. Mass was recorded via a digital animal weighing scale. Percent fat was calculated using the following formula from Newman et al. 2019 (see. Cann and Weber et al. 2020) fat % = 2.025 + (3.978 × 10-4 × (CL3 ⁄mass)) − (1.152 × 10-3 × mass), where CL = carapace length. Morphometric data filename: Morphometrics_allCohorts Spatial data In quotes are from the manuscript itself:

Juveniles in RR2016 [Recently Released group 2016] were tracked once weekly from May 2016 to November 2016 for spatial analyses, and then bi-monthly as weather allowed to April 2017 for estimated emergence from brumation. Juveniles in RR2017 [Recently Released group 2017] were tracked two to three times weekly from May 2017 to November 2017 for spatial analyses, and then bi-monthly as weather allowed until April 2018 for estimated emergence from brumation. Juveniles in RR2016 were tracked further in tandem with juveniles in RR2017 until April 2018 if survival status allowed, though this data was excluded from RR2017 statistical analyses. Global positioning system coordinates were recorded in UTMs on Garmin (GPSmap 62sc/64st) devices. To provide a probabilistic estimate of SMAs and AHRs by scaling the boundaries to the area’s most frequently visited by the individual, we used the Geospatial Modeling Environment (Beyer 2015; version 0.7.4.0 in tandem with R 3.1.1+) to calculate kernel density SMAs and AHRs estimates (KD; i.e. non-parametric method to measure the probability of occurrence based on the density of points in similar areas). Variation in seasonal movement areas estimates were calculated for each individual juvenile released during each season (Table 1), approximately following the phenology of Blanding’s turtle activity in the adjacent state of Wisconsin, U.S.A. (Ross and Anderson 1990, Thiel and Wilder 2010), and for AHRs (i.e. total active season; static location of the juveniles during brumation were excluded from SMA and AHR calculations). We used the least squares cross-validation (LSCV) bandwidth following Seaman and Powell (1996) and Byer et al. (2017) with a cell size of two. Kernel density raster files were then converted to isopleths at 95, 90, and 50% confidence intervals to get a range of estimates (Fischer et al. 2013, Ghaffari et al. 2014, Drabik-Hamshare and Downs 2017). These intervals represent how likely it is for our tracked juveniles to be found in their respective isopleth SMAs and AHRs. For example, a SMA KD home range at 95% represents the entire range of an individual, or the area in which the animal spends 95% of its time; whereas an isopleth of 50% represents the core area of habitat where the animal spends 50% of its time. Isopleth files were then imported into ArcMap version. 10.3.1 (ESRI 2015) where we converted each isopleth to polygons for area calculation. Individual juveniles that did not complete the entire season or seasons of observation were removed from the mean calculation of the group (e.g. deaths, transmitter loss; see Cann and Weber et al. 2021 for more survivorship details).

Once polygons for each isopleth were created and the areas estimated within ArcMap, two excel files were created to record all individual's home ranges according to the two estimators used: Kernal Density Estimate filename: KDE_area_allCohorts Minimum Convex Polygon filename: MCP_area_allCohorts Temperature data In quotes are from the manuscript itself: Thermal characteristics of the brumation sites were assessed by placing a Thermochron iButton dataloggers (model DS1921G, Dallas Semiconductor) layered in a black rubber coating (i.e. for water damaged mitigation; Performix – Plasti-dip Brand®) on the carapace of each released individual. Carapace temperature (Tc) was measured in Celsius using the iButton dataloggers attached to a single carapacial scute adjacent to the radio-transmitter on each juvenile turtle also using 5-min epoxy (Milanovich et al. 2017) prior to release. Although it has been shown that coating Thermochron iButtons can have an effect on the temperature readings, the differences seen were relatively small at 0.0-1.3°C compared to those uncoated (Roznik and Alford 2012), therefore we followed methods of Akins et al. (2014) and Harden et al. (2015) to coat iButtons. Tc was logged at a rate of once every 60 mins day-1 for the spring, summer, and fall months for RR2016. Tc iButtons were collected and replaced after the following periods: May-August and September-October 2016. We changed the rate at which temperatures were logged for the 2016-2017 winter season (November-April), programming them to log once every 150 mins day-1 until turtle emergence to ensure memory space for the entirety of the brumation period. We subsequently made the same data-logging rate change as well for the dataloggers attached to RR2017 in the spring, summer, fall, and 2017-2018 winter. Environmental temperature (Te) was measured by using black rubber (Performix – Plasti-dip Brand®) coated iButtons placed in three vertically oriented PVC pipes randomly located throughout the wetland among emergent vegetation, excluding the open water habitat. iButtons were inserted at 15 cm intervals in notched groves on the PVC pipes to correlate with depths into the substrate of 45 cm, 30 cm, and 15 cm below substrate level, 0 cm at surface level of the muddy substrate/water, and 15 cm above surface level (ambient, above the water), allowing us to compare the Tc with Te at or above surface level and various subsurface levels. Te dataloggers for the wintering period were programed to log temperatures once every 150 mins day-1 until spring or turtle emergence. Additionally, Te loggers enabled us to quantify brumation site depth in the substrate after cross-comparison with Tc loggers (Currylow et al. 2013). RR2016 filename: Temp_RR2016 RR2017 filename: Temp_RR2017 Note: Cohort 2016 and Cohort 2017 in any excel sheet correlate to RR2016 and RR2017 in the manuscript.

Processing Morphometric data We used Statistica to import the excel file containing all morphometric related data. We then summarized said data by month, cohort, and year data was collected. All turtle ID measurements were combined and then averaged for each month and each year. Cohorts (i.e. RR2016 and RR2017) were summarized separately. Spatial data We used Statistica to summarize and analyse Kernel Density and Minimum Convex Polygon estimator datasets. All turtle IDs within the same cohort were analyzed across each season for the three isopleth values used (i.e. 95%, 90%, 50%) in the Kernel Density estimates. Since there were no isopleths associated with the Minimum Convex Polygon estimate, this was done once. In quotes are from the manuscript itself: Differences in AHR [annual home range] KD [kernel density] home ranges of individual turtles across years, and with different number of locations, were examined using a mixed-effect ANOVA with year as fixed effects, individual turtle ID as the random effect, and the number of locations as the covariate. We used one-way ANOVAs to test if there was variation in SMA KD home ranges for each cohort separately. Tukey Multiple Comparison tests were calculated for significant ANOVAs. Temperature data For the two temperature dataset files corresponding to RR2016 and RR2017, the three random locations that were used to measure environmental temperature (Te) labeled: Center_PVC, East_PVC, and West_PVC were consolidated into one Te, and averaged for each day/time categorized by the depth of Te (i.e. 15 cm below, 0 cm ground, 15 cm above, 30 cm above, and 45 cm above). All turtle ID temperatures were consolidated into carapace temperature (Tc), and averaged for each day/time. In

Human encroachment into natural habitats is typically followed by conflicts derived from wildlife damages to agriculture and livestock. Spatial risk modelling is a useful tool to gain understanding of wildlife damage and mitigate conflicts. Although resource selection is a hierarchical process operating at multiple scales, risk models usually fail to address more than one scale, which can result in the misidentification of the underlying processes. Here, we addressed the multi-scale nature of wildlife damage occurrence by considering ecological and management correlates interacting from household to landscape scales. We studied brown bear (Ursus arctos) damage to apiaries in the North-eastern Carpathians as our model system. Using generalized additive models, we found that brown bear tendency to avoid humans and the habitat preferences of bears and beekeepers determine the risk of bear damage at multiple scales. Damage risk at fine scales increased when the broad landscape context also ...

In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg et al., 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).

It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter et al., 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (e.g., Naoki et al., 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:

In the first instance, we generated the background points and extracted the values of the variables (Code2_Extract_values_DWp_SC.R). Barbet-Massin et al. (2012) recommend the use of 10,000 background points when using regression methods (e.g., Generalized Linear Model) or distance-based models (e.g., DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (e.g., bioclimatic, topographic, demographic, habitat) in function of presence and background points (e.g., Hijmans and Elith, 2017).

Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).

After that, we ran the code for the GBM method (Gradient Boost Machine; Code3_GBM_Relative_contribution.R and Code4_Relative_contribution.R), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (e.g., Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.

Subsequently, the correlation of the variables is run by Pearson's method (Code5_Pearson_Correlation.R) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (e.g., Awan et al., 2021).

Once the above codes were run, we uploaded the same subgroups (i.e., presence and background groups with 75% training and 25% testing) (Code6_Presence&backgrounds.R) for the GLM method code (Code7_GLM_model.R). Here, we first ran the GLM models per variable to obtain the p-significance value of each variable (alpha ≤ 0.05); we selected the value one (i.e., presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (e.g., Fielding and Bell, 1997; Allouche et al., 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.

On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).

Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).

We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche et al., 2006).

The last code (i.e., Code8_DOMAIN_SuitHab_model.R) is for species distribution modelling using the DOMAIN algorithm (Carpenter et al., 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.

Regarding the model evaluation and estimation, we selected the following estimators:

1) partial ROC, which evaluates the approach between the curves of positive (i.e., correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).

2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long et al., 1988).

Social interaction and location data for gopher tortoises in the Boyd Hill Nature Preserve, located in Pinellas County, Florida, USA and the R code needed to perform spatial and social network analysis of the data. The social interaction data was manually extracted from time-lapse videos taken by camera traps located at the entrance of tortoise burrows. Location data was collected using radio telemetry.

Analysis for spatial ecology manuscript Zamenis longissimus

Data associated with the manuscript:Wyse, S.V., Etherington, T.R., & Hulme, P.E. (2022) Quantifying the risk of non-native conifer establishment across heterogeneous landscapes. Journal of Applied Ecology.

Dear data users, This repository contains 3 sets of files corresponding to the data analysis (R Script) and dataset (xlsx or csv files) for 3 sections of this paper.

Attached are the following pairs of R script and dataset for the section of:

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estuary * - categorical variables of estuary -either Port Hacking, Crookhaven River or Hunter River tile_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from reef_id - unique replicate of each tile where the response variable (oyster counts and percentages) where collected from liveoys_aug2020 - live counts of oysters on each tile when the abiotic variables where measured totoys_aug2020 percent_live_aug2020 - live counts of oysters on each tile when the abiotic variables where measured deadoys_aug2020 – dead counts of oysters on each tile when the abiotic variables where measured cover_area – total cover area (in mm2) oysters on each tile when the abiotic variables where measured sed.rate – sedimentation rate (grams/ day) of sediment traps deployed next to tile replicates. cov.temp_logger – Covariation of temperature (no unit) based upon on temperature measured by temperature logger attached to selected tile replicates for a specific deployment period q5_logger – 5th quartile of daily temperature in Celsius measured by temperature logger attached to selected tile replicates for a specific deployment period q95_logger – 95th quartile of daily temperature in Celsius measured by temperature logger attached to selected tile replicates for a specific deployment period

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For any queries regarding the contents of the file above, please email first co-author, Dr. Rick Leong at rick.leong@unsw.edu.au

For dataset pertaining to <