Activity Project Area Sale Area Improvement (SAI) Plan represents an area (polygon) within which one or more Sale Area Improvement (SAI) related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.

Activity Project Area NEPA represents an area (polygon) within which one or more activities related to the National Environmental Policy Act (NEPA) are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and Downloads

Activity Project Area Timber Sale represents an area (polygon) within which one or more Timber Sale related activities are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.These data are a central source for project area boundaries for use in national information requests and cross unit analysis and makes the project area boundaries and their basic attributes more easily available to field units. It also provides public access to the data during project planning and implementation. Please note that this dataset is not complete and forests continue to improve the quality of the data over time.Metadata and Downloads

Data represents presence-absence prediction of kelp forest. Biological ground truth data were integrated with high resolution environmental datasets to develop statistical model that accurately predict the structure of Laminaria forests within the Bay of Morlaix. As a direct management output, high-resolution map (25 m2 grid) was produced. Data represents presence-absence prediction of kelp forest. Biological ground truth data were integrated with high resolution environmental datasets to develop statistical model that accurately predict the structure of Laminaria forests within the Bay of Morlaix. As a direct management output, high-resolution map (25 m2 grid) was produced.

Trait data represent the basis for ecological and evolutionary research and have relevance for biodiversity conservation, ecosystem management and earth system modelling. The collection and mobilization of trait data has strongly increased over the last decade, but many trait databases still provide only species-level, aggregated trait values (e.g. ranges, means) and lack the direct observations on which those data are based. Thus, the vast majority of trait data measured directly from individuals remains hidden and highly heterogeneous, impeding their discoverability, semantic interoperability, digital accessibility and (re-)use. Here, we integrate quantitative measurements of verbatim trait information from plant individuals (e.g. lengths, widths, counts and angles of stems, leaves, fruits and inflorescence parts) from multiple sources such as field observations and herbarium collections. We develop a workflow to harmonize heterogeneous trait measurements (e.g. trait names and their values and units) as well as additional information related to taxonomy, measurement or fact and occurrence. This data integration and harmonization builds on vocabularies and terminology from existing metadata standards and ontologies such as the Ecological Trait-data Standard (ETS), the Darwin Core (DwC), the Thesaurus Of Plant characteristics (TOP) and the Plant Trait Ontology (TO). A metadata form filled out by data providers enables the automated integration of trait information from heterogeneous datasets. We illustrate our tools with data from palms (family Arecaceae), a globally distributed (pantropical), diverse plant family that is considered a good model system for understanding the ecology and evolution of tropical rainforests. We mobilize nearly 140,000 individual palm trait measurements in an interoperable format, identify semantic gaps in existing plant trait terminology and provide suggestions for the future development of a thesaurus of plant characteristics. Our work thereby promotes the semantic integration of plant trait data in a machine-readable way and shows how large amounts of small trait data sets and their metadata can be integrated into standardized data products.

Tropical rainforests play a powerful role in mediating the global climate through the exchange and storage of carbon and water. Climate change is expected to generate higher atmospheric water demand in many areas, potentially increasing the rate of evaporation. In this study, we show that higher evaporative demand may in fact lead to lower fluxes of water from tropical rainforests and a reduced capacity of these forests to store carbon.

The record contains meteorological and forest inventory data in addition to data on soil water potential, sapflow measurements and tree hydraulic vulnerability measures from Robson Creek and Cow Bay study sites in Far North Queensland. The measurements occurred over a period of two years form 2019 to 2020.

Areas of suitable habitat for species and communities have arisen, shifted, and disappeared with Pleistocene climate cycles, and through this shifting landscape, current biodiversity has found paths to the present. Evolutionary refugia, areas of relative habitat stability in this shifting landscape, support persistence of lineages through time, and are thus crucial to the accumulation and maintenance of biodiversity. Areas of endemism are indicative of refugial areas where diversity has persisted, and endemism of intraspecific lineages in particular is strongly associated with late-Pleistocene habitat stability. However, it remains a challenge to consistently estimate the geographic ranges of intraspecific lineages and thus infer phylogeographic endemism, because spatial sampling for genetic analyses is typically sparse relative to species records. We present a novel technique to model the geographic distribution of intraspecific lineages, which is informed by the ecological niche of a species and known locations of its constituent lineages. Our approach allows for the effects of isolation by unsuitable habitat, and captures uncertainty in the extent of lineage ranges. Applying this method to the arc of rainforest areas spanning 3500 km in eastern Australia, we estimated lineage endemism for 53 species of rainforest dependent herpetofauna with available phylogeographic data. We related endemism to the stability of rainforest habitat over the past 120,000 years and identified distinct concentrations of lineage endemism that can be considered putative refugia. These areas of lineage endemism are strongly related to historical stability of rainforest habitat, after controlling for the effects of current environment. In fact, a dynamic stability model that allows movement to track suitable habitat over time was the most important factor in explaining current patterns of endemism. The techniques presented here provide an objective, practical method for estimating geographic ranges below the species level, and including them in spatial analyses of biodiversity.

Analysis of ‘Activity Project Areas NEPA (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5c68b6ec-4c09-497c-bcd3-bceae1b0f878 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Activity Project Area NEPA represents an area (polygon) within which one or more activities related to the National Environmental Policy Act (NEPA) are aggregated or organized. The data comes from the Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS), which is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. FACTS is an activity tracking application for all levels of the Forest Service.

--- Original source retains full ownership of the source dataset ---

Abstract The reports of the Intergovernmental Panel on Climate Change constantly reveal the possibility that climatic extremes will increase in the coming years. The Amazon Region has experienced these events frequently, which has contributed to the fact that more studies about its susceptibility are carried out. This study aimed to identify the microclimate response to droughts of 2005 and 2010 in areas of native forest and pasture in the Western Amazon by analyzing specific air humidity, air temperature and net radiation. The data used come from towers belonging to Large Scale Biosphere-Atmosphere Experiment in Amazonia. The results indicate that in the years studied there were significant changes in the variables studied at both sites, with reductions of approximately 16% in specific humidity and increases of up to 3.76% in temperature. However, the effects of the 2010 drought may have been softened in the forest due to the event being preceded by an extreme flood event (2009). The results show that the conversion of forest areas to pasture, together with extreme events, can interfere in the meteorological variables, being necessary the continuous study of this dynamics for the microclimatic implications to be elucidated.

Data base ms-repositoryData file that includes ant nest dimensions, nest entrances dimensions, ant speed, ant flux

The accuracy of digital elevation models (DEMs) in forested areas plays a crucial role in canopy height monitoring and ecological sensitivity analysis. Despite extensive research on DEMs in recent years, significant errors still exist in forested areas due to factors such as canopy occlusion, terrain complexity, and limited penetration, posing challenges for subsequent analyses based on DEMs. Therefore, a CNN-LightGBM hybrid model is proposed in this paper, with four different types of forests (tropical rainforest, coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest) selected as study sites to validate the performance of the hybrid model in correcting COP30DEM in different forest area DEMs. In the hybrid model of this paper, the choice was made to use the Densenet architecture of CNN models with LightGBM as the primary model. This choice is based on LightGBM’s leaf-growth strategy and histogram linking methods, which are effective in reducing the data’s memory footprint and utilising more of the data without sacrificing speed. The study uses elevation values from ICESat-2 as ground truth, covering several parameters including COP30DEM, canopy height, forest coverage, slope, terrain roughness and relief amplitude. To validate the superiority of the CNN-LightGBM hybrid model in DEMs correction compared to other models, a test of LightGBM model, CNN-SVR model, and SVR model is conducted within the same sample space. To prevent issues such as overfitting or underfitting during model training, although common meta-heuristic optimisation algorithms can alleviate these problems to a certain extent, they still have some shortcomings. To overcome these shortcomings, this paper cites an improved SSA search algorithm that incorporates the ingestion strategy of the FA algorithm to increase the diversity of solutions and global search capability, the Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) is introduced. By comparing multiple models and validating the data with an airborne LiDAR reference dataset, the results show that the R2 (R-Square) of the CNN-LightGBM model improves by more than 0.05 compared to the other models, and performs better in the experiments. The FA-SSA-CNN-LightGBM model has the highest accuracy, with an RMSE of 1.09 meters, and a reduction of more than 30% of the RMSE when compared to the LightGBM and other hybrid models. Compared to other forested area DEMs (such as FABDEM and GEDI), its accuracy is improved by more than 50%, and the performance is significantly better than other commonly used DEMs in forested areas, indicating the feasibility of this method in correcting elevation errors in forested area DEMs and its significant importance in advancing global topographic mapping.

Workbook on forest management changes in Canada, 2017 and 2020 Workbook on forest management changes in Canada, 2017 and 2020. This is part of the Forest Management in Canada, 2020 (Story Map of Forest Management in Canada, 2020). This third party metadata element was translated using an automated translation tool (Amazon Translate).