Habitat restoration is commonly used to recover ecosystem services, but due to resource constraints, post-project monitoring often fails to fully evaluate the recovery of important ecosystem functions. Metric-based indicators use simple-to-measure variables to assess ecosystem health and function, thereby providing a time- and cost-effective method to improve monitoring. We used a tidal marsh dataset to develop metric-based indicators of ecosystem recovery. In 2021 and 2022, we surveyed eight restored/created and three natural reference tidal marshes in the northern Gulf of Mexico to assess recovery of ecosystem attributes [e.g., above- and below-ground biomass, soil organic matter (SOM), and sediment total carbon (C) and total nitrogen (N)]. To determine what combinations of variables best predicted recovery, we split our data into model training and testing datasets, used backward model selection, and then created and tested a metric-based indicator of ecosystem recovery. Recovery of plant above- and below-ground biomass and sediment structure (i.e., SOM, C, and N)—important measures of wetland carbon sink capacity and biogeochemical functioning—could be predicted through a combination of simpler-to-measure variables, such as time since restoration, percent plant cover, and sediment bulk density. The indicator constructed from these relationships was highly effective in predicting the development of ecosystem attributes (r = 0.85, p < 0.001). This indicator approach provides an effective but simple method to assess the recovery of ecosystem attributes in tidal marshes, and it can be used to develop similar indicators in other ecosystems. By overcoming resource constraints of post-project monitoring, metric-based indicators of ecosystem recovery may serve as a key strategy to improve restoration outcomes.

International commitments are challenging countries to restore their degraded lands, particularly forests. These commitments require global assessments of recovery timescales and trajectories of different forest attributes to inform restoration strategies. We use a meta-chronosequence approach including 125 forest chronosequences to reconstruct the past (c. 300 years), and model future recovery trajectories of forests recovering from agriculture and logging impacts. We found recovering forests significantly differed from undisturbed ones after 150 years and projected that difference to remain for up to 218 or 494 years for ecosystem attributes like nitrogen stocks or species similarity, respectively. These conservative estimates, however, do not capture the complexity of forest ecosystems. A centennial recovery of forests requires strategic, unprecedented planning to deliver a restored world. Methods Database construction We collected data from 16,873 plots from 125 chronosequences of recovering forest ecosystems in 110 published primary studies. From these chronosequences, we extracted 641 recovery trajectories of quantitative measures of ecosystem attributes along time, related to six recovery metrics (organism abundance, species diversity, species similarity, carbon cycling, nitrogen stock, and phosphorus stock), two restoration strategies (passive and active), three disturbance types [agriculture (including abandoned croplands and pastures), logging and mining], and a climatic metric (i.e., aridity index). From the selected chronosequences, we extracted 641 recovery trajectories, i.e., field-based quantitative measurements of ecosystem integrity repeated through time, reported in tables, figures, and text of the selected studies. Each trajectory included at least two data points, defined as the value of the ecosystem metric at different times since recovery started (hereafter, recovery time). Average values were considered for the data points with the same recovery time (n = 72, in 21 studies). We used response ratios (RRs) to estimate the recovery completeness, i.e., the effect sizes between reference and recovering systems. We computed the RR for each data point along the trajectory as ln (Xres/Xref), where Xres is the value of the ecosystem metric at a certain recovery time and Xref is the reference value of the same metric in the reference forest. Effect sizes of the meta-analysis were weighted by study precision, which was estimated as the product of the number of subplots and their area, assuming that a higher sampling effort would imply a higher precision. For abundance, diversity and similarity, we fitted fixed-effects models, with weights only accounting for within-study variability; whereas for biogeochemical functions, we assumed random-effect meta-analytic models, accounting for both between- and within-study variability. Statistical analysis To estimate the trajectory of forest recovery over time, we fitted a separate linear mixed model (LMM) for the RR of each recovery metric. We included the recovery time as a fixed factor and as a random slope, and the trajectory identity as a random intercept, enabling a different slope and intercept for each trajectory. As the recovery process along time may result in a wide range of trajectories from linear to more saturating shapes, we consider three functions to include the recovery time variable: one linear and two decelerating trends [ln(recovery time + 1) and √recovery time]. We then selected among the three options the one that best fit the data of each recovery metric according to the minimum AICc. The models for the recovery of similarity were fitted using the Morisita-Horn index, as the Pearson correlation test informed that it was correlated to Jaccard and Bray-Curtis indices. Their absolute values were square root transformed to meet the assumptions of general linear models and then multiplied by -1 to facilitate interpretation. Using the resulting LMMs, we predicted the RR after 73, 146, and 219 years of recovery [i.e., one, two, and three times the global life expectancy in 2019]. We then predicted the time needed for forest ecosystems to recover to 90% of reference values for each trajectory and recovery metric and calculated the median by metric. Also using the resulting LMMs, we predicted the RR after 50 and 100 years of recovery for each metric and trajectory (1) to know if the recovery completeness is dependent on the metric and (2) to understand the main explanatory variables underlying the recovery process for each metric. We fitted linear models (LM) to analyse the difference in the RR after 50 years and after 100 years of recovery among recovery metrics. The models had the recovery metric and the intercepts of the LMMs for each trajectory as fixed factors. The latter was included to account for the effect of the initial state of degradation when recovery started. We then fitted a separate LM for the effect of each explanatory variable studied (i.e., aridity, disturbance category, restoration strategy or life form) on the RR predictions after 50 and 100 years of all recovery metrics together, and then for each recovery metric individually. In all the cases, the intercept of the LMMs for each trajectory was also included as a fixed factor to account for the effect of the initial state of degradation when recovery started. For the models fitted for the disturbance category and the life form, we excluded the categories with <1% of the values (i.e., “mining” for disturbance and “bird” for life form) or those including data with mixing information from other categories (i.e., “agriculture and logging” for disturbance and “woody and non-woody” for life form).

1. Harvest-related poisoning events are common in tropical streams, yet research on stream recovery has largely been limited to temperate streams and generally does not include any measures of ecosystem function, such as leaf breakdown. 2. We assessed recovery of a second-order, high-gradient stream draining the Luquillo Experimental Forest, Puerto Rico, three months after a chlorine-bleach poisoning event. The illegal poisoning of freshwater shrimps for harvest caused massive mortality of shrimps and dramatic changes in those ecosystem properties influenced by shrimps. We determined recovery potential using an established recovery index and assessed actual recovery by examining whether the poisoned reach returned to conditions resembling an undisturbed upstream reference reach.3. Recovery potential was excellent (score=729 out of a possible 729) and can be attributed to nearby sources of organisms for colonization, the mobility of dominant organisms, unimpaired habitat, rapid flushing and processing of chlorine, and location within a national forest.4. Actual recovery was substantial. Comparison of the reference reach with the formerly poisoned reach indicated: (1) complete recovery of xiphocaridid and palaemonid shrimp population abundances, shrimp size distributions, leaf breakdown rates, and abundances of oligochaetes and mayflies on leaves, and (2) only small differences in atyid shrimp abundance and community and ecosystem properties influenced by atyid shrimps (standing stocks of epilithic fine inorganic and organic matter, chlorophyll a, and abundances of chironomids and copepods on leaves). 5. There was no detectable pattern between any measured variables and distance downstream from the poisoning. However, shrimp size-distributions indicated that the observed recovery may represent a source-sink dynamic, in which the poisoned reach acts as a sink which depletes adult shrimp populations from surrounding undisturbed habitats. Thus, the rapid recovery observed in this study is consistent with results from other field studies of pulse chlorine disturbances, harvest-related fish poisonings, and recovery of freshwater biotic interactions, but it is unlikely to be sustainable if multiple poisonings deplete adult populations to the extent that juvenile recruitment does not offset adult shrimp mortality.

This dataset contains data used in the associated publication in the International Journal of Remote Sensing.Wilson, Natalie R., and Laura M. Norman. 2018. “Analysis of Vegetation Recovery Surrounding a Restored Wetland Using the Normalized Difference Infrared Index (NDII) and Normalized Difference Vegetation Index (NDVI).” International Journal of Remote Sensing 39 (10): 3243–74. https://doi.org/10.1080/01431161.2018.1437297.The geodatabase contains four feature classes: AOI, MajorZone, MinorZone, and Green2007.Publication abstract: Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegeta ...

Most ecosystems are affected by anthropogenic or natural pulse disturbances, which alter the community composition and functioning for a limited period of time. Whether and how quickly communities recover from such pulses is central to our understanding of biodiversity dynamics and ecosystem organization, but also to nature conservation and management. Here, we present a meta-analysis of 508 (semi-)natural field experiments globally distributed across marine, terrestrial and freshwater ecosystems. We found recovery to be significant yet incomplete. At the end of the experiments, disturbed treatments resembled controls again when considering abundance (94%), biomass (82%), and univariate diversity measures (88%). Most disturbed treatments did not further depart from control after the pulse, indicating that few studies showed novel trajectories induced by the pulse. Only multivariate community composition on average showed little recovery: disturbed species composition remained dissimilar to the control throughout the experiment. Still, when experiments showed a higher compositional stability, they tended to also show higher functional stability. Recovery was more complete when systems had high resistance, whereas resilience and resistance were negatively correlated. The overall results were highly consistent across studies, but significant differences between ecosystems and organism groups appeared. Future research on disturbances should aim to understand these differences, but also fill obvious gaps in the empirical assessments for regions (especially the tropics), ecosystems and organisms. In summary, we provide general evidence that (semi-)natural communities can recover from pulse disturbances, but compositional aspects are more vulnerable to long-lasting effects of pulse disturbance than the emergent functions associated to them. Methods This data set contains all effect sizes between a control and a disturbed treatment for 508 experiments used to analyze recovery across ecosystems and organisms. A full description of the methods is in the paper associated to this manuscript. The data contains 6 columns.

case.ID is a unique identifier for each of the 508 experiments. Appendix S1 at the paper contains full documentation for each of the experiments.

resp.cat categorizes the response variable as either biomass, abundance, an index of diversity or composition. The manuscript details the rationale behind these categries, and the detailed variables measures are given in Appendix S1.

Day of the experiment, with 0 being the pre-distrubance day.

RD: Relative duration within the experiment, i.e., normalizing the day of the experiment to the duration of the experiment, such that 1 is the final sampling date.

LRR is the effect size of the disturbance, in most cases a log-response ratio, only for composition mostly a dissimilarity metric. Again, for detauls see the paper's method section

var.lrr is the sampling variance of the effect sizes, if blank it could not be derived from the primary literature.

This data set contains the original model output data submissions from the 24 terrestrial biosphere models (TBM) that participated in the North American Carbon Program (NACP) Site-Level Synthesis. The model teams generated estimates for, but not limited to, a minimum of six variables, including gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) for each of 47 selected eddy covariance flux tower sites across North America. Participating modeling teams followed the NACP Site Synthesis Protocol (site_synthesis_protocol_v7.pdf), which covers procedures, plans, and infrastructure for the site-level analyses. File format and units conversions of several data submissions were made by the MAST-DC to produce NetCDF files of consistent content and structure for all 24 TBM outputs. The model outputs are structured as described in Appendix A: Model Output Variables, of the Site Synthesis Protocol.

In addition, MAST-DC processed these original model submissions to derive uniquely processed and formatted data files for model inter-comparison and evaluation (NACP Site: Terrestrial Biosphere Model and Aggregated Flux Data in Standard Format). This related data set provides GPP, NEE, LAI, Re, LE, and sensible heat (H) model output variables at the native half-hourly time step, and in daily, monthly, and annual aggregations. The related data set also contains gap-filled observations and total uncertainty estimates at the same time steps. There are 24 compressed (*.zip) files with this data set -- one file for each model. When expanded, the .zip files contain model output data files for flux tower sites in NetCDF and some in text formats.

This data set provides standardized output variables for gross primary productivity (GPP), net ecosystem exchange (NEE), leaf area index (LAI), ecosystem respiration (Re), latent heat flux (LE), and sensible heat flux (H) from 24 terrestrial biosphere models for 47 eddy covariance flux tower sites in North America. Each model used standardized input data for each flux tower site (i.e., gap-filled, locally observed weather; land use history; and other site specific data) and followed standard model setup and spinup procedures. The files also contain gap-filled observations and total uncertainty estimates. The data set was compiled for the North American Carbon Program (NACP) Site-Level Synthesis for use in model inter-comparison and assessment of how well the models simulate carbon processes across vegetation types and environmental conditions in North America.

There is one compressed (.zip) file with this data set. When expanded, the .zip file contains model output data for one variable at one site. The model output and observations are available at the native half-hourly time step, or in daily, monthly, and annual aggregations, in comma-separated text (.csv) format.

Archaea play vital roles in global biogeochemical cycles, particularly in nitrification and methanogenesis. The recovery of archaeal community following disturbance is essential for maintaining the stability of ecosystem function. To examine whether the archaeal community could recover from water flooding and assess the influence of anthropogenic pollution on the autogenic recovery, soil samples from two riparian zones with contrasting pollution background were investigated. Collected samples in each area were divided into three groups of reference, flooding, and recovery according to the flooded state of each site. The results showed that the archaeal abundance was resilient to the disturbances of both water flooding and anthropogenic pollution. More similar community composition and diversity appeared between the recovery and reference groups in the area with low anthropogenic pollution. It indicated that high anthropogenic pollution could result in less resilience of archaeal community. The co-occurrence network further revealed that the archaeal community in the area of low anthropogenic pollution exhibited more associations suggesting a higher ecosystem stability. The better recovery of archaeal community was associated with the high resilience ability. The Nitrososphaerales was the key taxon maintaining the better recovery of the archaeal community from the disturbances due to its high resilience index and quantitative dominance. Overall, archaeal community has the capability of autogenic recovery, the process of which might be intervened by anthropogenic pollution, and then potentially affects the ecosystem functions of the riparian system.

This dataset provides a map of the distribution of ecosystem functional types (EFTs) at 0.05 degree resolution across Mexico for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT for each pixel.

The goal of this study was to predict the global distribution of plant rooting depths based on data about global aboveground vegetation structure and climate. Vertical root distributions influence the fluxes of water, carbon, and soil nutrients and the distribution and activities of soil fauna. Roots transport nutrients and water upwards, but they are also pathways for carbon and nutrient transport into deeper soil layers and for deep water infiltration. Roots also affect the weathering rates of soil minerals. For calculating such processes on a global scale, data on vertical root distributions are needed as inputs to global biogeochemistry and vegetation models. In the Project for Intercomparison of Land Surface Parameterization Schemes (PILPS), rooting depth and vertical soil characteristics were the most important factors explaining scatter for simulated transpiration among 14 land-surface models. Recently, the Terrestrial Observation Panel for Climate of the Global Climate Observation System (GCOS) identified the 95% rooting depth as a key variable needed to quantify the interactions between the climate, soil, and plants, stating that the main challenge was to find the correlation between rooting depth and soil and climate features (GCOS/GTOS Terrestrial Observation Panel for Climate 1997). In response to this challenge, a data set of vertical rooting depths was collected from the literature in order to construct maps of global ecosystem rooting depths.

The parameters included in these data sets are estimates for the soil depths containing 50% and 95% of all roots, termed 50% and 95% rooting depths (D50 and D95, respectively). Together, these variables can be used to calculate estimates for vertical root distributions, using a logistic equation provided in this documentation. The data represent mean ecosystem rooting depths for 1 by 1 degree grid cells.

Related data sets:Â The ORNL DAAC offers related data sets by Jackson et al. (2003), Gordon and Jackson (2003), Schenk and Jackson (2003), and Gill and Jackson (2003).

This data set is one of the products of the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP II) data collection which contains 50 global time series data sets for the ten-year period 1986 to 1995. Selected data sets span even longer periods. ISLSCP II is a consistent collection of data sets that were compiled from existing data sources and algorithms, and were designed to satisfy the needs of modelers and investigators of the global carbon, water and energy cycle. The data were acquired from a number of U.S. and international agencies, universities, and institutions. The global data sets were mapped at consistent spatial (1, 0.5 and 0.25 degrees) and temporal (monthly, with meteorological data at finer (e.g., 3-hour)) resolutions and reformatted into a common ASCII format. The data and documentation have undergone two peer reviews.

ISLSCP is one of several projects of Global Energy and Water Cycle Experiment (GEWEX) [http://www.gewex.org/] and has the lead role in addressing land-atmosphere interactions -- process modeling, data retrieval algorithms, field experiment design and execution, and the development of global data sets.

Land degradation can result in a loss of critical ecosystem services that we often seek to restore through re-establishment of desired plant communities. Trait-based approaches have the potential to target specific ecosystem services based on associations between the functional composition of plant communities and ecosystem properties that serve as indicators of those services. The effect of functional composition on ecosystem recovery may depend on the amount of restored plant biomass, itself a supporting service frequently targeted in restoration efforts. Yet, interactions between functional composition and biomass are not formally integrated into trait-based analytical frameworks. We tested the hypothesis that functional composition of plant communities both drives, and interacts with, biomass production to influence indicators of soil functioning and weed suppression across a network of degraded dryland restoration experiments. This networked approach allowed us to identify generalized effects of functional composition on ecosystem recovery across a range of dryland climate conditions. Climate had a substantial effect on ecosystem indicators, with weed cover and soil surface stability increasing in more arid climates, water infiltration increasing with precipitation, and aggregate structure increasing with less freezing. After accounting for climate effects across study sites, we found significant effects of community-weighted mean (CWM) trait values on biomass, particularly a positive effect of leaf carbon-to-nitrogen ratio, and of CWM-biomass interactions on other ecosystem indicators. Cover of exotic species was reduced in restored communities with a combination of low leaf dry matter content and high biomass, soil water infiltration increased with lower specific root length and high biomass, and soil aggregate stability increased with higher root dry matter content and high biomass, among other effects. Functional diversity had no significant effects on any ecosystem indicators. Synthesis: The influence of community functional composition on ecosystem properties increases with community biomass, particularly in disturbed or low productivity systems. This suggests that active management should not only focus on trait values that optimize individual ecosystem indicators, but also how those functional strategies are complementary or counter to those that increase biomass.

Tropical forest recovery is fundamental to addressing the intertwined climate and biodiversity loss crises. While regenerating trees sequester carbon relatively quickly, the pace of biodiversity recovery remains contentious. Here, we use bioacoustics and meta-barcoding to measure forest recovery post-agriculture in a global biodiversity hotspot in Ecuador. We show that the community composition, and not species richness, of vocalizing vertebrates identified by experts reflects the restoration gradient. Two automated measures – an acoustic index model and a bird community derived from an independently developed Convolutional Neural Network – correlated well with restoration (adj-R2 = 0.62 and 0.69, respectively). Importantly, both measures reflected composition of non-vocalizing nocturnal insects identified via meta-barcoding. We show that such automated monitoring tools, based on new technologies, can effectively monitor the success of forest recovery, using robust and reproducible data..., DNA was extracted from 200-µL aliquots using the DNEasy blood & tissue kit (Qiagen) following the manufacturer’s instructions. Multiplex PCR was performed using 5 µL of extracted genomic DNA, Plant MyTAQ (Bioline, Luckenwalde, Germany) and high-throughput sequencing (HTS)-adapted mini-barcode primers targeting the mitochondrial CO1-5P region (mlCOIintF – 5’– GWACWGGWTGAACWGTWTAYCCYCC–3’; dgHCO2198–5’-TAAACTTCAGGGTGACCAAARAAYCA–3’; following Leray et al., 2013 – also see Morinière et al.; Morinière et al.. Amplification success and fragment length were determined using gel electrophoresis. The amplified DNA was cleaned and each sample was resuspended in 50 µL of molecular water. Illumina Nextera XT (Illumina Inc., San Diego, USA) indices were ligated to the samples in a second PCR, conducted at the same annealing temperature as in the first but with only seven cycles. Ligation success was confirmed by gel electrophoresis. DNA concentrations were measured using a Qubit fluorometer (Li..., , # DNA Metabarcoding RAW FASTQ data for "Soundscapes and artificial intelligence provide powerful tools to track biodiversity recovery in tropical forests"

[this dataset contains demultiplexed Illumina FASTQ files along with a samplesheet to process these files and an excel file listing all of the detected BINs with identification success rate and taxonomy retreived from sequence BLAST on BOLD (www.boldsystems.org)]

Demultiplexed RAW FASTQ Illumina sequence data files used for analysis of the dataset.

Use the "BIOINFORMATIC_INFORMATION.xlsx" file for your bioinformatic pipeline to run primer trimming. Each FASTQ file is listed within the BIOINFORMATIC_INFORMATION.xlsx with its corresponding primer sequences used. Follow the methods described in the material & methods section (described below) in order to generate an annotated OTU table. Primer used CO1 Leray et al., 2013 - https://frontiersinzoology.biomedcentral.com/articles/10.1186/1742-9994-10-34

**Excel File contain...

This dataset supports the publication “Remote sensing diagnosis of ecosystem resilience dynamics in agricultural heritage landscapes”. It provides processed remote sensing products, derived indicators, and analysis codes used to quantify and map ecosystem resilience across polder-based agricultural heritage landscapes in the Taihu Basin, China, from 2005 to 2024.The dataset includes:EVI Time Series (2005–2024):A long-term Enhanced Vegetation Index (EVI) dataset derived from MODIS imagery.LSWI Time Series (2005–2024):A Land Surface Water Index (LSWI) time series produced from MODIS surface reflectance data to capture spatiotemporal variations in surface moisture and hydrological dynamics relevant to agricultural resilience.Long-term Resilience Indicator:Pixel-wise resilience metrics estimated from EVI time series using autoregressive modeling (AR(1)) and recovery trajectory analysis to capture long-term stability and recovery capacity.Short-term Resilience Index:Disturbance–recovery responses computed over short temporal windows to detect transient resilience fluctuations following perturbations.Environmental Drivers Dataset:Spatial layers representing key environmental and anthropogenic factors influencing resilience.R Code for Indicator Calculation:Scripts used for preprocessing, resilience computation, and visualization. All analyses were performed in R (v4.3.2) using packages raster, dplyr, mgcv, and ggplot2.

Given physical-chemical indexes are one of the indicators of the pollution quality of the Sea water.

This dataset provides maps of the distribution of ecosystem functional types (EFTs) and the interannual variability of EFTs at 0.05 degree resolution across the conterminous United States (CONUS) for 2001 to 2014. EFTs are groupings of ecosystems based on their similar ecosystem functioning that are used to represent the spatial patterns and temporal variability of key ecosystem functional traits without prior knowledge of vegetation type or canopy architecture. Sixty-four EFTs were derived from the metrics of a 2001-2014 time-series of satellite images of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) product MOD13C2. EFT diversity was calculated as the modal (most repeated) EFT and interannual variability was calculated as the number of unique EFTs for each pixel.

Forest fires alter soil microbial communities that are essential to support ecosystem recovery following land burning. These alterations have different responses according to soil abiotic pre- and post-fire conditions and fire severity, among others, and tend to decrease along vegetation recovery over time. Thus, understanding the effects of fires on microbial soil communities is critical to evaluate ecosystem resilience and restoration strategies in fire-prone ecosystems. We studied the state of community-level physiological profiles (CLPPs) and the prokaryotic community structure of rhizosphere and bulk soils from two fire-affected sclerophyll forests (one surveyed 17 months and the other 33 months after fire occurrence) in the Mediterranean climate zone of central Chile. Increases in catabolic activity (by average well color development of CLPPs), especially in the rhizosphere as compared with the bulk soil, were observed in the most recently affected site only. Legacy of land burning was still clearly shaping soil prokaryote community structure, as shown by quantitative PCR (qPCR) and Illumina MiSeq sequencing of the V4 region of the 16S rRNA gene, particularly in the most recent fire-affected site. The qPCR copy numbers and alpha diversity indexes (Shannon and Pielou’s evenness) of sequencing data decreased in burned soils at both locations. Beta diversity analyses showed dissimilarity of prokaryote communities at both study sites according to fire occurrence, and NO3– was the common variable explaining community changes for both of them. Acidobacteria and Rokubacteria phyla significantly decreased in burned soils at both locations, while Firmicutes and Actinobacteria increased. These findings provide a better understanding of the resilience of soil prokaryote communities and their physiological conditions in Mediterranean forests of central Chile following different time periods after fire, conditions that likely influence the ecological processes taking place during recovery of fire-affected ecosystems.

Disturbance disrupts the balance between gross primary productivity and respiration, resulting in a net C loss for some time after a stand-replacing fire. However, our understanding of this process is based on a limited number of studies. Ecosystem C recovery post-fire must be explicitly and carefully examined in order to generate accurate predictions of C cycle impacts of future wildfires and change in fire regimes. Montane ponderosa and lodgepole pine forests, either single-species stands or mixed, dominate surface area in the Southern Rockies. These species have drastically different relationships with wildfire; the current narrative portrays ponderosa pine as accustomed to low-severity surface fires with low regeneration rates following high-severity wildfire, whereas lodgepole pine forests readily regenerate after a high-severity stand-replacing wildfire. Forests at the transition between lower montane and upper montane may be more sensitive to future climate change than their lower counterparts; e.g., a stand-replacing disturbance could cause montane ponderosa pine forests to yield to lodgepole pine. It is important to understand how wildfire impacts ecosystem C fluxes in these ecosystems and how landscape dynamics, including topographical changes in climate and distance from forest seed source, can be used to predict C cycle responses to future wildfire patterns. To date, no single study has collected data at an adequate temporal resolution to fully characterize the short-term, intermediate-term, and long-term response and recovery of forest soil respiration to pre-burn conditions. The aim of this work is to predict soil respiration and net primary productivity in pine forests of the southern Rocky Mountains based on time since fire, fire severity, forest type, and forest and soil properties, such as tree basal area, leaf area index and soil carbon pools. We sampled 5 wildfires and 1 high-severity prescribed fire as well as nearby unburned reference forests. The following time-since-fire intervals were sampled along a 30-yr chronosequence: 1-5 years (n=1), 5-10 years (n=1), 10-20 years (n=3), and 20-25 years (n=1).

Earlier declines in marine resources, combined with current fishing pressures and devastating coral mortality in 2015, have resulted in a degraded coral reef ecosystem state at Puakō in West Hawaiʹi. Changes to resource management are needed to facilitate recovery of ecosystem functions and services. We developed a customised ecosystem model to evaluate the performance of alternative management scenarios at Puakō in the provisioning of ecosystem services to human users (marine tourists, recreational fishers) and enhancing the reef's ability to recover from pressures (resilience). Outcomes of the continuation of current management plus five alternative management scenarios were compared under both high and low coral-bleaching related mortality over a 15-year time span. Current management is not adequate to prevent further declines in marine resources. Fishing effort is already above the multispecies sustainable yield, and, at its current level, will likely lead to a shift to algal-dominated reefs and greater abundance of undesirable fish species. Scenarios banning all gears other than line fishing, or prohibiting take of herbivorous fishes, were most effective at enhancing reef structure and resilience, dive tourism, and the recreational fishery. Allowing only line fishing generated the most balanced trade-off between stakeholders, with positive gains in both ecosystem resilience and dive tourism, while only moderately decreasing fishery value within the area. Synthesis and applications. Our customised ecosystem model projects the impacts of multiple, simultaneous pressures on a reef ecosystem. Trade-offs of alternative approaches identified by local managers were quantified based on indicators for different ecosystem services (e.g. ecosystem resilience, recreation, food). This approach informs managers of potential conflicts among stakeholders and provides guidance on approaches that better balance conservation objectives and stakeholders' interests. Our results indicate that a combination of reducing land-based pollution and allowing only line fishing generated the most balanced trade-off between stakeholders and will enhance reef recovery from the detrimental effects of coral bleaching events that are expected over the next 15 years.

This dataset was for the paper titled with “Plant and soil biodiversity are essential for supporting highly multifunctional forests during Mediterranean rewilding” on Functional Ecology. Specifically, We investigated the changes in multiple dimensions of biodiversity and ecosystem services in a 120-year forest succession after harvest to identify potential trade-offs in multiple dimensions of ecosystem function, and further assess the link between above and belowground biodiversity and function.

This data set contains standardized gridded observation data, terrestrial biosphere model output data, and inverse model simulations of carbon flux parameters that were used in the North American Carbon Program (NACP) Regional Synthesis activities. The data set provides five observation data files (MODIS GPP, MODIS NPP, FIA forest biomass/forest area, NASS crop NPP, and NASS agricultural land fraction) and simulation results from 18 terrestrial biosphere models (TBM) (28 variables; 114 files) and seven inverse models (IM) (two variables; 8 files).

To produce this data set, the NACP Modeling and Synthesis Thematic Data Center (MAST-DC) resampled original model simulation results and observation measurement data to 1-degree spatial resolution for North American region, interpolated into monthly or yearly temporal resolution, and reformatted into Climate and Forecast (CF) convention compatible netCDF format.

This data set is related to two other processed regional data sets (i.e., NACP Regional: Supplemental Gridded Observations, Biosphere and Inverse Model Outputs; and NACP Regional: National Greenhouse Gas Inventories and Aggregated Gridded Model Data) and the originally-submitted NACP Regional: Original Observation Data and Biosphere and Inverse Model Outputs.