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583 results for “plant distributions”
Estimation of Abundance and Distribution of Salt Marsh Plants from Images Using Deep Learning
Recent advances in computer vision and machine learning, most notably deep convolutional neural networks (CNNs), are exploited to identify and localize various plant species in salt marsh images. Three different approaches are explored that provide estimations of abundance and spatial distribution at varying levels of granularity in terms of spatial resolution. In the coarsest-grained approach, CNNs are tasked with identifying which of six plant species are present/absent in large patches within the salt marsh images. CNNs with diverse topological properties and attention mechanisms are shown capable of providing accurate estimations with > 90% precision and recall in the case of the more abundant plant species whereas the performance of the CNNs is observed to decline in the case of less common plant species. Estimation of percent cover of each plant species is performed at a finer spatial resolution, where smaller image patches are extracted and the CNNs tasked with identifying the plant species or substrate at the center of the image patch. In an ecological setting, several image patches (~100) are extracted and classified using this approach to estimate the percent cover of the various plant species in the image. For the percent cover estimation task, the CNNs are observed to exhibit a performance profile similar to that for the presence/absence estimation task, but with an ~ 5–10% reduction in precision and recall. Finally, estimation of the spatial distribution of the various plant species is performed via semantic segmentation of the input images at the finest level of granularity in terms of spatial resolution. The Deeplab-V3 semantic segmentation architecture is observed to provide very accurate estimations for abundant plant species; however, a significant degradation in performance is observed in the case of less abundant plant species and, in extreme cases, rare plant classes are seen to be ignored entirely. Overall, a clear trade-off is observed between
Species Distribution Modeling of Carnivorous Plants Worldwide
Forecasting how carnivorous plant species will respond to climatic change is a key issue in their conservation and management but presents a number of challenges. These challenges derive from interactions between the relatively simplistic statistical methods typically used to forecast species responses to climatic change, which to date have been limited mainly to species distribution models (“SDMs) and particular aspects of the ecology of carnivorous plants, including their rarity, habitat specialization, and limited dispersal ability. The small ranges and oftentimes low local abundance of carnivorous plants provide few occurrence records, which increase the potential for poorly or over-fitted SDMs and misspecification of relationships with their “optimal” environments. The unique habitats in which carnivorous plants often grow also are difficult to characterize using the basic temperature and precipitation data that often undergird SDMs. Rather, habitats in which carnivorous plants are common often are decoupled from broader climatic patterns (e.g., many retain high soil moisture even during seasonal drought) and may be associated with frequent disturbance. Last, dispersal limitation also may constrain range shifts of carnivorous plants as the climate changes. These three issues raise two related questions that are critical for understanding and forecasting the future of carnivorous plants. First, to what extent are current carnivorous plants distributions constrained by climate; and second, how readily, if at all, might carnivorous plants disperse to colonize new habitat as it becomes climatically suitable? We estimated the vulnerability of carnivorous plants to climatic change in light of challenges identified with SDMs in general and their particular application to these unique species. We combined two approaches: “ensembles of small models”, which attempt to deal with the challenges of fitting SDMs for data-limited species; and “bioclimatic velocity”, which is
Distribution and habitat suitability maps for Central European steppe plants
<p>This dataset contains distribution maps for Central European steppe plants and coordinates of species occurrence points used by Divíšek et al. (2022) to calibrate habitat suitability models. These models were projected onto past climates and the resulting habitat suitability maps for 10 periods since the Last Glacial Maximum (LGM) are also included. These maps were further used as input data for simulations of species migration from climatically suitable areas in the LGM to identify those that may have served as a source for colonisation of the species' current ranges. For each species, we present maps of climatically suitable areas during the LGM and mid-Holocene (for the latter period, only areas accessible from the LGM are shown), as well as maps of the "source areas" from which the species may have colonised the regions occupied today.</p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km distribution trends for 1930–2019 (long-term) and 1987–2019 (short-term), including country-level breakdowns
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data for the long- (1930–2019) and short- term (1987–2019) 10 x 10 km (“hectad”) distribution trends, presented in both the <em>Plant Atlas 2020</em> book (Stroh et al., 2023) and website (www.plantatlas2020.org).</p>
Stomatal Distribution and Post-fire Recovery: Intra- and Interspecific Variation in Plants of the Pyrogenic Florida Scrub, 2023-2024
Premise of the study: Amphistomy is the presence of stomata on both leaf surfaces. This distribution of stomata can increase photosynthesis, but is relatively infrequent, which is often attributed to high costs such as water loss. This study takes place in the Florida scrub- a hot, dry, shrub-dominated habitat that naturally experiences fire. However, decades of anthropogenic suppression and the reintroduction of controlled burns has created varied fire regimes across the region. In this study, we investigated the links between amphistomy and fire by determining (1) how common the trait is in this habitat, and (2) within-species variation before and after experimental fire, and across a time-since-fire gradient (0.25 - 50 years). Methods: We (1) surveyed 116 plant species across scrub habitats for amphistomy presence, and (2) experimentally and observationally investigated intraspecific variation in stomatal traits in response to fire for two post-fire resprouting species of palmetto, Serenoa repens and Sabal etonia (Arecaceae). Key results: Amphistomy was present in 62.9% of all surveyed species and 85.7% of post-fire obligate reseeders, suggesting amphistomy may be beneficial in this group and in the Florida scrub conditions. The stomatal ratio (upper/total stomatal density) was generally stable in response to fire. Stomatal density decreased following fire in S. etonia, with both species experiencing high variation in the post-fire years. Conclusions: Amphistomy is common in this habitat and relatively stable within species in response to fire, while stomatal density responds plastically during postfire regrowth.
Mechanisms mediating plant distributions across estuarine landscapes in a low-latitude tidal estuary
Understanding of how plant communities are organized and will respond to global changes requires an understanding of how plant species respond to multiple environmental gradients. We examined the mechanisms mediating the distribution patterns of tidal marsh plants along an estuarine gradient in Georgia using a combination of field transplant experiments and monitoring. Our results could not be fully explained by the “competition-to-stress hypothesis” (the current paradigm explaining plant distributions across estuarine landscapes). This hypothesis states that the upstream limits of plant distributions are determined by competition, and the downstream limits by abiotic stress. We found that competition was generally strong in freshwater and brackish marshes, and that conditions in brackish and salt marshes were stressful to freshwater marsh plants, results consistent with the competition-to-stress hypothesis. Four other aspects of our results, however, were not explained by the competition-to-stress hypothesis. First, several halophytes found the freshwater habitat stressful, and performed best (in the absence of competition) in brackish or salt marshes. Second, the upstream distribution of one species was determined by the combination of both abiotic and biotic (competition) factors. Third, marsh productivity (estimated by standing biomass) was a better predictor of relative biotic interaction intensity (RII) than was salinity or flooding, suggesting that productivity is a better indicator of plant stress than salinity or flooding gradients. Fourth, facilitation played a role in mediating the distribution patterns of some plants. Our results illustrate that even apparently simple abiotic gradients can encompass surprisingly complex processes mediating plant distributions.
The dataset of photovoltaic power plant distribution in China by 2020
<p>Photovoltaic (PV) technology, an efficient solution for mitigating the impacts of climate change, has been increasingly used across the world to replace fossil-fuel power to minimize greenhouse gas emissions. With the world's highest cumulative and fastest built PV capacity, China needs to assess the environmental and social impacts of these established photovoltaic (PV) power plants. However, a comprehensive map regarding the PV power plants' locations and extent remain scarce on the country scale. This study developed a workflow combining machine learning and visual interpretation methods with big satellite data to map PV power plants across China. We applied a pixel-based Random Forest (RF) model to classify the PV power plants from composite images in 2020 with 30-meter spatial resolution on Google Earth Engine (GEE). The result classification map was further improved by a visual interpretation approach. Eventually, we established a map of PV power plants in China by 2020, covering a total area of 2917 km<sup>2</sup>. We found that most PV power plants were sited on cropland, followed by barren land and grassland based on the derived national PV map. In addition, the installation of PV power plants has generally decreased the vegetation cover. This new dataset is expected to be conducive to policy management, environmental assessment, and further classification of PV power plants.</p>
Data and code for: Habitat preference of an herbivore shapes the habitat distribution of its host plant
<p>Initial release of analysis and code for:</p> <p>Alexandre, N. M., P. T. Humphrey, A. D. Gloss, J. Lee, J. Frazier, H. A. Affeldt III, and N. K. Whiteman. 2018. Habitat preference of an herbivore shapes the habitat distribution of its host plant. Ecosphere 00(00):e02372. (full citation pending)</p> <p>Release published to accompany corrected proofs on 2018-Jul-26.</p>
Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ
<p>This repository contains data reported in the below study:</p> <p>Atherton, J., Liu, W. and Porcar-Castell, A., 2019. Nocturnal Light Emitting Diode Induced Fluorescence (LEDIF): A new technique to measure the chlorophyll a fluorescence emission spectral distribution of plant canopies in situ. <em>Remote Sensing of Environment</em>.</p> <p>Each text file contains the data-set used to produce the relevant figure (see file name). You can find the data to produce A.4. online at https://avaa.tdata.fi/web/smart/smear/ </p> <p>Please pay attention to the following before using this data.</p> <ol> <li><strong>Figure2_lampRadPanel_Wm2srnm.txt</strong>: Note that the shapes are of interest here. The magnitude is not the same as the incident light at top of canopy, as these spectra were measured in a laboratory. See paper section A.1. for more details. </li> <li><strong>Figure3_LEDIFspectra_Wm2srnm.txt</strong>: This data contains the whole observed spectrum including the non-fluorescence regions, which were saturated (warped) in the visible. The fluorescence region is approximately > 650 nm. </li> <li><strong>Figure4_AQYspectra_nm.txt</strong>: As with Figure3 the whole spectrum is included here.</li> <li><strong>FigureA3_repLEDIFspectra_[pmay/psep/usep]._nm.txt</strong>: Data from which the mean spectra (Figure3) were calculated, including the uncorrected red spectra. I have split these by canopy type to avoid name conflicts.</li> </ol> <p> </p>
Projected distribution of invasive plant species in the tropical Andes under climate change
<p>Distribution maps of 11 invasive species now and in the future (2040-70). The projections were the result of the assembly of three algorithms: Adaptive Boosting (AdaBoost), Boosted Regression Trees (BRT), and Extreme Gradient Boosting (XGBoost). Future projections were made for three global circulation models and three climate change scenarios, each with low (SSP126), medium (SSP370), and high (SSP585) levels of carbon emission.</p> <p>Habitat suitability and presence/absence maps are also included. The threshold for establishing a species as present was determined to be the value that maximized the TSS. </p> <p>For more information, see the article accompanying the dataset by González-Trujillo et al. Mapping the threat: Projecting invasive plant distribution in the tropical Andes under climate change</p> <p>List of modeled invasive plant species and their known impacts in the tropics.</p> <table> <tbody> <tr> <td> <p><strong>Species </strong></p> </td> <td> <p><strong>Biogeographic origin</strong></p> </td> <td> <p><strong>Impacts </strong></p> </td> <td> <p><strong>References</strong></p> </td> <td> <p><strong>GBIF data (DOIs)</strong></p> </td> </tr> <tr> <td> <p><em>Acacia decurrens </em></p> </td> <td> <p>Australian</p> </td> <td> <p>Create regular layers of litter on the ground, inhibit or redirect successional processes, inhibit the expression of seed banks, and limit resource supply, leading to displacement of native plants and animals and increasing the frequency of fires.</p> </td> <td> <p> (Cárdenas López et al., 2017; Le Maitre et al., 2011)</p> </td> <td> <p>https://doi.org/10.15468/dl.mjyxhw</p> </td> </tr> <tr> <td> <p><em>Acacia melanoxylon</em></p> </td> <td> <p>Australian</p> </td> <td> <p>Alter the structure and function of their ecosystems, thereby displacing their native flora. It also causes soil erosion and alters hydrological cycles, negatively affecting agriculture.</p> </td> <td> <p>(Kumschick and Jansen, 2023; Le Maitre et al., 2011)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.4cugnk</p> </td> </tr> <tr> <td> <p><em>Arundo donax</em></p> <p><em> </em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter<em> </em>the natural vegetation structure, outcompete native plant species and diminish the diversity and abundance of animals such as arthropods and birds. It also drives out soil, fuels forest fires, displaces native species, and increases the invasion of ticks that affect livestock.</p> </td> <td> <p>(Cárdenas López et al., 2017; Girotto et al., 2021; Lambert et al., 2010)</p> </td> <td> <p>https://doi.org/10.15468/dl.bfep4t</p> </td> </tr> <tr> <td> <p><em>Genista monspessulana</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter fire regime and nutrient cycling displace native species and decrease native diversity by forming dense monospecific stands. It also facilitates the establishment of other invasive species and produces seeds that are toxic to livestock and humans.</p> </td> <td> <p>(Cárdenas López et al., 2017; Herrera et al., 2016; Pauchard et al., 2008)</p> </td> <td> <p>https://doi.org/10.15468/dl.gyhnxh</p> </td> </tr> <tr> <td> <p><em>Hedychium coronarium </em></p> </td> <td> <p>Indo-Malesian</p> </td> <td> <p>Alter hydrological and nutrient cycles in soil. It forms thickets that suppress the successional and regeneration processes of native species, thus affecting the native flora and crops.</p> </td> <td> <p>(Cárdenas López et al., 2017; Costa et al., 2019)</p> </td> <td> <p>https://doi.org/10.15468/dl.6z2jgb</p> </td> </tr> <tr> <td> <p><em>Melinis minutiflora</em></p> </td> <td> <p>African</p> </td> <td> <p>Increases the occurrence of fires, displaces native species, and alters soil properties and decomposition. It also inhibits the growth of native species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Nogueira et al., 2019; Sandoval et al., 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.fsqwsv</p> </td> </tr> <tr> <td> <p><em>Pteridium aquilinum</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p> (Berget et al., 2015; Cárdenas López et al., 2017; Valdez-Ramírez et al., 2020)</p> <p> </p> </td> <td> <p>https://doi.org/10.15468/dl.sp4uuv</p> </td> </tr> <tr> <td> <p><em>Ricinus communis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter vegetation success processes affect crops and cause livestock poisoning. It also produces acids that inhibit root growth in native and cultivated species.</p> </td> <td> <p>(Cárdenas López et al., 2017; Sandoval et al., 2022; Silva and Fabricante, 2022)</p> </td> <td> <p>https://doi.org/10.15468/dl.dhbphb</p> </td> </tr> <tr> <td> <p><em>Senecio madagascariensis</em></p> </td> <td> <p>African</p> </td> <td> <p>Alter soil nutrient cycles, damage to agricultural crops, and outcompete native species. It also contains substances that are toxic to both animals and humans. </p> </td> <td> <p>(Wijayabandara et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.7e8eyx</p> </td> </tr> <tr> <td> <p><em>Thunbergia alata</em></p> </td> <td> <p>African</p> </td> <td> <p>Displace native species and reduce habitat heterogeneity, thereby affecting the structure and function of native ecosystems.</p> </td> <td> <p>(Cárdenas López et al., 2017; Quijano-Abril et al., 2021)</p> </td> <td> <p>https://doi.org/10.15468/dl.g9zybc</p> </td> </tr> <tr> <td> <p><em>Ulex europeaus</em></p> </td> <td> <p>Holarctic</p> </td> <td> <p>Dry soil and increase the occurrence of fires. Inhibits vegetative growth, including pastures in agricultural and livestock lands.</p> </td> <td> <p>(Anderson and Anderson, 2009; Cárdenas López et al., 2017)</p> </td> <td> <p>https://doi.org/10.15468/dl.6642q9</p> </td> </tr> </tbody> </table>
Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences IX - Plant Identifications 2015
This data set was collected as a part of Brian Houseman's MS Thesis, Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences (December 2017). Data include plant voucher collections that were collected on study plots in 2015. Data were collected on study plots established across two burn scars (2004 Boundary Fire and 1971 Wickersham Dome Fire) within the Yukon-Tanana Uplands ecoregion of interior Alaska.
Figure 5 in Geographic distribution, host plants, and morphological variation of the currently radiating phytophagous ladybird beetle Henosepilachna diekei
Figure 5. Elytra height of seven populations of Henosepilachna diekei. (A) Females; (B) males. The host plants were denoted in the parentheses as M, Mikania; L, Leucas; D, Dicliptera; P, Plectranthus. The different letter on the right shoulder of each box indicates significant difference (P <0.05) after adjustment of P-value for multiple comparisons (NS, P ≥ 0.05).
Fig. 5 in Distribution patterns of selected insect populations on their host plants - an ecological study
Fig. 5: Determination of the grade of aggregation (k) according to two independent methods (see text) and illustration of the relationship between k and xm: (a) greenflies (first method), (b) sap beetles (first method), (c) greenflies (second method), (d) sap beetles (second method).
Fig. 4 in Distribution patterns of selected insect populations on their host plants - an ecological study
Fig. 4: Mean values and standard deviations of the x/s2 ratios for a more detailed differentiation of m the animal distribution patterns. According to the results greenflies and sap beetles colonizing the upper parts of the nettle are distinguished by aggregated distribution patterns, whilst sap beetles residing on the lower parts of the nettle are characterized by a more regular distribution. Mealybugs tend to develop random distribution patterns.
Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations
<p>Plants growing outside their native range may be confronted by new regimes of herbivory, but how this affects plant chemical defense profiles has rarely been studied. Using <em>Plantago lanceolata</em> as a model species, we investigated whether introduced populations show significant differences from native populations in several growth and chemical defense traits. <em>Plantago lanceolata </em>(ribwort plantain) is an herbaceous plant species native to Europe and Western Asia that has been introduced to numerous countries worldwide. We sampled seeds from nine native and ten introduced populations that covered a broad geographic and environmental range and performed a common garden experiment in a greenhouse, in which we infested half of the plants in each population with caterpillars of the generalist herbivore <em>Spodoptera littoralis</em>. We then measured size-related and resource-allocation traits as well as the levels of constitutive and induced chemical defense compounds in roots and shoots of <em>P. lanceolata</em>. When we considered the environmental characteristics of the site of origin, our results revealed that populations from introduced ranges were characterized by an increase of chemical defense compounds without compromising plant biomass. The concentrations of iridoid glycosides and verbascoside, the major anti-herbivore defense compounds of <em>P. lanceolata</em>,<em> </em>were higher in introduced populations than in native populations. In addition, introduced populations exhibited greater rates of herbivore-induced volatile organic compound emission and diversity, and similar chemical diversity based on untargeted analyses of leaf methanol extracts. In general, the geographic origin of the populations had a significant influence on morphological and chemical plant traits, suggesting that <em>P. lanceolata</em> populations are not only adapted to different environments in their native range but also in their introduced range.</p>
Early Eocene Global Vegetation Modern Plant Distribution Dataset
<p>Early Eocene Global Vegetation Modern Plant Distribution Dataset </p> <p>Global occurances for early Eocene fossil plant Nearest Living Relatives (NLRs) from the Global Biodiversity Information Facility (GBIF: https://www.gbif.org/), used for palaeocliate reconstruction.</p>
Fig. 2 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 2. Mean total abundance of Siluriformes from the Jaguariaiva River, Upper Parana River basin by site [capture-per-unit-effort (CPUE); unit: number of individuals/1,000 m² of nets/16 h]. Vertical bars = standard error.
Fig. 4 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 4. Variations in composition of Siluriformes in the three distinct zones in the Nova Jaguaraiva River under the effects of damming. Species richness (beta diversity) was assessed as species dispersion within the three zones using permutational analysis of multivariate dispersions (PERMDISP; e.g., a greater distance to the spatial median indicates a larger dispersion and, therefore, broader beta diversity). The upper and lower hinges correspond to the 25th and 75th quartiles, respectively.
Fig. 1 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 1. Location of sampling sites in the Jaguariaiva River, Upper Parana River basin, Brazil: PCH Nova Jaguariaíva (bar); upstream (red circle); reservoir (black circle); downstream (yellow circle).
Fig. 3 in Distribution of Siluriformes in a river under the influence of a small hydroelectric power plant of the Paraná River Basin, Brazil
Fig. 3. Mean abundance of the Siluriformes species from the Jaguariaiva River, Upper Parana River basin by site [capture-per-unit-effort (CPUE); unit: number of individuals/1,000 m² of nets/16 h]. Vertical bars = standard error (Cher, Corydoras ehrhardti; Halb, Hypostomus albopunctatus; Hanc, Hypostomus ancistroides; Hher, Hypostomus hermanni; Hpau, Hypostomus paulinus; Hstr, Hypostomus strigaticeps; Nsel, Neoplecostomus selenae; Rque, Rhamdia quelen; Tcan, Trichomycterus candidus; Cdia, Cambeva diabola).
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.