Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity. The datasets are described in the associated manuscript submitted to PLOS ONE. The LIDSAT, LID, and SAT files are structured the same way, with each row representing a Public Land Survey System (PLSS) section and each column representing a response variable or remote sensing predictor. The first column (“section_id”) indicates the PLSS section ID. The next six columns (“received_6” to “received_1”) represent the number of transmitted signals received by 5, 4, 3, 2, 1, and 0 stationary goTennas, respectively, and the “tot_trans” column represents the total number of signals transmitted by the mobile goTenna in the section. The next six columns (“Con_6_obs” to “Con_1_obs”) represent the proportion of transmitted signals received by 5, 4, 3, 2, 1, and 0 stationary goTennas (i.e., the six connectivity levels). These were calculated by dividing the respective “received” columns by the “tot_trans” column (e.g., Con_6_obs = received_6/tot_trans, etc.). Because Dirichlet regression cannot handle zero values, zeroes were imputed as described in the manuscript in order to derive the next six columns (“Con_6” to “Con_1”). These columns correspond to the compositional response variables used to develop the Dirichlet regression models and represent the proportion of time 5, 4, 3, 2, 1, and 0 stationary goTennas were connected to the mobile goTenna, respectively. All remaining columns after “Con_1” correspond to either a lidar- or satellite-derived metric calculated for each section, according to the descriptions and variable keys located in the manuscript. The LIDSAT, LID, and SAT datasets have identical response variables and the only difference between them is the inclusion of different remote sensing predictors. The LIDSAT dataset contains all of the lidar- and satellite-derived predictors, the LID dataset only contains the lidar-derived predictors, and the SAT dataset only contains the satellite-derived predictors. The ATAK_Full_RS_Metrics_MaxMinValues dataset contains the maximum and minimum values for each remote sensing predictor variable which were used to normalize the variables as described in the manuscript. The first column contains the remote sensing predictor variable name and matches the remote sensing variable names in the LIDSAT, LID, and SAT datasets. The next two columns list the minimum and maximum values of the corresponding predictor.

This dataset aggregates data extracted from the CIRAD wood chemical composition database, describing 614 botanical species belonging to 358 genera and 89 families, in Montpellier, France. WOOD CHEMICAL COMPOSITION DATABASE The CIRAD wood chemical composition database is a part of the general CIRAD wood database covering a wide range of technological properties, mainly physics and mechanics, anatomy, natural durability and preservation, wood machining, etc. 614 wood species are described with measurements of wood polymers (cellulose, lignin and pentosan) and overall extraneous components (ethanol-benzene, hot water extracts, ash, silica content). These measurements were taken between 1945 and 1990 using the same standard protocols. Chemical characteristics were measured on specimen from 1,194 trees corresponding to 614 botanical species, 358 genera and 89 families. DESCRIPTION OF THE DATASET It consists in the following file: Cirad chemical wood composition database - dataset‧xlsx General information The file is in XLSX format. Data fields The file provides the following characteristics for each specimen (also given in the second tab of the file Cirad chemical wood composition - dataset‧xlsx): • Test: Each individual set of measurements • Year: Calendar year of the measurement • Tree: CTFT reference in the wood collection • Origin: Provenance country of the tree • Family: Botanical family (updated version June 2018) • Species: Botanical genus and species (updated version June 2018) • Density Code: 1- value from Cirad wood collection; 2- value from literature; 3- missing value • Density: Value from wood collection or sometimes from literature • AB ext: Ethanol/benzene extract (%) • W ext: Water extract (%) • Ash: Mineral content (%) • Silica: Silica content (%) • Lignin: Lignin content (%) • Pentosan: Pentosan content (%) • Cellulose: Cellulose content (%) • Balance: Summation of all contents except silica (%) % values are always related to anhydrous wood gross weight

This is the complete dataset for the 500 Cities project 2019 release. This dataset includes 2017, 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations. Data sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2017, 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017, 2012-2016 estimates. Because some questions are only asked every other year in the BRFSS, there are 7 measures (all teeth lost, dental visits, mammograms, pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) from the 2016 BRFSS that are the same in the 2019 release as the previous 2018 release. More information about the methodology can be found at www.cdc.gov/500cities.

This database is comprised of 603 participants who provided self-report data online in their school classrooms. The data was collected in 2016 and 2017. The dataset is comprised of 208 males (34%) and 395 females (66%). Their ages ranged from 12 to 15 years. Their age in years at baseline is provided. The majority were born in Australia. Data were drawn from students at two Australian independent secondary schools. The data contains total responses for the following scales: The Intolerance of Uncertainty Scale (IUS-12; Short form; Carleton et al, 2007) is a 12-item scale measuring two dimensions of Prospective and Inhibitory intolerance of uncertainty. Two subscales of the Children’s Automatic Thoughts Scale (CATS; Schniering & Rapee, 2002) were administered. The Peronalising and Social Threat were each composed of 10 items. UPPS Impulsive Behaviour Scale (Whiteside & Lynam, 2001) which is comprised of 12 items. Dispositional Envy Scale (DES; Smith et al, 1999) which is comprised of 8 items. Spence Children’s Anxiety Scale (SCAS; Spence, 1998) which is comprised of 44 items. Three subscales totals included were the GAD subscale (labelled SCAS_GAD), the OCD subscale (labelled SCAS_OCD) and the Social Anxiety subscale (labelled SCAS_SA). Each subscale was comprised of 6 items. Avoidance and Fusion Questionnaire for Youth (AFQ-Y; Greco et al., 2008) which is comprised of 17 items. Distress Disclosure Index (DDI; Kahn & Hessling, 2001) which is comprised of 12 items. Repetitive Thinking Questionnaire-10 (RTQ-10; McEvoy et al., 2014) which is comprised of 10 items. The Brief Fear of Negative Evaluation Scale, Straightforward Items (BFNE-S; Rodebaugh et al., 2004) which is comprised of 8 items. Short Mood and Feelings Questionnaire (SMFQ; Angold et al., 1995) which is comprised by 13 items. The Self-Compassion Scale Short Form (SCS-SF; Raes et al., 2011) which is comprised by 12 items. The subscales include Self Kindness, Self Judgment, Social Media subscales - These subscale scores were based on social media questions composed for this project and also drawn from three separate scales as indicated in the table below. The original scales assessed whether participants experience discomfort and a fear of missing out when disconnected from social media (taken from the Australian Psychological Society Stress and Wellbeing Survey; Australian Psychological Society, 2015a), style of social media use (Tandoc et al., 2015b) and Fear of Missing Out (Przybylski et al., 2013c). The items in each subscale are listed below. Pub_Share Public Sharing When I have a good time it is important for me to share the details onlinec On social media how often do you write a status updateb On social media how often do you post photosb Surveillance_SM On social media how often do you read the newsfeed On social media how often do you read a friend’s status updateb On social media how often do you view a friend’s photob On social media how often do you browse a friend’s timelineb Upset Share On social media how often do you go online to share things that have upset you? Text private On social media how often do you Text friends privately to share things that have upset you? Insight_SM Social Media Reduction I use social media less now because it often made me feel inadequate FOMO I am afraid that I will miss out on something if I don’t stay connected to my online social networksa. I feel worried and uncomfortable when I can’t access my social media accountsa. Neg Eff of SM I find it difficult to relax or sleep after spending time on social networking sitesa. I feel my brain ‘burnout’ with the constant connectivity of social mediaa. I notice I feel envy when I use social media.
I can easily detach from the envy that appears following the use of social media (reverse scored) DES_SM Envy Mean acts online Feeling envious about another person has led me to post a comment online about another person to make them laugh Feeling envious has led me to post a photo online without someone’s permission to make them angry or to make fun of them Feeling envious has prompted me to keep another student out of things on purpose, excluding her from my group of friends or ignoring them. Substance Use: Two items measuring peer influence on alcohol consumption were adapted from the SHAHRP “Patterns of Alcohol Use” measure (McBride, Farringdon & Midford, 2000). These items were “When I am with friends I am quite likely to drink too much alcohol” and “Substances (alcohol, drugs, medication) are the immediate way I respond to my thoughts about a situation when I feel distressed or upset. Angold, A., Costello, E. J., Messer, S. C., & Pickles, A. (1995). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents. International Journal of Methods in Psychiatric Research, 5(4), 237–249. Australian Psychological Society. (2015). Stress and wellbeing in Australia survey. https://www.headsup.org.au/docs/default-source/default-document-library/stress-and-wellbeing-in-australia-report.pdf?sfvrsn=7f08274d_4 Greco, L.A., Lambert, W. & Baer., R.A. (2008) Psychological inflexibility in childhood and adolescence: Development and evaluation of the Avoidance and Fusion Questionnaire for Youth. Psychological Assessment, 20, 93-102. https://doi.org/10.1037/1040-3590.20.2.9 Kahn, J. H., & Hessling, R. M. (2001). Measuring the tendency to conceal versus disclose psychological distress. Journal of Social and Clinical Psychology, 20(1), 41–65. https://doi.org/10.1521/jscp.20.1.41.22254 McBride, N., Farringdon, F. & Midford, R. (2000) What harms do young Australians experience in alcohol use situations. Australian and New Zealand Journal of Public Health, 24, 54–60 https://doi.org/10.1111/j.1467-842x.2000.tb00723.x McEvoy, P.M., Thibodeau, M.A., Asmundson, G.J.G. (2014) Trait Repetitive Negative Thinking: A brief transdiagnostic assessment. Journal of Experimental Psychopathology, 5, 1-17. Doi. 10.5127/jep.037813 Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in human behavior, 29(4), 1841-1848. https://doi.org/10.1016/j.chb.2013.02.014 Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the self-compassion scale. Clinical Psychology and Psychotherapy, 18(3), 250-255. https://doi.org/10.1002/cpp.702 Rodebaugh, T. L., Woods, C. M., Thissen, D. M., Heimberg, R. G., Chambless, D. L., & Rapee, R. M. (2004). More information from fewer questions: the factor structure and item properties of the original and brief fear of negative evaluation scale. Psychological assessment, 16(2), 169. https://doi.org/10.1037/10403590.16.2.169 Schniering, C. A., & Rapee, R. M. (2002). Development and validation of a measure of children’s automatic thoughts: the children’s automatic thoughts scale. Behaviour Research and Therapy, 40(9), 1091-1109. . https://doi.org/10.1016/S0005-7967(02)00022-0 Smith, R. H., Parrott, W. G., Diener, E. F., Hoyle, R. H., & Kim, S. H. (1999). Dispositional envy. Personality and Social Psychology Bulletin, 25(8), 1007-1020. https://doi.org/10.1177/01461672992511008 Spence, S. H. (1998). A measure of anxiety symptoms among children. Behaviour Research and Therapy, 36(5), 545-566. https://doi.org/10.1016/S0005-7967(98)00034-5 Tandoc, E. C., Ferrucci, P., & Duffy, M. (2015). Facebook use, envy, and depression among college students: Is facebooking depressing? Computers in Human Behavior, 43, 139–146. https://doi.org/10.1016/j.chb.2014.10.053 Whiteside, S.P. & Lynam, D.R. (2001) The five factor model and impulsivity: using a structural model of personality to understand impulsivity. Personality and Individual Differences 30,669-689. https://doi.org/10.1016/S0191-8869(00)00064-7 The data was collected by Dr Danielle A Einstein, Dr Madeleine Fraser, Dr Anne McMaugh, Prof Peter McEvoy, Prof Ron Rapee, Assoc/Prof Maree Abbott, Prof Warren Mansell and Dr Eyal Karin as part of the Insights Project. The data set has the option of downloading an excel file (composed of two worksheet tabs) or CSV files 1) Data and 2) Variable labels.

Description: Deadwood decay and traits was measured across 20 SAFE Vegetation plots (17 existing plots and 3 new plots set up for this project). The plots form a gradient from no logging disturbance to salvage logged forest. See the 'PlotInfo' worksheet for plot descriptions. 'DeadwoodPieces_FieldData' worksheet: measurement of deadwood pieces in the forest. In the initial campaign in 2017, deadwood pieces were chosen for sampling from four decay classes, from DC_1 to DC_4 (DC_5 was not included, as it was unlikely that these pieces could have been resampled in later campaigns) and from six diameter classes of 5-10 cm, 10-20 cm, 20-40 cm, 40-70 cm, 70-110 cm and >110 cm. The aim was to sample one deadwood piece in each decay class x diameter class combination inside the plot or in the immediate vicinity, but in practice, not all combinations were found in every plot.Decay classes are based on a five-point scale following Harmon et al. (1995) Biotropica 27: DC_1 - recently dead, bark cover extensive and leaves and fine twigs present; DC_2 - some signs of decay, no leaves and few fine twigs, bark started to fall off, but wood is still firm; DC_3 - clear signs of decay, bark has started to fall off and/or sapwood is softening; DC_4 - wood is soft, typically no bark; DC_5 - wood is very soft and typically lost its shape. Each deadwood piece was given a unique tag and its lenght and diameter was measured. In addition, the penetration of the knife into the wood was measured and bark cover, moss cover, presence of fungal bodies and presence of insect holes was recorded. Soil moisture was measured near each deadwood piece. All pieces were mapped and photographed.Small samples of wood and bark were taken from each deadwood piece for deadwood trait measurements. For standing deadwood, the sampling point was 1.3 above the ground and for fallen deadwood the sampling point was determined from a random number generator, as a random proportion of the lenght of the tree, measured from the base of the deadwood. Volumetric samples were take using an increment borer or cylindrical samplign ring (for soft pieces), and small bulk samples were collected with chisel to obtain enough material for chemical analyses. The deadwood pieces were resampled in 2019, after two years of decay. In addition, the 5-10 cm diameter class pieces were resampled in 2018 after one year decay, as it was not certain how many of the small pieces could be found after two years. The dimensions and other characteristics were measured as in 2017, and new wood samples were take near the initial sampling point.'Density_Moisture_LabData' worksheet:The samples were stored in plastic bags and transported to the field laboratory. They were stored in a vrefrigerator and processed within two days. Fresh Volume was estimated either based on geometry (diameter and lenght of the samples collected using increment borer or sampling ring) or on water displacement method. Fresh mass was recorded. Samples were dried at 50°C until constant mass and dry weight was recored. Wood moisture was estimated from the fresh and dry mass and wood density was calculated using the different volume estimates and dry weight.'WoodChemistry_LabData' worksheet:After drying, all material from each deadwood piece was pooled for chemical analyses (2017 samples). The chemical analyses were carried out at Forest Research Centre, Sabah Forestry Department, Sepilok, Sabah, Malaysia, led by Dr. Noreen Majalap. The samples were analysed for wood pH, total phosphorus concentration, total nitrogen concentration and total carbon concentration. Project: This dataset was collected as part of the following SAFE research project: Decomposition of deadwood debris left over from tropical rainforest logging operations Funding: These data were collected as part of research funded by:

NERC (Grant, NE/P002218/1) Sime Darby Foundation (Grant, SAFE Core data) This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.

Permits: These data were collected under permit from the following authorities:

Sabah Biodiversity Council (Research licence JKM/MBS.1000-2/2 JLD.10 (104))

XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_WoodDecomposition_Data_SAFEdatabase_2021-06-04.xlsx SAFE_WoodDecomposition_Data_SAFEdatabase_2021-06-04.xlsx This file contains dataset metadata and 4 data tables:

Field measurements of deadwood pieces (described in worksheet DeadwoodPieces_FieldData) Description: Field measurements of deadwood pieces Number of fields: 28 Number of data rows: 523 Fields:

Block: SAFE Project Block code (Field type: id) PlotNumber: Plot within Block (Field type: id) Block_Plot: SAFE Project plot name (combination of Block and plot number/code) (Field type: location) SamplingYear: Sampling year. 1st campaign in 2017 (all diameter classes - set up phase), 2nd campaign in 2018 (diameter class 5-10 cm only - resampling), 3rd campaign in 2019 (all diameter classes - resampling). (Field type: numeric) SamplingCampaign: 1st campaign in 2017, 2nd campaign in 2018 (diameter class 5-10 cm only), 3rd campaign in 2019 (all diameter classes). (Field type: ordered categorical) Date: Field data collection date in excel format (dd/mm/yyyy) (Field type: date) RA: Observers (RA=reseach assistant) (Field type: comments) DiameterClass_cm: Diameter class of the deadwood piece, categorised during the first (2017) measurement campaign and not changed in subsequent campaigns (Field type: ordered categorical) Tag: Unique tag number given to deadwood pieces (Field type: id) DecayClass: Decay class of the deadwood piece, assessed separately during each campaign (Field type: ordered categorical) Fallen_Standing: Fallen (F) or Standing (S) deadwood (Field type: categorical) GroundContact: The point where the wood samples were taken was touching the ground (On) or off the ground (Off) (Field type: categorical) DBH1_base: Diameter of the deadwood piece, measured at the base end. Not measured for standing deadwood. (Field type: numeric) DBH2_tip: Diameter of the deadwood piece, measured at the tip end. Not measured for standing deadwood. (Field type: numeric) DBH3_SamplingPoint: Diameter of the deadwood piece, measured at the point where the wood samples were taken (1.3 from the ground for standing deadwood, randomly assigned point for fallen deadwood) (Field type: numeric) Length_Total: Length from the base to the tip of the deadwood piece (Field type: numeric) Length_PaintToPaint: Distance between the base and tip diameter measurement points, which is typically somewhat shorter than the total length of the deadwood piece (Field type: numeric) Length_ToSamplingPoint: Length from the deadwood base to the sampling point (Field type: numeric) Hollowness_Proportion: Proportion of the total diameter that was hollow (Field type: numeric) KnifePenetration: Depth to which a pocket knife could be pushed into the deadwood, maximum was 7.7 cm. (Field type: numeric) BarkCover: Percentage cover of bark (Field type: numeric) BarkLoose: How loose is the bark: _0-no bark; _1-bark not loose; _2-bark slightly loose; _3-bark very loose and peeling (Field type: ordered categorical) MossCover: Percentage cover of moss (not taking into accout the sections of the surface against the ground) (Field type: numeric) Fungi_YN: Fungal bodies present (Y) or not (N) (Field type: categorical) InsectHole_YN: Insect holes present (Y) or not (N) (Field type: categorical) SoilMoisture: Volumetric soil water content in the top 12 cm of soil measured near the deadwood samplign point (Field type: numeric) LivingTreeTag: Tag number used in the living tree census (not found for most of the deadwood pieces). (Field type: id) Notes: Any notes in the field or at data entry stage (Field type: comments)

Wood density and wood moisture data (described in worksheet Density_Moisture_LabData) Description: Wood density and wood moisture data, measured in the field lab Number of fields: 23 Number of data rows: 2906 Fields:

SamplingYear: Sampling year. 1st campaign in 2017, 2nd campaign in 2018 (diameter class 5-10 cm only), 3rd campaign in 2019 (all diameter classes). (Field type: numeric) SamplingCampaign: 1st campaign in 2017, 2nd campaign in 2018 (diameter class 5-10 cm only), 3rd campaign in 2019 (all diameter classes). (Field type: ordered categorical) SamplingDate: Date when sample collected in the field, in excel format (dd/mm/yyyy). Samples were processed within two days of the collection. (Field type: date) RA_Lab: Names of people processing the samples in the lab (RA = research assistant) (Field type: comments) Block: SAFE Project Block code (Field type: id) Plot: Plot within Block (Field type: id) PlotCode: SAFE Project plot name (combination of Block and plot number/code) (Field type: location) Tag: Unique tag number given to deadwood pieces (Field type: id) Method: Method for collecting wood and bark samples. For most deadwood pieces, several methods were used. Samples with an increment borer ('Core') or cylindrical samplign ring ('SamplingRing') allowed for volumetric samples, while the 'Bulk' method allowed for enough material to be collected for chemical analyses. (Field type: categorical) SampleType: Samples are either Wood or Bark. CombWood samples are pooled wood samples (see the Summary sheet for futher info) (Field type: categorical) SampleReplicate: Replicates of the individually measured and weighed samples within one deadwood piece in each samplign campaign. These are not independent, e.g. one core can result into several independently measured segments, and bulk samples can yield individually measured pieces. All samples were taken within 15 cm of one another. (Field type: replicate) SampleDiameter: Diameter of the samples taken either with an

The PEN network was launched in September 2004 by the Center for International Forestry Research (CIFOR) with the aim of collecting uniform socio-economic and environmental data at household and village levels in rural areas of developing countries. The data presented here were collected by 33 PEN partners (mainly PhD students) and comprise 8,301 households in 334 villages located in 24 countries in Asia, Africa and Latin America. Three types of quantitative surveys were conducted: 1. Village surveys (V1, V2) 2. Annual household surveys (A1, A2) 3. Quarterly household surveys (Q1, Q2, Q3, Q4) The village surveys (V1-V2) collected data that were common to all or showed little variation among households. The first village survey, V1, was conducted at the beginning of the fieldwork to get background information on the villages while the second survey, V2 was conducted the end of the fieldwork period to get information for the 12 months period covered by the surveys. The household surveys were grouped into two categories: quarterly surveys (Q1, Q2, Q3, Q4) to collect income information, and, household surveys (A1, A2) to collect all other household information. A critical feature of the PEN research project was to collect detailed, high-quality data on forest use. This was done through quarterly income household surveys, for two reasons: first, short recall periods increase accuracy and reliability and, second, quarterly data would allow us to document seasonal variation in (forest) income and thus, inter alia, help us understand to what extent forests act as seasonal gap fillers. There are three partners (10101, 10203, and 10301 ) who, because of various particular circumstances, only conducted three of the four income surveys. In addition, 598 of the households missed out on one of the quarterly surveys, e.g., due to temporal absence or sickness, or insecurity in the area. These are still included in the database, while households missing more than one quarter were excluded. Two other household surveys were conducted. The first annual household survey (A1) collected basic household information (demographics, assets, forest-related information) and was done at the beginning of the survey period while the second (A2) collected information for the 12-month period covered by the surveys (e.g., on risk management) and was done at the end of the survey period. Note, however, that we did not collect any systematic data on the time allocation of households: while highly relevant for many analyses, we believed that it would be too time-consuming a component to add to our standard survey questions. The project is further described and discussed in two edited volumes by Angelsen et al. (2011) (describes particular the methods used) and Wunder et al. (2014) (includes six articles based on the PEN project).

Contemporary data (2017/2018): An open area on the north side of the ESGR (GPS coordinates: 42.461808, -84.011128) was the primary site for this study as it corresponds to the location of “Evans’ Old Field”, one of the areas historically sampled for bees. The field was described by Evans as a 7.7 ha abandoned field with a mid-successional community of plants surrounded by oak-hickory woods. It is now 1.3 ha of semi-open habitat with significant encroachment of the surrounding oak-hickory woods and invasive autumn olive (Elaeagnus umbellata Thunb.). The site was visited every other week during the summers of 2017 and 2018 to sample bees. In 2017, the first sampling day was June 1 and the final sampling day was September 25. In 2018, the first sampling day was May 8 and the final day was October 3. We expanded sampling in 2018 to include a wider diversity of bees with narrower phenological periods.During each visit we sampled bees using three methods. First, we walked to the center of the open field and randomly selected a direction to start the first 25 meter transect. Three other 25 m transects were then established based on the first one, each at a 90-degree angle from the neighboring transect for a total of 100m sampled, with each transect segment moving away from a central location. Each transect was walked for 10 minutes each, a total of 40 minutes of sampling. We used aerial insect nets to collect bees found within 1.5m of the transect, and time was stopped for specimen processing. The host plant was recorded for all specimens captured from flowers. Flowering plants were identified to the lowest taxonomic level in the field using Newcomb’s guide and the PlantNet app, usually to species. Second, we spent 20 minutes collecting bees from plants of any species in the general vicinity of the open field. Third, to most closely match the methods used by Evans (see below), we spent 30 minutes sampling bees at each of the primary blooming plant species located in the field. Total time spent conducting this final sampling method varied based on the number of primary blooming plants at each visit, with a minimum of 30-minutes if there was only one primary plant. This sampling method was always done last, and included any plants that we collected more than one bee from that day. All bees were identified to species (or lowest possible taxonomic level) using relevant keys. All specimens collected in 2017 and 2018 are currently held in the Isaacs Lab at Michigan State University (as of 2024), and will eventually be deposited at the A.J. Cook Arthropod Collection at Michigan State University for long-term inclusion in that collection.Historical data (1921-1999): The University of Michigan Museum of Zoology Insect Collection (UMMZI), Ann Arbor, MI, holds over 4,000 bee specimens from the historical collections at the ESGR, and specimens were databased as part of this study. Historical data were checked for entry errors and outdated taxonomies. Specimens with questionable species determinations were re-examined and re-identified using relevant keys (see above) where possible. Bees that could not be confidently identified to the species level were excluded from the dataset, and entries that were missing the date of collection were also removed. Excluded entries accounted for less than 1% of the specimens. There were notable gaps in records at the ESGR, as there were no focused survey efforts since Evans’ last efforts in 1989, and only occasional specimen records from 1990-1999. There were no surveys and no records for the ESGR after 1999 and prior to this study in 2017/2018. All specimens from the ESGR were included in this dataset, not only those specifically collected at the Evans’ Old Field.In addition to the 4,000 plus records from the ESGR since 1921, we also include Evans’ dataset from his 1972 and 1973 collection effort. Evans’ original dataset from 1972/1973 was available through UM records. The dataset is unique compared to the records from the museum, because Evans did not always collect observed bees if he was confident in their identification (especially Bombus spp. and oligolectic species, e.g., Andrena rudbeckiae Robertson, 1891 and Dufourea monardae (Viereck, 1924)), and these records come only from the site now called Evans’ Old Field, whereas the exact sampling locations within the ESGR of many other specimens in the collection are not known. Therefore, his original dataset provides a more complete representation of the community he encountered at the Evans’ Old Field location.Evans describes his sampling as: “records of the dates and duration of flowering were made at frequent intervals (2-3 days every week) throughout the flowering season…Observation of visitation by bees was usually made between 9:00 am and 4:00 pm and on any given day was limited to a maximum of 30-40 minutes per flower species…no orderly system of monitoring was developed. More attention was given to abundant resources when they were being heavily visited than was paid to them near the beginning or end of their flower periods or to less frequently encountered species".Please open the README file first, which has descriptions of each included data file.

This Web Map is a subset of Above and below ground biomass carbon (tonnes/ha)This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.This layer supports analysis but, if needed, a direct download of the data can be accessed here.The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).DatasetScopeYearResolutionDefinitionSantoro et al. 2018Global2010100 mAbove-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.Xia et al. 2014Global1982-20068 kmAbove-ground grassland biomass.Bouvet et al. 2018Africa201025 mAbove-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.Spawn et al. 2017Global2010300 mSynthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.This dataset has not been validated.

1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.

2. We investigated mixture effects on the productivity of Pinus ponderosa, Pseudotsuga menziesii, and Abies concolor. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.

3. We found support for a positive mixture effect in P. menziesii, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of P. menziesii neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in P. menziesii does not necessarily extend to the stand level. We found no evidence for mixture effects in P. ponderosa, A. concolor or their neighboring species.

4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.

Methods Data for this project were collected by the United States Department of Agriculture, Forest Serivce, Forest Inventory and Analysis Program. These data were collected under a common sampling and measurement protocol, as fully detailed in this agency document valid through the date of database queries: https://www.fs.fed.us/rm/ogden/data-collection/pdf/P2%20Manual_70_Feb2sm.pdf. These data are part of the Public Domain and freely available.

We have provided a workflow for querying FIA plots, applicable both to individual U.S. states as well as the aggregated regional dataset. An SQL script was designed for use with postgreSQL. Many researchers may opt instead to query and summarize the FIA Database with the rFIA R package (Stanke and Finley, 2020; https://cran.r-project.org/web/packages/rFIA/index.html), which does not require SQL querying or other database skills. Please see the link to this R package in "Related Works."

We have also provided the R script for building the analysis files based on our flattened data query. This script details our data filtering steps, such as dropping distrubed or multi-stand plots, not performed in the SQL query. This annotated file details the process metadata used to calculate derived data fields such as competition index and potential evapotranspiration.

Lastly, performing the same query on more up-to-date versions of the FIA database will yield subtly different results. The FIA dataset is continuously updated, correcting minor errors as well as adding additional plot measurements. Based on our criteria, disturbance, harveting, or conversion to non-forest land would be grounds for dropping plots from inclusion in analysis.

This data release consists of a compilation of previously published mineral potential maps that were used for the Sagebrush Mineral-Resource Assessment (SaMiRA) project. This information was used as guides for assessing mineral potential assessment of approximately 10 million acres in Idaho, Montana, Nevada, Utah, and Wyoming. Specifically, the compilation was used to identify the deposit types to be assessed and the deposit models to develop. The data release consists of georeferenced images of mineral potential maps and vector shapefiles of mineral potential tracts. The georeferenced images are presented in two formats: 1) as images within raster mosaic datasets in Esri geodatabases, and 2) as individual tiff images with an accompanying .csv data table. There are four geodatabases containing the raster mosaic datasets, one for each of the four SaMiRA report areas: North-Central Montana; North-Central Idaho; Southwestern and South-Central Wyoming and Bear River Watershed; and Nevada Borderlands. Tract map images are from BLM and Forest Service wilderness study summary reports, along with multiple other mineral potential reports that were done under the USGS CUSMAP program and for USGS assessments of USGS National Forests. The georeferenced images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text was imported into the raster mosaic dataset database as ‘Footprint’ layer attributes. This data is also included as a .csv table, which can be used in conjunction with the individual georeferenced tiff images. The data compiled into the tables contains the figure caption from the original map, online linkage to the source report when available, and information on the assessed commodities according to the legal definition of mineral resources—metallic, non-metallic, leasable non-fuel, leasable fuel, geothermal, paleontological, and saleable. The shapefiles were compiled from datasets which had different data structure schemes and which used two different types of assessment methodology. The BLM used qualitative categorical and others used the USGS quantitative 3-part form of assessment. The original GIS data was re-formatted so that all of the shapefiles had one of two consistent attribute table structures, one for reports that had quantitative data, and one for reports with qualitative data. A general attribute table structure was created which contained fields for information on the deposit type assessed, assessment rank, type of assessment, and tract name and identifier. For the attribute table of the quantitatively assessed reports which used the USGS 3-part form of assessment, we added additional fields for the deposit model name and number, probabilistic assessment results data, and estimators. We captured the original information as presented but also standardized nomenclature when we could and referred to the report text in some instances in order to fill in missing data into the descriptive data tables.

This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.This layer supports analysis but, if needed, a direct download of the data can be accessed here.

The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.

Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).

Dataset

Scope

Year

Resolution

Definition

Santoro et al. 2018

Global

2010

100 m

Above-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.

Xia et al. 2014

Global

1982-2006

8 km

Above-ground grassland biomass.

Bouvet et al. 2018

Africa

2010

25 m

Above-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.

Spawn et al. 2017

Global

2010

300 m

Synthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.

After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.

Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.This dataset has not been validated.

The maps in this dataset were produced from existing datasets to determine the climate mitigation potential of reforestation in Southeast Asia under various constraints, namely biophysical, financial, land-use and operational constraints through to the year 2030. This was done for three main forest types: peatswamp, mangrove and terrestrial forests. All calculations were based on data dated between 2013–2019 and at a resolution of 0.01 degrees (~1 km).

Biophysical constraints. Biophysical constraints were firstly determined by identifying degraded forest areas: maximum threshold of 35 MgCha^-1 above-ground carbon for terrestrial forests^1,2, indications of clearings for peatswamp forests^3,4 and changes in Landsat pixels over time for mangrove forests⁵ from a pantropical above-ground carbon layer⁶. We then focus on degraded areas that are low in biomass due to natural biophysical settings, by masking out ‘forest’ or ‘woodland’ areas that were previously identified as degraded from the Potential Natural Vegetation (PNV) map⁷. We also masked out current landcover areas that would preclude reforestation, such as bare ground, industrial land, large scale agriculture, water and urban areas^8,9. Lastly, we estimated the climate mitigation potential of each raster cell in the biophysical constraint layer based on the different forest types and subtypes according to the PNV map and IPCC classifications^3,5,7,10. This was calculated as the sum of carbon dioxide likely to be sequestered due to aboveground biomass growth and avoided business-as-usual (BAU) flux annualised to 2030 (see Table S3 for details and key references). Climate mitigation potential for areas of smallholder agriculture – defined as agricultural areas of less than 2 ha – identified within the layer nevertheless, were taken as forests and its carbon gain was calculated as the difference between croplands and natural forests¹¹.

Financial constraints. Financial constraints were determined by two components: direct cost of reforestation and the opportunity cost based on revenue lost from agricultural production. Direct costs of reforestation (including planning, planting and maintenance) across Southeast Asia were specified by forest type^12,13 and adjusted to each country based on relative hourly wages¹⁴ and gross domestic product per capita¹⁵. The opportunity cost based on revenue lost from agricultural production in Southeast Asia were derived from spatially explicit crop rents of the 17 most economically important crops based on production in 2017, considering only crops produced in >1% of the country’s land area¹⁶. The maximum crop rent for each cell was then identified, indicating the maximum agriculture revenue lost due to reforestation. All costs were adjusted to 2018 USD. The low estimate of reforestation costs was based purely on direct cost. The moderate estimate was based on both direct and opportunity cost from foregone agricultural rent weighted by crop development potential index¹⁷. The high estimate was based on the direct and full opportunity cost. We thus calculated the cost of reforestation per ton of carbon dioxide equivalent mitigated, utilising the biophysical constraints layer and omitting all areas > 100 USD MgCO₂e^-1 to limit reforestation to cost-effective areas ^18,19,20.

Land-use constraints. There are two levels of land-use constraints: more permissive one, which only excluded reforestation on smallholder agriculture lands (any raster cell that possessed agriculture lands ≤ 2 ha) with high estimated yield¹⁷, and a less permissive one which excluded reforestation on all smallholder agriculture lands.

Operational constraints. Four operational constraints were applied to account for the practical considerations that may influence the long-term viability of reforested sites. These include proximity to seed sources (SS), protection status (PA), deforestation risk (DR) and accessibility for monitoring and management (AM). SS was determined by utilising a 2-km buffer from the nearest existing forest edge as a proxy for propagule sources^21-24 to support natural regeneration. Reforestation and thus climate mitigation potential is thus constrained to areas in relative proximity to seed sources. For PA, we constrained reforestation to legally protected areas²⁵, namely those of IUCN categories I-VI, estimating the climate mitigation potential in areas with some form of protection status. For DR, we constrained reforestation to areas with acceptable likelihood of transition to deforested areas i.e. ≥ 0.5 probability of deforestation²⁶ (medium to high potential) from a spatially explicit layer predicting tree cover loss to 2029, estimating the climate mitigation potential in areas with acceptable deforestation risk. We also considered AM to account for the need for continued monitoring and management associated with post-planting site upkeep, thus, limiting reforestation areas to within a day’s travelling time to the nearest cities²⁷ and estimated the climate mitigation potential for these areas.

Uncertainties across estimations of climate mitigation potential were derived from the range of values associated with the aboveground carbon gain and the BAU flux reported in our literature review (see Table S3 for details), where the minimum and maximum climate mitigation potential across each forest type were calculated for each specific study^10,28 or collated across a number of studies^29-31. This produced a total of 111 maps, which represented the mean, minimum and maximum climate mitigation potential of each of the constrained reforestation estimations.

Further details for this dataset are presented in Zeng et. al.

Aims

We carried out an opportunistic correlative study between past crown conditions and current deadwood volumes.

Our aim was to mobilise available data on site factors and long-term monitoring of crown vitality indicators in Europe to investigate the influence of current and recent local defoliation levels on plot-level deadwood volume.

For a subset of level I, 16*16-km monitoring plots located throughout Europe, we benefitted from data on both (i) deadwood measurements carried out within the framework of the Forest Focus Biosoil Project (Galluzzi et al., 2019), pre-processed into a consistent and harmonized deadwood dataset by Puletti et al. (2019), and (ii) defoliation assessments provided yearly since 1989 by the International Co-operative Program on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests), the most comprehensive European monitoring network for the large-scale assessment of forest ecosystem health (Vitale et al., 2014).

Biosoil data on deadwood and ICP data on defoliation have never been crossed before.

We used defoliation level as a proxy for the severity of stand dieback. Deadwood patterns can be addressed through deadwood profiles, which subdivide local deadwood stocks into classes based on size, position and decay stage.

ICP database and defoliation protocol

The International Cooperative Program to assess and monitor air pollution effects on the forest (ICP Forests) is responsible for an extensive level I monitoring system of forest sites (Hauβmann & Fischer, 2004), which has been in operation since 1986. This large-scale level I network is made up of dense, spatially representative sampling points placed throughout European forests on a 16 × 16 km virtual grid, and is dedicated to monitoring forest conditions. The sampling points cover most European forested areas and encompasses ca. 6000 monitoring plots in 42 countries. In each plot, a visual evaluation of defoliation and discoloration of tree crowns is performed annually to survey forest health status (http://icp-forests.net/page/largescale-forest-condition). Data management is presently carried out at the Programme Co-ordinating Centre (PCC) of ICP Forests in Eberswalde, Germany, and all data are available upon request. Since 1989, a standardized procedure for “annual surveys of crown condition’’ has been applied to 24 selected dominant and co-dominant trees with a minimum height of 60 cm and showing no significant mechanical damage. The defoliation and discoloration level of each tree crown is visually assessed on a sliding scale of 5% increments as the percentage of needle/leaf loss in the assessable crown as compared to a reference tree with full foliage. Mean defoliation at the plot scale was defined as the proportion of “damaged” trees i.e., with a defoliation rate of more than 25%, and used as a proxy for plot decline level. In the ICP database, the factors associated with observed defoliation related to natural disturbances or management (i.e., vertebrate or insect herbivory, fungal or fire damage, drought impacts, signs of removal of coarse woody debris, past landscape) were not recorded in a sufficiently standardized way to be used as covariates in our models. Similarly, plot-level living tree density and above-ground biomass for standing living trees (expressed in kg.ha⁻¹), presumably surveyed in subplot 2, were not available.

Biosoil database and deadwood protocol

In the framework of the large collaborative European Forest Focus BioSoil-Biodiversity project, a system of circular concentric subplots was built around certain ICP level I plots to collect additional data on stand structure and biodiversity between 2005 and 2008 (Figure 1). The individual countries were responsible for selecting the ICP level I plots to be included in the BioSoil project (Galluzzi et al., 2019). Overall, a total of 3243 geocoded Level I plots were considered in 19 European countries (Puletti et al., 2017): Austria, Belgium (Flanders only), Cyprus, the Czech Republic, Denmark, Finland, France, Germany (eight federal states only), Hungary, Ireland, Italy, Latvia, Lithuania, Poland, Slovakia, Slovenia, Spain, Sweden and the United Kingdom (Figure 1). BioSoil project results are recorded in the multi-dimensional LI-BioDiv geodatabase that contains raw data on forest structure and vegetation records used to calculate simple plot-level structural and compositional forest variables (i.e., biomass, deadwood volume, plant alpha-diversity; Bastrup-Birk et al. 2007; Hiederer & Durant 2010). At each plot, deadwood was quantified on an area of 400 m² (BioSoil subplots 1 and 2, radius of 11.28 m; Puletti et al., 2017). The deadwood survey included coarse woody debris (including lying dead trees), snags (including standing dead trees) and stumps more than 10 cm in diameter. Only snags and stumps more than 130 cm in height were considered. Diameter, length or height, tree species and decay stage (5 classes) were recorded for each deadwood piece. The raw ICP deadwood data were processed by Puletti et al. (2017, 2019) into a consistent and harmonized pan-European deadwood dataset, which we used in this study. The dataset provides total deadwood volume and the volume of several deadwood types for each plot. Further details can be found in the ICP Forests manual (http://icp-forests.net/page/icp-forests-manual), Puletti et al. (2019) and Augustynczik et al. (2024).

In our study, we considered the following response variables: (i) total deadwood volume, (ii) standing deadwood (snags) volume, (iii) volume of ground-lying deadwood, (iv) fresh deadwood volume (= Vm3_dec1_Biosoil + Vm3_dec2_Biosoil), and (v) decayed deadwood volume = (= Vm3_dec4_Biosoil + Vm3_dec5_Biosoil).

A few environmental covariates were collected from the Biosoil data: (i) management intensity (grouped into two classes: recently harvested, i.e., with management evidence within the last 10 years; and not recently harvested, i.e., unmanaged (no management evidence) or managed a long time ago (management evidence but more than 10 years previously), (ii) average stand age (separated into 3 classes: mature [>100 yrs], mid-aged [41-100 yrs], young [1-40 yrs]), (iii) elevation (above sea level, a.s.l.), a continuous quantitative variable, (iv) dominant tree genus, and (v) forest type, depending on the dominant tree species: coniferous, deciduous or mixed.

Database joint: plot matching in time series

After harmonizing plot names and coordinates in the two datasets (ICP-defoliation and Biosoil-deadwood), only plots with matched data in both datasets were selected. Plots with a maximum of one year’s discontinuity in the data were retained, and the missing values were reconstructed from the average values in contiguous years. Plots with discontinuities in defoliation measurements of more than 2 years were deleted. We matched defoliation measurements for the Biosoil-ICP datasets from 1989 to 2007 and finally obtained 2,070 five-year, 1,804 ten-year and 1,399 fifteen-year time series. This approach made it possible to define three 10-year time series [1995-2005, 1996-2006, 1997-2007] with plots in 17 countries, from five plots in Ireland and nine in the United Kingdom, to 337 plots in Finland and 461 in France.

Calculation of global defoliation metrics

We calculated 16 univariate metrics to summarize changes in defoliation throughout the 10-year period prior to the Biosoil deadwood measurements. Some of the selected parameters describe the immediate possible effects of defoliation severity in the recent past on a given year: (i) defoliation level of the previous year (n-1), (ii) defoliation level of the year before the previous year (n-2), (iii) defoliation level of the year two years before the previous year (n-3). Other defoliation metrics relate to the cumulative effects of defoliation levels in the near or the distant past: (i) average defoliation level over the last two years, (ii) average defoliation level over the last three years, (iii) average defoliation level over the last five years, (iv) average defoliation level over the first five years of the 10-year time series, and (v) time elapsed since last peak defoliation. Several other parameters depict general trends in the level of defoliation over the 10-year time series: for cumulative metrics: (i) arithmetic mean of annual defoliation level; (ii) geometric mean of annual defoliation level;

The base data set used in this forest fragmentation analysis is the 2010 C-CAP Land Cover Analysis (http://http://coast.noaa.gov/ccapftp/). Land cover categories that were considered 'forest' for this analysis include Deciduous Forest, Evergreen Forest, Mixed Forest, Estuarine Forested Wetland, and Palustrine Forested Wetland. Two buffered roads layers were erased from the forest polygons, in order to approximate the fragmenting effect of roads on the landscape. Because the area of interest crosses the boundaries of multiple states, the ESRI North America Detailed Streets layer (http://www.arcgis.com/home/item.html?id=f38b87cc295541fb88513d1ed7cec9fd) was used. Two selections of the roads data were extracted and buffered: Interstate roads were buffered 150 feet from the center line in both directions, while US, State, and County roads were buffered 33 feet from the center line. The final data set is limited to forest patches falling within a 5 mile radius of either the Hudson River Estuary watershed boundary or the 10 counties of New York's Hudson Valley.The accompanying symbology layer divides forests into four size classes following the Orange County Open Space Plan (Orange County Planning Department 2004): Globally important (greater than 15,000 acres). These large and intact forest ecosystems support characteristic, wide-ranging, and area-sensitive species, especially those that depend on interior forest. Globally important forests are large enough so over time they will express a range of forest successional stages including areas that have been subjected to recent large-scale disturbance such as blowdowns and fire, areas under recovery, and mature areas. These forests also provide sufficient area to support enough individuals of most species to maintain genetic diversity over several generations. Regionally important: (6,000 - 14,999 acres). Patches 6,000 acres and greater provide habitat to more area-sensitive species and can accommodate large-scale disturbances that maintain forest health over time. Smaller patches are often less able to maintain the entire range of needed habitats and successional stages after large-scale disturbances. Locally important: 2,000 – 5,999 acres). These smaller but locally important forest ecosystems often represent the lower limit of intact, viable forest size for forest-dependent birds. Such bird species often require 2,500 to 7,500 acres of intact interior habitat. These forests, like the larger regionally important forests, can also provide important corridors and connectivity among forest ecosystems. Stepping stone forests: (200 – 1,999 acres) These examples of smaller forest ecosystems provide valuable, relatively broad corridors (not just a narrow strip) and links to larger patches of habitat such as local, regional, and global forests. These smaller forests, therefore, enable a large array of species, including wide-ranging and area-sensitive species, to move from one habitat to another across an otherwise hostile and fragmented landscape. They also provide important habitat at key times during many animals’ life cycles. These forests should be considered the absolute minimum size for intact forest ecosystems. Forests as small as 200 acres will support some forest interior bird species, but several may be missing, and species that prefer “edge” habitats will dominate. Forest patches less than 200 acres have lesser ecological significance at the landscape scale and were excluded from the symbology layer, However, smaller forests may have local importance, and can be viewed by changing the symbology settings.

© Cornell University Department of Natural Resources 2014. This Project was funded by the New York State Environmental Protection Fund through the Hudson River Estuary Program of the New York State Department of Environmental Conservation. This layer is sourced from giswww.westchestergov.com.

In-stream wood jams alter their surrounding channel morphology, thus setting riverscape morphology and ecosystem function. While wood jams clearly scour pools, retain sediment, and influence bank erosion, there is a lack of observational evidence of how wood jam characteristics themselves control morphologic effects. Here, I analyzed field observations of hundreds of wood jams to test the hypothesis that wood jam characteristics can predict local morphologic effects such as sediment retention, bar forcing, and pool scour. I found mixed support for this hypothesis: while jam characteristics such as porosity, channel blockage ratio, thalweg occupation, and having rootwads and multiple trunks significantly predicted morphologic effect occurrence and magnitude, they only explained a small portion of the variance in those morphologic effects. While wood jam characteristics are relevant in controlling their overall morphologic functions, those functions are both inherently variable and likely..., , , ## Data from: Wood jam characteristics influence but do not fully explain wood jam morphologic functions

This dataset describes wood jam characteristics and morphologic effects across 6 study sites in the western United States.

Description of the Data and file structure

Data are shown in the wood_morph_dataset.csv file. Metadata are shown in the wood_morph_dataset_metadata.csv. The Metadata describe each variable in detail. Missing data are marked as "NA" cells.

This dataset supersedes the version in https://doi.org/10.25909/5e93ff29cd66b. Added to version 2 is the R script that runs the reforestation scenarios for the study. Version 3 contains an updated landuse constraint layer. The maps in this dataset were produced from existing datasets to determine the climate mitigation potential of reforestation in Southeast Asia under various constraints, namely biophysical, financial, land-use and operational constraints through to the year 2030. This was done for three main forest types: peatswamp, mangrove and terrestrial forests. All calculations were based on data dated between 2013–2019 and at a resolution of 0.01 degrees (~1 km). Biophysical constraints. Biophysical constraints were firstly determined by identifying degraded forest areas: maximum threshold of 35 MgCha-1 above-ground carbon for terrestrial forests1,2, indications of clearings for peatswamp forests3,4 and changes in Landsat pixels over time for mangrove forests5 from a pantropical above-ground carbon layer6. We then focus on degraded areas that are low in biomass due to natural biophysical settings, by masking out ‘forest’ or ‘woodland’ areas that were previously identified as degraded from the Potential Natural Vegetation (PNV) map7. We also masked out current landcover areas that would preclude reforestation, such as bare ground, industrial land, large scale agriculture, water and urban areas8,9. Lastly, we estimated the climate mitigation potential of each raster cell in the biophysical constraint layer based on the different forest types and subtypes according to the PNV map and IPCC classifications3,5,7,10. This was calculated as the sum of carbon dioxide likely to be sequestered due to aboveground biomass growth and avoided business-as-usual (BAU) flux annualised to 2030 (see Table S3 for details and key references). Climate mitigation potential for areas of smallholder agriculture – defined as agricultural areas of less than 2 ha – identified within the layer nevertheless, were taken as forests and its carbon gain was calculated as the difference between croplands and natural forests11. Financial constraints. Financial constraints were determined by two components: direct cost of reforestation and the opportunity cost based on revenue lost from agricultural production. Direct costs of reforestation (including planning, planting and maintenance) across Southeast Asia were specified by forest type12,13 and adjusted to each country based on relative hourly wages14 and gross domestic product per capita15. The opportunity cost based on revenue lost from agricultural production in Southeast Asia were derived from spatially explicit crop rents of the 17 most economically important crops based on production in 2017, considering only crops produced in >1% of the country’s land area16. The maximum crop rent for each cell was then identified, indicating the maximum agriculture revenue lost due to reforestation. All costs were adjusted to 2018 USD. The low estimate of reforestation costs was based purely on direct cost. The moderate estimate was based on both direct and opportunity cost from foregone agricultural rent weighted by crop development potential index17. The high estimate was based on the direct and full opportunity cost. We thus calculated the cost of reforestation per ton of carbon dioxide equivalent mitigated, utilising the biophysical constraints layer and omitting all areas > 100 USD MgCO2e-1 to limit reforestation to cost-effective areas18,19,20. Land-use constraints. There are two levels of land-use constraints: more permissive one, which only excluded reforestation on smallholder agriculture lands (any raster cell that possessed agriculture lands ≤ 2 ha) with high estimated yield17, and a less permissive one which excluded reforestation on all smallholder agriculture lands. Operational constraints. Four operational constraints were applied to account for the practical considerations that may influence the long-term viability of reforested sites. These include proximity to seed sources (SS), protection status (PA), deforestation risk (DR) and accessibility for monitoring and management (AM). SS was determined by utilising a 2-km buffer from the nearest existing forest edge as a proxy for propagule sources21-24 to support natural regeneration. Reforestation and thus climate mitigation potential is thus constrained to areas in relative proximity to seed sources. For PA, we constrained reforestation to legally protected areas25, namely those of IUCN categories I-VI, estimating the climate mitigation potential in areas with some form of protection status. For DR, we constrained reforestation to areas with acceptable likelihood of transition to deforested areas i.e. ≥ 0.5 probability of deforestation26 (medium to high potential) from a spatially explicit layer predicting tree cover loss to 2029, estimating the climate mitigation potential in areas with acceptable deforestation risk. We also considered AM to account for the need for continued monitoring and management associated with post-planting site upkeep, thus, limiting reforestation areas to within a day’s travelling time to the nearest cities27 and estimated the climate mitigation potential for these areas. Uncertainties across estimations of climate mitigation potential were derived from the range of values associated with the aboveground carbon gain and the BAU flux reported in our literature review (see Table S3 for details), where the minimum and maximum climate mitigation potential across each forest type were calculated for each specific study10,28 or collated across a number of studies29-31. This produced a total of 111 maps, which represented the mean, minimum and maximum climate mitigation potential of each of the constrained reforestation estimations. Four reforestation scenarios were then analysed using the derived outputs, namely 1) an independent scenario where each constraint is considered separately 2) full contingent scenario with all constraints are sequentially applied, 3) moderate contingent scenario 1, where we consider a moderate cost estimate, and 4) moderate contingent scenario 2 which applies a more permissive land-use constraint.Further details for this dataset are presented in Zeng et. al.

These data are those used in the article "Linking radial growth patterns and moderate-severity disturbance dynamics in boreal old-growth forests driven by recurrent insect outbreaks: a tale of opportunities, successes, and failures" published in Ecology and Evolution by Martin, M., Krause, C. & Morin H.

The dataset is divided into two parts: field survey data (Dataset_Field_Survey_Martin_et_al_2020_Ecology_and_Evolution.txt) and dendrochronological data (Dataset_Dendrochron_Martin_et_al_2020_Ecology_and_Evolution.txt). An ID has been assigned to each tree and each site to facilitate navigation between the two datasets. The name of each variable and each class has been made to be as self-explanatory as possible. For more details on the sampling method, please refer to the article.

Missing values are presented as "NA."

If you have any questions or problems, please do not hesitate to contact the authors.

This dataset contains descriptions of unidentified remains whose cases have been processed by the Medical Examiner’s Office.

Call 312-666-0500 to speak to Deputy Chief Investigator, Earl Briggs, about matching one of these unidentified bodies to the identity of a missing person. Descriptions of cases can also be found at NAMUS.gov

Please note that images posted in this section may be graphic in nature and may not be appropriate for all users.

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The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.