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1. Burn Study Sites Seasonal Biomass and Seasonal and Annual NPP Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico

   https://doi.org/10.6073/pasta/d3ed80e070b9afc7e4e4c1c77cc140fb  

   Baur, Lauren; Collins, Scott; Muldavin, Esteban; Rudgers, Jennifer A; Pockman, William T. (January 2021, Environmental Data Initiative)

   {"Abstract":["In 2003, the U.S. Fish and Wildlife Service conducted a prescribed\n burn over a large part of the northeastern corner of the Sevilleta\n NWR. This study was designed to look at the effect of fire on\n above-ground net primary productivity (ANPP) within different\n vegetation types. Net primary production (NPP) is a fundamental\n ecological variable that measures rates of carbon consumption and\n fixation. Estimates of NPP are important in understanding energy\n flow at a community level as well as spatial and temporal responses\n to a range of ecological processes. While measures of both below-\n and above-ground biomass are important in estimating total NPP, this\n study focuses on above-ground net primary production (ANPP).\n Above-ground net primary production (ANPP) is equal to the change in\n plant mass, including loss to death and decomposition, over a given\n period of time. To measure this change, ANPP is sampled twice a year\n (spring and fall) for all species in each of three vegetation types.\n In addition, volumetric measurements are obtained from adjacent\n areas to build regressions correlating biomass and volume. Three\n vegetation types were chosen for this study: mixed grass (MG), mixed\n shrub (MS) and black grama (G). Forty permanent 1m x 1m plots were\n installed in both burned and unburned sections of each habitat type.\n The core black grama site included in SEV129 was incorporated into\n this dataset as an unburned control, so an additional unburned G\n site was not created. The data for this site is noted as site=G and\n treatment=C (i.e., control). The original mixed-grass unburned plot\n caught fire unexpectedly in the fall of 2009 and was subsequently\n moved to the south. Volumetric measurements are made using\n vegetation data from permanent plots collected in SEV156, "Burn\n Study Sites Quadrat Data for the Net Primary Production Study"\n and regressions correlating biomass and volume constructed using\n seasonal harvest weights from SEV157, "Net Primary Productivity\n (NPP) Weight Data.""]} 

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2. Data from: Restoration fosters later-successional seed rain, yet large seeds remain scarce after two decades

   https://doi.org/10.5061/dryad.cc2fqz6mt  

   San-José, Miriam; Werden, Leland; Holl, Karen; Rosales, Juan; Zahawi, Rakan (January 2026, Dryad)

   {"Abstract":["These datasets contain seed rain and fruiting trees data collected from a\n long-term tropical forest restoration experiment in southern Costa Rica.\n Seed sampling was conducted across seven experimental sites, each\n including three restoration treatments (natural regeneration, applied\n nucleation, and plantation), as well as four nearby reference forest\n sites. Within each plot, 16 seed traps (0.5 m², 1 mm mesh) were installed\n and monitored over a two-year period (March 2021–February 2023), with\n seeds collected twice monthly. All seeds ≥1 mm were counted and identified\n to the lowest possible taxonomic level using a reference collection,\n resulting in 142,501 seeds from 104 later-successional species across 41\n families. For each record, associated information includes trap identity\n and coordinates, site and treatment, species identity, dispersal mode,\n growth form, and seed size. Species were classified into successional\n categories (mid- or late-successional), and seed size was recorded or\n categorized. Additional data include observations of fruiting trees within\n plots to assess potential local versus external seed sources."],"TechnicalInfo":["# Data from: Restoration fosters later-successional seed rain, yet large\n seeds remain scarce after two decades Dataset DOI:\n [10.5061/dryad.cc2fqz6mt](https://doi.org/10.5061/dryad.cc2fqz6mt) ##\n Description of the data and file structure These datasets contain seed\n rain and fruiting trees data collected from a long-term tropical forest\n restoration experiment near Agua Buena, Puntarenas, Costa Rica. See the\n Islas Project for more information\n ([http://www.holl-lab.com/islas-project.html](http://www.holl-lab.com/islas-project.html)) Seed sampling was conducted across seven experimental sites, each site contained three 0.25-ha areas with a tree plantation, natural regeneration, and an applied nucleation restoration plot. Additionally, four mature forests nearby the restoration sites are used as reference forest. Within each plot, 16 seed traps (0.5 m², 1 mm mesh) were installed and monitored over a two-year period (March 2021–February 2023), with seeds collected twice monthly. All seeds ≥1 mm were counted and identified to the lowest possible taxonomic level using a reference collection, resulting in 142,501 seeds from 104 later-successional species across 41 families. For each record, associated information includes trap identity and coordinates, site and treatment, species identity, dispersal mode, growth form, and seed size. Species were classified into successional categories (mid- or late-successional), and seed size was recorded or categorized. Tree species’ successional stage was determined based on the expert opinion of two taxonomists who have each worked in the region for >20 years, herbarium specimens at Las Cruces, and literature resources (Chazdon et al. 2011 A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332–1343. De Souza, 2001. Seed size, seed germination, and seedling survival of Brazilian tropical tree species differing in successional status. Biotropica 33, 447–457. Werden et al. 2020 Effects of dispersal- and niche-based factors on tree recruitment in tropical wet forest restoration. Ecol. Appl. 30, e02139). Finally, we recorded data on which trees were flowering or fruiting in restoration plots from January 2021 to April 2023. Observations were made during seed collection fieldwork and during bird surveys (4 times per year). ### Files and variables #### File – Seed_rain_data.csv Primary dataset containing seed counts at the trap level. Each row represents a sampling event for a given seed trap. It also contains the species-level trait dataset used to assign ecological categories. Key variables include: *Site* Two-letter identifier of the 7 restoration sites used in the study. At four of them (*), a nearby mature forest was selected as a reference forest. These acronyms are mostly based on the original landowner names: BB = Bambu EC = El Cenizo JG = Julio Gonzalez* LL = Loma Linda* MM = Melissa's Meadow* OM = Omar* SG = San Gabriel *Treatment* Restoration treatment: NR = natural regeneration AN = applied nucleation PL = plantation RF = reference forest *Trap* Number of the trap where the seeds were collected (1 to 16) *Species* Species name 6-letter code: first three letters for genus, followed by the three first letters of the species (GEN_SPP). *succ_stage* Early- mid- or late-successional species according to expert opinion, herbarium specimens at Las Cruces, and literature resources. *disp_mode* Dispersal syndrome: ZOO = animal-dispersed WIND = wind-dispersed GRAV = barochory EXPL = explosive dispersal *grow_form* Growth form of the seed species: UP = understory palm CP = canopy palm ET = emergent tree CT = canopy tree UT = understory tree Shrub Liana Epiphyte *Forbs were not taken into account due to the height of our traps (>1 m from the ground) *seed_size* Size classes according to seed width: 1: <5 mm 2: 5 < 10 mm 3: 10 - 15 mm 4: >15 mm *seed_width* Seed size in mm measured or assigned based on the literature *seed_sum* Sum of the number of seeds recorded in each trap along two sampling years. Missing values are indicated as "NA". **File – Fruiting_tree_data.csv** Records of fruiting individuals within plots are used to infer potential local seed sources. Key variables include: *Date* Date of observation dd-month-yy *Site* Two-letter identifier of the 7 restoration sites used in the study. At four of them (*) a nearby mature forest was selected as a reference forest. These acronyms are mostly based on the original landowner names: BB = Bambu EC = El Cenizo JG = Julio Gonzalez* LL = Loma Linda* MM = Melissa's Meadow* OM = Omar* SG = San Gabriel *Treatment* Restoration treatment: NR = natural regeneration AN = applied nucleation PL = plantation RF = reference forest *Species* Species name 6-letter code: first three letters for genus followed by the three first letters of the species (GEN_SPP). *Family* Taxonomical family of the species *Genus* Taxonomical genus of the species *Specific.Epithet* Epithet of the species *Authority* Author(s) of the species name *grow_form* Growth form of the seed species: UP = understory palm CP = canopy palm ET = emergent tree CT = canopy tree UT = understory tree Shrub Liana Epiphyte *Forbs were not taken into account due to the height of our traps (>1 m from the ground) *disp_mode* Dispersal syndrome: ZOO = animal-dispersed WIND = wind-dispersed GRAV = barochory EXPL = explosive dispersal *succ_stage* Early- mid- or late-successional species according to expert opinion, herbarium specimens at Las Cruces, and literature resources. *seed_size* Size classes according to seed width: 1: <5 mm 2: 5 < 10 mm 3: 10 - 15 mm 4: >15 mm *seed_width*_mm Missing values are indicated as "NA". **File – Trap_location.csv** *Site* Two-letter identifier of the 7 restoration sites used in the study. At four of them (*) a nearby mature forest was selected as a reference forest. These acronyms are mostly based on the original landowner names: BB = Bambu EC = El Cenizo JG = Julio Gonzalez* LL = Loma Linda* MM = Melissa's Meadow* OM = Omar* SG = San Gabriel *Treatment* Restoration treatment: NR = natural regeneration AN = applied nucleation PL = plantation RF = reference forest *Trap* Number of the trap where the seeds were collected (1 to 16) *Latitude*, *Longitude*: Geographic coordinates of each trap in decimal degrees (WSG86/UTM zone 17 N) ## Code/software Excel (Microsoft Office 2016) Data processing involved several steps to prepare the dataset for analysis. Early-successional species and selected taxonomically ambiguous groups were excluded to focus on mid- and late-successional species relevant to forest recovery. Seed deposition rate (SDR) was calculated at the seed-trap level as the total number of seeds divided by trap area and sampling duration (seeds m⁻² yr⁻¹), and values were log-transformed to account for high variance in seed counts. Species were grouped by successional stage and seed size (small <5 mm; large ≥5 mm), and dispersal modes and growth forms were assigned based on previously published trait databases. Spatial coordinates were used to calculate distances among traps and account for spatial autocorrelation in subsequent analyses. Species-by-trap matrices were constructed for diversity and composition analyses, and additional datasets were merged (e.g., fruiting tree records) to classify seeds as locally produced or externally dispersed at the plot (treatment within a site) level."]} 

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3. Data from: Lower-intensity restoration interventions drive greater seedling establishment for later-successional tree species

   https://doi.org/10.5061/dryad.cfxpnvxjp  

   Joyce, Francis H; Zahawi, Rakan A; Holl, Karen D (January 2025, Dryad)

   Recovery of tree community composition in restored tropical forests relies on successful recruitment of later-successional species. However, the long-term effects of different restoration interventions on establishment success of arriving seeds are poorly understood. We evaluated the effects of three restoration treatments on the seed-to-seedling transition for later-successional tree species in a fragmented agricultural landscape in southern Costa Rica. Restoration plots (0.25 ha) were established in a block design nearly two decades prior and spanned a gradient of intervention intensity: natural regeneration (not planted), applied nucleation (planted tree clusters), and plantation (fully planted). We conducted seed addition experiments from 2021-2023 using eight species at seven replicate restoration sites and in four nearby remnant forests. Study region We conducted this study from June 2021 to September 2023 in southern Costa Rica near Las Cruces Biological Station. The forests in this region are at the boundary between Tropical Premontane Wet and Rain Forest zones (Holdridge et al., 1971), and the study sites range from ~1100 to 1200 m in elevation. Soils are volcanic in origin, mildly acidic (pH ~5.5), low in P, and high in organic matter. Mean annual temperature is ~21°C, and annual precipitation is between 2700 and 5000 mm, with a dry season from December to March. The landscape was largely deforested between 1960 and 1980 and is now a fragmented mosaic of remnant forest, secondary forest, cattle pastures and agricultural fields. Design of long-term restoration experiment We used seven restoration sites set up in 2004-2006 (Figure 1), a subset of the 15 sites described in Holl et al. (2020). All sites were previously used for ≥ 18 years for grazing or coffee production, and most are steeply sloped (15-35º). Minimum distance between sites is 700 m. Each restoration site had three 50 × 50 m (0.25 ha) plots: natural regeneration, applied nucleation, and plantation. Natural regeneration plots were not planted, whereas applied nucleation and plantation plots were planted with seedlings of four tree species: Erythrina poeppigiana (Walp.) Skeels, Inga edulis Mart., Terminalia amazonia (J.F. Gmel.) Exell, and Vochysia guatemalensis Donn. Sm. E. poeppigiana and I. edulis are naturalized, fast-growing N-fixers commonly used in agricultural intercropping, whereas T. amazonia and V. guatemalensis are native timber species. In applied nucleation plots trees were planted in six nuclei or patches, two each of 4 × 4, 8 × 8, and 12 × 12 m, whereas plantation plots were planted uniformly throughout; the number of trees planted in applied nucleation plots was 27% of the total in plantation plots. Four sites also have paired remnant forest (2 to >300 ha) located 5-50 m from restoration plots, which serve as a reference system for comparing the conditions and ecological processes of restoration plots. On average, percent canopy cover was lower and cover of exotic pasture grasses was much greater in natural regeneration than planted treatments (Kulikowski et al., 2023). Seed addition experiment We selected eight tree species (Table 1; Figure S1) for seed addition experiments, based on: (a) their later-successional status, categorized by expert opinion as occurring exclusively in late-successional forest or both early- and late-successional forest (Schubert et al., 2025); (b) a continuum of seed sizes; and (c) regional availability of seeds (>700) for the experiment. Study species were all animal-dispersed and ranged from 2.4 to 24.8 mm seed width. We collected seeds from at least three mother trees per species, encountered in conditions that differed by species: (1) mature fruits/nuts from the ground (Pseudolmedia mollis, Quercus benthamii, Trophis mexicana, and some Erythroxylum macrophyllum and Otoba novogranatensis), (2) seeds with pulp already removed by birds from the ground (Ocotea puberula and some O. novogranatensis), and (3) ripe fruit directly from trees (Lacistema aggregatum, Palicourea padifolia, some E. macrophyllum). We manually removed any fruit pulp from seeds and briefly submerged all seeds in water to check for insect damage. Seeds that floated were assumed to be non-viable and not used. We measured fresh mass and width for ≥50 seeds per species (Table 1; Figure S1). We thoroughly mixed conspecific seeds to avoid bias in seed source or quality among sites or habitat types (hereafter ‘treatments’). We conducted concurrent germination trials to confirm that no species completely failed to germinate in a nursery setting. Within each restoration plot and remnant forest, we established four sets (hereafter ‘stations’) of three 1-m² quadrats separated by ≥15 m. In natural regeneration, plantation, and reference forest plots, one station was located in each quarter of the plot (Figure 2a). In applied nucleation plots, stations were systematically distributed with one station at each of four positions relative to the initial planting design (Figure 2b): (1) within a large nucleus; (2) at the edge of a medium nucleus; (3) between two nuclei, and (4) far from any nucleus. Nuclei have spread considerably beyond the planted area due to crown expansion and natural recruitment; however, these positions spanned a representative range of microsite conditions. Two quadrats per station were used for seed additions to accommodate all eight species, and one non-sown control quadrat was surveyed to assess background abundance of naturally recruited seedlings of the study species (Figure 2c). Each control quadrat was located 2 m from a seed addition quadrat and on the uphill side if on a slope. We placed quadrats to avoid large tree trunks but did not remove existing vegetation or leaf litter. At each station, we sequentially added seeds of three species (P. mollis, O. novogranatensis, and O. puberula) to one quadrat (26 total seeds m⁻²) and five species (Q. benthamii, E. macrophyllum, L. aggregatum, T. mexicana, and P. padifolia) to the second quadrat (47 total seeds m⁻²) as seeds of each species became available between July 2021 and August 2022. The number of seeds added per quadrat ranged from 7 to 11 seeds per species (Table 1) because of varying seed availability. Realized densities of total added individuals were lower than the cumulative densities because of (a) temporal staggering of additions by species and (b) seed mortality. We placed seeds on top of the litter or soil to simulate how seeds would naturally be deposited from primary dispersal, slightly pressing down to minimize rolling on steeper slopes. We secured a roll of fine mesh ~5 cm high on the downhill side of each seed addition quadrat to catch seeds washed downslope by runoff. Seeds were placed in rows with 10-20 cm minimum spacing and assigned an identifier based on grid position. For the five smallest-seeded species, locations were marked with popsicle sticks to facilitate monitoring. We monitored seedling emergence, survival, and height (to nearest 0.5 cm) for each species at least four times. Census intervals were timed to: (a) to capture seedling emergence approximately 1-2 months post seed addition and every 2-4 months thereafter, and (b) measure the first-year survival percentage of emerged seedlings 12-15 months after seed addition. Because some seedlings may have emerged and died between censuses or prior to the first census, we may have underestimated emergence and overestimated seedling survival. This uncertainty was mitigated by our observations of obviously dead seeds (indicating failure to emerge) or dead seedlings (indicating emergence but subsequent mortality). For the four larger-seeded species for which ungerminated seeds could be reliably re-found, missing seeds were interpreted as pre-emergence mortality. Assuming that missing seeds were predated was reasonable because a companion study found that almost all seeds removed by vertebrates were eventually predated (Joyce et al., 2024). Variable durations of the “first year” period are typical in tropical tree seed addition experiments involving multiple species with differing phenologies (e.g. Svenning & Wright, 2005). One of the seven sites where we added seeds in 2021 was then cleared by the landowner, so in 2022 we added seeds of the remaining six species to an alternate site. The other six sites were used for the duration of the study. Accordingly, P. mollis and Q. benthamii, were tested at six sites rather than seven. We did not tag seedlings but were able to distinguish between natural recruits and seedlings that recruited from added seeds based on the location of seedlings relative to the grid and the size of seedlings compared to the experimental cohort. We sampled the non-sown control quadrat at each station at a single timepoint (July 2023) for seedlings similarly sized to our added individuals. References Holdridge, L. R., Grenke, W. C., Hatheway, W. H., Liany, T., & Tosi Jr, J. A. (1971). Forest environments in tropical life zones: A pilot study. Pergamon Press. Holl, K. D., Reid, J. L., Cole, R. J., Oviedo‐Brenes, F., Rosales, J. A., & Zahawi, R. A. (2020). Applied nucleation facilitates tropical forest recovery: Lessons learned from a 15-year study. Journal of Applied Ecology, 57(12). https://doi.org/10.1111/1365-2664.13684 Joyce, F. H., Ramos, B. M., Zahawi, R. A., & Holl, K. D. (2024). Vertebrate seed predation can limit recruitment of later-successional species in tropical forest restoration. Biotropica, 56(6), e13381. https://doi.org/10.1111/btp.13381 Kulikowski, A. J., Zahawi, R. A., Werden, L. K., Zhu, K., & Holl, K. D. (2023). Restoration interventions mediate tropical tree recruitment dynamics over time. Philosophical Transactions of the Royal Society B: Biological Sciences, 378(1867), 20210077. https://doi.org/10.1098/rstb.2021.0077 Schubert, S. C., Zahawi, R. A., Oviedo-Brenes, F., Rosales, J. A., & Holl, K. D. (2025). Active restoration increases tree species richness and recruitment of large-seeded taxa after 16–18 years. Ecological Applications, 35(1), e3053. https://doi.org/10.1002/eap.3053 Svenning, J.-C., & Wright, S. J. (2005). Seed limitation in a Panamanian forest. Journal of Ecology, 93(5), 853–862. https://doi.org/10.1111/j.1365-2745.2005.01016.x # Data from: Lower-intensity restoration interventions drive greater seedling establishment for later-successional tree species Dataset DOI: [10.5061/dryad.cfxpnvxjp](10.5061/dryad.cfxpnvxjp) ## Description of the data and file structure Data from a field experiment in which seeds of eight later-successional tree species were added to restoration plots and remnant forests in southern Costa Rica to assess barriers to seedling establishment.   ### Files and variables #### File: canopy\_cover.csv **Description:** canopy cover above each seed addition quadrat, calculated from four densiometer measurements ##### Variables * plot_id: (character) Unique identifier for each plot, combining site and treatment (see below). * site: (character) two-letter code identifying each study site * treatment: (character) two-letter code for treatment. NR = natural regeneration, AN = applied nucleation, PL = plantation, RF = reference forest * station: factor (integer 1-4) identifying the set of plots (“station”) within each plot. * station_id: (character) uniquely identifying each station; combination of site, treatment, and station. * quadrat: character variable identifying the quadrat within each station * quadrat_id: (character) unique identifier for each seed addition quadrat * densi_1 to densi_4 : Four numeric variables with raw densiometer measurements (number of densiometer points not open sky, range 0-96). * mean_densi: (numeric) mean of densi_1, densi_2, densi_3, and densi_4 * canopy_cover: mean_densi measurement converted to percent canopy cover (mean_densi/96*100), rounded to 1 decimal point #### File: litter\_depth.csv **Description:** leaf litter (or thatch) depth in each seed addition quadrat ##### Variables * plot_id: (character) Unique identifier for each plot, combining site and treatment (see below). * station_id: (character) Unique identifier for each station * quadrat_id: (character) unique identifier for each seed addition quadrat * site: (character) two-letter code identifying each study site * treatment: (character) two-letter code for treatment. NR = natural regeneration, AN = applied nucleation, PL = plantation, RF = reference forest * station: factor (integer 1-4) identifying the set of plots (“station”) within each plot. * quadrat: character variable (A or B) identifying the quadrat within each station. * date: date of measurement; format is YYYY-MM-DD * pt1-pt5: litter depth in cm at each of 5 points laid out in an X-shaped pattern within each 1-m quadrat, measured using a marked pin-flag (nearest 0.5 cm). * quadrat_mean_depth: mean of 5 depth measurements in each quadrat (pt1-pt5) #### File: natural\_recruit\_frequency.csv **Description:** Summary of naturally recruiting seedlings of study species recorded in non-seeded quadrats at each station. Each row is a combination of study species and treatment. ##### Variables * treatment: (character) two-letter code for treatment. NR = natural regeneration, AN = applied nucleation, PL = plantation, RF = reference forest * species: (character) 6-letter code for species * quads_with_recruits: (integer) number of sampled non-seeded quadrats with natural recruits of the specified species * n_quads: (integer) total number of non-seeded quadrats that were sampled for each species and treatment combination * freq: relative frequency of natural recruits within sampled non-seeded quadrats. Equals quads_with_recruits divided by n_quads. #### File: seedling\_emergence.csv **Description:** summary of seedling emergence, where each row is a combination of study species and station ##### Variables * species: (character) 6-letter code for species. Corresponding study species are as follows: * PAL_PAD = *Palicourea padifolia* * TRO_MEX = *Trophis mexicana* * LAC_AGG = *Lacistema aggregatum* * ERY_MAC = *Erythroxylum macrophyllum* * OCO_PUB = *Ocotea puberula* * OTO_NOV = *Otoba novogranatensis* * QUE_BEN = *Quercus benthamii* * PSE_MOL = *Pseudolmedia mollis* * plot_id: (character) Unique identifier for each plot, combining site and treatment (see below). * site: character) two-letter code identifying each study site * treatment: (character) two-letter code for treatment. NR = natural regeneration, AN = applied nucleation, PL = plantation, RF = reference forest * station: integer (1-4) identifying the set of plots (“station”) within each plot. * station_id: (character) uniquely identifying each station; combination of site, treatment, and station. * added_seeds: (integer) number of conspecific seeds added to each quadrat * live_stems: (integer) number of recorded seedlings that emerged within the quadrat * failed: (integer) number of seeds that failed to emerge. Equal to difference between added_seeds and live_stems. * prop_emerged: proportion of added seeds that emerged. Equal to live_stems/added_seeds. #### File: seedling\_heights.csv **Description:** median seedling height for seedlings of each species surviving to one year in each station ##### Variables * species: (character) 6-letter code for species * site: (character) two-letter code identifying each study site * treatment: (character) two-letter code for treatment. NR = natural regeneration, AN = applied nucleation, PL = plantation, RF = reference forest * station: integer (1-4) identifying the set of plots (“station”) within each plot. * station_id: (character) uniquely identifying each station; combination of site, treatment, and station. * median_height_cm: (numeric) median height of conspecific seedlings that survived to the end of the 1-year experimental period. NA = not available (used when sample_size = 0; i.e., when no seedlings survived). * sample_size: (integer) number of seedlings that survived to the end of the 1-year period; n for calculation of median_height_cm. #### File: seedling\_survival.csv **Description:** proportion of seedlings surviving to one year for each combination of species and station. ##### Variables * species: (character) 6-letter code for species. * plot_id: (character) Unique identifier for each plot, combining site and treatment (see below). * site: (character) two-letter code identifying each study site * treatment: (character) two-letter code for treatment. NR = natural regeneration, AN = applied nucleation, PL = plantation, RF = reference forest * station: integer (1-4) identifying the set of plots (“station”) within each plot. * station_id: (character) uniquely identifying each station; combination of site, treatment, and station. * germ_seeds: (integer) number of added seeds that emerged * survived_seedlings: (integer) number of seedlings that survived to the end of the ~1-year experimental period. * dead_seedlings: (integer) number of emerged seedlings that died * prop_surv: proportion of emerged seedlings that survived to the end of the ~1-year experimental period, rounded to two decimal places. Equal to survived_seedlings/germ_seeds. ## Code/software Any software capable of opening csv files. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA 

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4. Baltimore Ecosystem Study: Physical, chemical and biological properties of forest and home lawn soils

   https://doi.org/10.6073/pasta/3847c58578bd9d5987a49e55066b497b  

   Groffman, Peter M; Raciti, Steve (January 2020, Environmental Data Initiative)

   Abstract: One-meter soil cores were taken to evaluate soil texture, bulk density, carbon and nitrogen pools, microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrification and inorganic nitrogen pools in 32 residential home lawns that differed by previous land use and age, but had similar soil types. These were compared to soils from 8 forested reference sites. Purpose: Soil cores were obtained from residential and forest sites in the Baltimore, MD USA metropolitan area. The residential sites were mostly within the Gwynns Falls Watershed (-76.012008W, -77.314183E, 39.724847N, 38.708367S and approximately 17 km2) Lawns on residential sites were dominated by a variety of cool season turfgrasses. Forest soil cores were taken from permanent forest plots of the Baltimore Ecosystem Study (BES) LTER (Groffman et al. 2006). These remnant forests are over 100 years old with soils that were comparable in type and texture to those underlying the residential study sites. Soils from all sites were from the Manor series (coarse-loamy, micaceous, mesic Typic Dystrudepts), which are well-drained upland soils with loamy textures and bedrock at 5 to 10 feet below the soil surface. To aid the site selection process we used neighborhoods in the Baltimore City metropolitan area that have been mapped using HERCULES, a high resolution land cover classification system designed to assist in the study of human-ecological systems (Cadenasso et al. 2007). Using HERCULES and additional data sources, we identified residential sites that were similar except for single factors that we hypothesized to be important predictors of ecosystem dynamics. These factors included land use history (agriculture and forest, n = 10 and n = 22), housing density (low and medium/high, n = 9 and n = 23), and housing age (4 to 58 yrs old, n = 32). Housing age was acquired from the Maryland Property View database. Prior land use was determined based on land use change maps developed by integrating aerial photos from 1938, 1957, 1971, and 1999 into a geographic information system. Once a list of residential parcels meeting the predefined criteria were identified, we sent mailings to property owners chosen at random from each of the factor groups with the goal of recruiting 40 property owners for a 3 year study (of which this work is a part). We had recruited 32 property owners at the time that soil cores were obtained. Data have been published in Raciti et al. (2011a, 2011b) References Cadenasso, M. L., S. T. A. Pickett, and K. Schwarz. 2007. Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Frontiers in Ecology and the Environment 5:80-88. Groffman, P. M., R. V. Pouyat, M. L. Cadenasso, W. C. Zipperer, K. Szlavecz, I. D. Yesilonis, L. E. Band, and G. S. Brush. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. Raciti, S. R., P. M. Groffman, J. C. Jenkins, R. V. Pouyat, and T. J. Fahey. 2011a. Controls on nitrate production and availability in residential soils. Ecological Applications:In press. Raciti, S. R., P. M. Groffman, J. C. Jenkins, R. V. Pouyat, T. J. Fahey, M. L. Cadenasso, and S. T. A. Pickett. 2011b. Accumulation of carbon and nitrogen in residential soils with different land use histories. Ecosystems 14:287-297. 

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5. Normalized Difference Vegetation Index (NDVI) dataset from Small Mammal Experiment 2018-2023 (3 sites near Toolik Lake, Alaska)

   https://doi.org/10.18739/a2g44hs3c  

   Boelman, Natalie; Griffin, Kevin; Gough, Laura; McLaren, Jennie; Rastetter, Edward; Rowe, Rebecca (January 2024, NSF Arctic Data Center)

   We conducted a field experiment in which small mammal density was manipulated at three replicate sites were set up in tussock tundra near Toolik Lake, Alaska(AK) (68.6⁰N, 149.5⁰W, 760 meters (m) above sea level). The motivation for collecting this Normalized Difference Vegetation Index (NDVI) dataset was to understand and quantify the impact of the most abundant herbivores, small rodents (voles and lemmings) on dynamics of canopy productivity on tussock tundra vegetation communities in the Arctic. In July of 2017, at each site we set up an 8 m x 8 m fenced exclosure with a 0.635 centimeter(cm) square mesh to prevent herbivore grazing and increase vegetation cover (Exclosure treatment (EX)) and identified an 8 m x 8 m unfenced control plot marked at each corner with pin flags at each site (Control (CT)). In 2018, at each site we set up a 20 m x 20 m enclosure that was stocked with up to 4 voles from spring snowmelt through September in each year from 2018-2023 (Press treatment (PR)). In 2019, at each site we set up a 20 m x 20 m enclosure that was stocked with up to 4 voles from spring snowmelt through September in 2019 only, after which voles were removed and the fences served to exclude voles from 2020-2023 (Pulse treatment (PU)). Within each treatment at each site, we measured NDVI at 8 distinct locations within each quadrat and subplot during the period of peak leaf out (~July) in each of 2018-2023. In 2022 only, we measured NDVI approximately every 2-3 weeks starting mid-June through mid-August. All NDVI measurements were made with a RapidSCAN CS-45 Handheld Sensor (Holland Scientific, USA). 

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