This data provides the integrated cadastral framework for the specified Canada Land. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), Registration Plans (RS) and Location Sketches (LS) archived in the Canada Lands Survey Records.

In the last 20 years, the North American sagebrush biome has lost over 500 000 ha of intact and largely intact sagebrush plant communities on an annual basis. Much of this loss has been associated with expansion and infilling of invasive annual grasses (IAGs). These species are highly competitive against native perennial grasses in disturbed environments, and create fuel conditions that increase both the likelihood of fire ignition and the ease of wildfire spread across large landscapes. Given the current rate of IAG expansion in both burned and unburned rangelands, we propose a range-wide paradigm shift from opportunistic and reactive management, to a framework that spatially prioritizes maintenance of largely intact, uninvaded areas and improvement of invaded habitats in strategic locations. We created a framework accompanied by biome-wide priority maps using geospatial overlays that target areas to MAINTAIN large, uninvaded areas as natural resource anchors through activities to prevent IAGs, IMPROVE areas where management success in restoring large, intact landscapes is most likely, and CONTAIN IAG infestations where necessary. We then offer three case studies to illustrate the use of these concepts and map products at multiple scales. Our map products operate at the biome scale using regional data sources and additional data sources will be needed to inform local conservation planning. However, the basic strategic management principles of (1) maintaining the intact and uninvaded areas that we can least afford to lose to IAGs, (2) improving areas where we have a reasonable likelihood of restoration success, and (3) containing problems where we must, are timely, relevant, and scalable from the biome to local levels.

Grass Carp (Ctenopharyngodon idella) are a non-native species to North America that were first introduced for vegetation control in the 1960s. However, wild-reproducing Grass Carp can negatively impact aquatic habitats and aquatic communities by consuming substantial amounts of aquatic vegetation and increasing turbidity. Numerous fisheries techniques have been used in an attempt to control or eradicate Grass Carp, including electrofishing. However, electrofishing efficiency for Grass Carp has been variable, and optimum electrofishing waveforms and parameters for inducing a capture-prone response have not been determined. The objective of this study was to determine the optimum electrofishing waveforms and parameters to induce a capture-prone response at various water temperatures and conductivities in juvenile Grass Carp in a controlled, laboratory setting. Results suggested that rectangular pulse waveforms with 60 to 100 Hz frequencies were most effective for immobilization of juvenile Grass Carp. All duty cycles tested (20 – 48%) at these frequencies were effective; although at 60 Hz and 80 Hz frequencies, 24% and 30% duty cycles, respectively, may be more effective. Water temperature was positively related to voltage gradient immobilization thresholds whereas ambient water conductivity and fish size were inversely related to voltage gradient immobilization thresholds. This study provides important information to those seeking to control, eradicate, or detect Grass Carp using electrofishing and provides a framework for future studies focusing on adult Grass Carp. The dataset includes: Electrofishing exposure trial data from each trial type

Revision, re-demarcation and re-mapping of dry grasslands in continental biogeographical region of Austria. Areas from the dry grass catalogue 1986 (Holzner 1986) were revised and supplemented by missing occurrences. Mainly dry grass of the following types were processed: 6210 Semi-natural dry grasslands and scrubland facies on calcareous substrates (Festuco-Brometalia) (* important orchid sites); 6130 Calaminarian grasslands of the Violetalia calaminariae; 6190 Rupicolous pannonic grasslands (Stipo-Festucetalia pallentis). The data come from a mapping carried out in 2022/23 within the framework of the project: Wolfgang Willner: Inventory and typology of dry grasslands in the Alpine biogeographical region of Austria, BMK Biodiversity Fund.

Wolfgang Holzner, Eva Horvatic, Erwin Köllner, Walter Köppl, Maria Pokorny, Ernst Scharfetter, Georg Schramayr, Michael Strudl (1986): Austrian Dry Grass Catalogue - Green Series of the Ministry of Life

Ungulate herbivores play a prominent role in maintaining the tree–grass balance in African savannas. Their top-down role through selective feeding on either trees or grasses is well studied, but their bottom-up role through deposition of nutrients in dung and urine has been overlooked. Here, we propose a novel concept of savanna ecosystem functioning in which the balance between trees and grasses is maintained through stoichiometric differences in dung of herbivores that feed on them. We describe a framework in which N2-fixing trees and grasses, as well as ungulate browsing and grazing herbivores, occupy opposite positions in an interconnected cycle of processes. The framework makes the testable assumption that the differences in dung N:P ratio among browsers and grazers are large enough to influence competitive interactions between N2-fixing trees and grasses. Other key elements of our concept are supported with field data from a Kenyan savanna.

This data provides the integrated cadastral framework for the specified Canada Land. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), Registration Plans (RS) and Location Sketches (LS) archived in the Canada Lands Survey Records.

The grasses are one of the most diverse plant families on Earth, however, their classification and evolutionary history are obscured by their pollen stenopalynous (similar) morphology. A combination of high-resolution imaging of pollen surface ornamentation and computational analysis has previously been proposed as promising tool to classify grass taxonomic boundaries. In this study, we test this hypothesis by studying Poaceae pollen across the phylogeny from plants collected in northern South America, but also from published literature across the globe. We assessed if morphotypes that we establish using descriptive terminology are supported by computational analysis, if they vary along six (a)biotic variables and how vary across the phylogeny. Based on this analysis, we constructed a reference framework for pollen surface ornamentation morphotypes. Our results showed that there is a very wide variation of grass pollen surface ornamentation. We identified nine new and six known morphotypes and established our dataset of 223 species (243 individual plant specimens) from 11 subfamilies. Computational analysis showed that our morphotypes are well-supported by two quantitative features of pollen sculptural elements (size and density). The specific dataset and mapping of the phylogeny confirmed that pollen morphological sculpture is unrelated to (a)biotic variables but is diverse across through the phylogeny.

predict_nutrient.py demonstrates PLSR modelling using Bootstrap validation and it was tested in Python 3.6. The folder "organised_data" includes all of the pre-processed data, including reflectance data and laboratory-measured data. The program conducts the following parts: 1. Trains a PLSR model using the original data and then validates the model using the original data. The validation results will be saved in a .xlsx file with column names of 'xxx_full'. 2. Trains a PLSR model using the Bootstrap data (re-sampling with replacement) and then validates the model using the original data. The validation results will be saved in the .xlsx file with column names of 'xxx_bs. 3. Trains a PLSR model using the Bootstrap data and then validates the model using the Bootstrap data. The validation results will be saved in the .xlsx file with column names 'xxx_a'.

This data provides the integrated cadastral framework for the specified Canada Land. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), Registration Plans (RS) and Location Sketches (LS) archived in the Canada Lands Survey Records.

The map shows the shows potential grass and pasture legume yields under low input/natural conditions in tons dry weight per hectare at global level. The map was developed with the IIASA/FAO GAEZ 2009 modelling framework.

Background and Aims: Subfamily Arundinoideae represents one of the last unsolved taxonomic mysteries in the grass family (Poaceae) due to the narrow and remote distributions of many of its 19 morphologically and ecologically heterogeneous genera. Resolving the phylogenetic relationships of these genera could have substantial implications for understanding character evolution in the grasses, for example the twisted geniculate awn – a hygroscopic awn that has been shown to be important in seed germination for some grass species. In this study, the phylogenetic positions of most arundinoid genera were determined using DNA from herbarium specimens, and their placement affects interpretation of this ecologically important trait. Methods: A phylogenetic analysis was conducted on a matrix of full-plastome sequences from 123 species in 107 genera representing all grass subfamilies, with 15 of the 19 genera in subfamily Arundinoideae. Parsimony and maximum likelihood mapping approaches were used to estimate ancestral states for presence of a geniculate lemma awn with a twisted column across Poaceae. Lastly, anatomical characters were examined for former arundinoid taxa using light microscopy and scanning electron microscopy. Key Results: Four genera traditionally included in Arundinoideae fell outside the subfamily in the plastome phylogeny, with the remaining 11 genera forming Arundinoideae sensu stricto. The twisted geniculate awn has originated independently at least five times in the PACMAD grasses, in the subfamilies Panicoideae, Danthonioideae/Chloridoideae and Arundinoideae. Morphological and anatomical characters support the new positions of the misplaced arundinoid genera in the phylogeny, but also highlight convergent and parallel evolution in the grasses. Conclusions: In placing the majority of arundinoid genera in a phylogenetic framework, our study answers one of the last remaining big questions in grass taxonomy while highlighting examples of convergent evolution in an ecologically important trait, the hygroscopic, twisted geniculate awn.

This data provides the integrated cadastral framework for the specified Canada Land. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), Registration Plans (RS) and Location Sketches (LS) archived in the Canada Lands Survey Records.

Grass leaf shape is a strong indicator of their habitat. Linear leaves predominate in open areas and more ovate leaves distinguish forest-associated grasses. This pattern among extant species suggests that ancestral shifts between forest and open habitats may have coincided with changes in leaf shape or size. We tested relationships between habitat, climate, photosynthetic pathway and leaf shape and size in a phylogenetic framework to evaluate drivers of leaf shape and size variation over the evolutionary history of the family. We also estimated the ancestral habitat of Poaceae and tested whether forest margins served as transitional zones for shifts between forests and grasslands. We found that grass leaf shape is converging towards different shape optima in the forest understory, forest margins and open habitats. Leaf size also varies with habitat. Grasses have smaller leaves in open and drier areas, and in areas with high solar irradiance. Direct transitions between linear and ovate leaves are rare as are direct shifts between forest and open habitats. The most likely ancestral habitat of the family was the forest understory and forest margins along with an intermediate leaf shape served as important transitional habitat and morphology respectively for subsequent shifts across forest-grassland biome boundaries.

This dataset contains data from a nested frequency monitoing program for two vernal pools containing the endangered Sacramento Orcutt grass (Orcuttia viscida) at the approximately eight-acre California Department of Fish and Wildlife (CDFW) Phoenix Field Ecological Reserve in Sacramento County, California. Invasive waxy mannagrass (Glyceria declinata) is also present in vernal pools on the Reserve, and can compromise the integrity of vernal pools supporting Sacramento Orcutt grass. Two monitoring macroplots (A and B) were established on the Reserve in 2014 to monitor the frequency of Sacramento Orcutt grass, waxy mannagrass, and other plant species, and as a reference for photomonitoring. This data and metadata were submitted by California Department of Fish and Wildlife (CDFW) Staff though the Data Management Plan (DMP) framework with the id: DMP000044. For more information, please visit https://wildlife.ca.gov/Data/Sci-Data.

A key challenge in the management of populations is to quantify the impact of interven-tions in the face of environmental and phenotypic variability. However, accurate estima-tion of the effects of management and environment, in large-scale ecological research is often limited by the expense of data collection, the inherent trade-off between quality and quantity, and missing data. In this paper we develop a novel modelling framework, and demographically informed imputation scheme, to comprehensively account for the uncertainty generated by miss-ing population, management, and herbicide resistance data. Using this framework and a large dataset (178 sites over 3 years) on the densities of a destructive arable weed (Alo-pecurus myosuroides) we investigate the effects of environment, management, and evolved herbicide resistance, on weed population dynamics. In this study we quantify the marginal effects of a suite of common management prac-tices, including cropping, cultivation, and herbici..., Data were collected from a network of UK farms using a density structured survey method outlined in Queensborough 2011. , , # Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data

Contained are the datasets and code required to replicate the analyses in Goodsell et al (2023), Quantifying the impacts of management and herbicide resistance on regional plant population dynamics in the face of missing data.

Description of the data and file structure

Data: Contains data required to run all stages in the analysis.

Many files contain the same variable names, important variables have been described in the first object they appear in.

all_imputation_data.rds - The data required to run the imputation scheme, this is an R list containing the following:

$Management - data frame containing missing and observed values for management imputation

FF & FFY: the specific field, and field year.

year: the year.

crop: crop

cult_cat : cultivation category

a_gly: number of autumn (post September 1st) glyphosate applicatio...

NatureServe#8217;s ecological integrity framework provides a practical approach to organize criteria and indicators for this purpose (Faber-Langendoen et al. 2006, Unnasch et al. 2008). This framework provides a scorecard for reporting on the ecological status of a given CE within a given location, and if needed, facilitates the aggregation and synthesis of the component results for broader measures of ecological integrity at broader scales

The layer represents the scorecard of multiple indicator values of ecosytemspecies integrity. Individual layers for ecosystems may have representitive values of change in extent, landscape condition, landscape connectivity, Fire Regime Departure, or invasive annual grass risk. Not all ecosystemspecies will utilize all potential indicators. Species do not include change in extent or Fire Regime departure.

Please see cmbrCD and indicators.xlsx for a complete list of how each individual CE utilized the measures.

File-based data for download at https://www.grasslandsroadmap.org/ To address these challenges, the CGR provides a flexible strategic framework. This assessment map is core to implementing this framework. Partners and community members can use it to identify opportunities where short- and long-term conservation programs need to take place. This large-scale approach works best when partners work together by combining local priorities, resource concerns, and community will.

This map categorizes three areas of conservation to support and grow our core grasslands. By keeping the grass intact and “green-side up”, (shown in green on the map) these grassland areas can ensure food security, traditional cultural values and land overeignty for Indigenous Nations. Voluntary short- and long-term conservation programs and practices are needed to keep these core grasslands intact and support grass-based economies to help rural communities thrive.

Areas marked in yellow represent lands impacted by the spread of invasive woody vegetation and other annual species that negatively change the characteristic of these grasslands, and by areas under immediate threat of conversion to row-crop agriculture. Every effort should be made to ensure that these areas remain healthy, connected grasslands that benefit both rural communities and wildlife.

Purple indicates areas that need strategic investment that include, but are not limited to, removing invasive woody species, converting cropland on marginal soils back to grassland, and connecting to larger blocks of existing grassland.

To learn more about the map, explore data layers, and how you can help to support the health of this irreplaceable landscape visit www.grasslandsroadmap.org

Grasslands Risk Map Version 1.0 Released 10/21/2022 see https://www.grasslandsroadmap.org/

Appropriate use of data:

The Grasslands Risk Map provides a biome-level predictor of area in core grassland habitat, area under threat of conversion or encroachment to trees/woody shrubs, and area already converted/encroached. These data provide context for the top-two drivers of grassland loss over the past couple decades and can help guide national/international conservation priorities for grassland conservation of remaining core areas. In addition, these data are useful for understanding proximity to biome threats for regional/local conservation planners and as a guide for corresponding conservation action. It is recommended to integrate additional data layers/information at appropriate resolutions to further refine conservation actions and priorities at local scales (e.g., local resource concerns, species stronghold data, cultural resources, collaborative conservation groups, etc.) that complement biome-level. Attribute data:

Forest, natural (value 1000) Converted/altered Grasslands (Plowed/Encroached) (value 500) Vulnerable Grasslands (At Risk) (value 100) Core Grasslands (value 5) Data still in progress (value 5000)

[ Derived from parent entry - See data hierarchy tab ]

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for Europe. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) was imposed using the land use translator developed by Hoffmann et al. (2021). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Model Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction of the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX and other CORDEX regions. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

This polygon dataset identifies the extent and distribution of intertidal seagrass beds mapped through surveys between 2009 and 2012. These surveys were carried out as part of the suite of Biological Quality Elements (BQE), to classify coastal waters for the Water Framework Directive. The data is also used in condition assessments for the Habitats Directive.

Users outside of the Spatial NI Portal please use Resource Locator 2.

Source: Central Grasslands Roadmap Initiative | File-based data for downloadThe Grasslands Risk Map was developed using a combination of cropland conversion and woody encroachment data, recognizing that all acres are not equal and that prioritization on the landscape will need to happen. Maps and acreage estimates are based on currently available data. Areas at risk of cultivation or converted to plowed land since 2009 are from Plowprint data (WWF; plowprint.com) and Olimb and Robinson (2019). Areas at risk to woody encroachment of infested by woody plants are from the Rangeland Analysis Platform (rangelands.app) and a derivate model showing early warning for woody transitions (Uden et al. 2019; Twidwell et al. 2021; available at wlfw.org/landscapes/great-plains/woodland-expansion/).This assessment map is core to implementing the Central Grasslands Roadmap framework. Partners and community members can use it to identify opportunities where short- and long-term conservation programs need to take place. This large-scale approach works best when partners work together by combining local priorities, resource concerns, and community will. This map categorizes three areas of conservation to support and grow our core grasslands. By keeping the grass intact and “green-side up”, (shown in green on the map) these grassland areas can ensure food security, traditional cultural values and land overeignty for Indigenous Nations. Voluntary short- and long-term conservation programs and practices are needed to keep these core grasslands intact and support grass-based economies to help rural communities thrive. Areas marked in yellow represent lands impacted by the spread of invasive woody vegetation and other annual species that negatively change the characteristic of these grasslands, and by areas under immediate threat of conversion to row-crop agriculture. Every effort should be made to ensure that these areas remain healthy, connected grasslands that benefit both rural communities and wildlife. Purple indicates areas that need strategic investment that include, but are not limited to, removing invasive woody species, converting cropland on marginal soils back to grassland, and connecting to larger blocks of existing grassland. To learn more about the map, explore data layers, and how you can help to support the health of this irreplaceable landscape visit www.grasslandsroadmap.org.Appropriate use of data: The Grasslands Risk Map provides a biome-level predictor of area in core grassland habitat, area under threat of conversion or encroachment to trees/woody shrubs, and area already converted/encroached. These data provide context for the top-two drivers of grassland loss over the past couple decades and can help guide national/international conservation priorities for grassland conservation of remaining core areas. In addition, these data are useful for understanding proximity to biome threats for regional/local conservation planners and as a guide for corresponding conservation action. It is recommended to integrate additional data layers/information at appropriate resolutions to further refine conservation actions and priorities at local scales (e.g., local resource concerns, species stronghold data, cultural resources, collaborative conservation groups, etc.) that complement biome-level. Attribute data: Core Grasslands (value 5), Vulnerable Grasslands (value 100), Converted/altered Grasslands (value 500), Desert/Shrub (value 7), Forest (value 1000), Developed (value 2000), Water (value 5000)