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Figure 2 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 2. Cumulative percent shatter over four time periods (soybean physiological maturity, maturity þ 2 wk,maturity þ 3 wk, maturity þ 4 wk) for each species.The darker the bar, the greater percent of sampled site-years that corresponded to the percent shatter value. This normalizes across species with different sampling efforts. Species sampled in just a single site-year are indicated by a single black square, which represents 100% of the sampling effort. Species are denoted by their EPPO codes.
Figure 3 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 3. Cumulative percent seed shatter for all species from planting date to soybean physiological maturity (black vertical line) for each state in 2016 and 2017. Species are denoted by their EPPO codes.
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity þ2 wk on October 24 in the state that year. Hence only data from maturity þ3 wk are for Arkansas for 2017.)
Figure 5 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 5. Distribution and mean monthly trap captures of Dacus longicornis, in relation with abiotic factors and host fruit availability.
Figure 4 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 4. Distribution and mean monthly trap captures of Zeugodacus cucurbitae (A) and Z. tau (B), in relation with abiotic factors and host fruit availability.
Figure 3 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 3. Distribution and mean monthly trap captures of Bactrocera rubigina (A) and B. correcta (B), in relation with abiotic factors.
Figure 2 in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 2. Distribution and mean monthly trap captures of Bactrocera dorsalis (A) and B. zonata (B), in relation with abiotic factors and host fruit availability.
Figure 1. A in Seasonal Abundance of Economically Important Fruit Flies (Diptera: Tephritidae: Dacinae) in Bangladesh, in Relation to Abiotic Factors and Host Plants
Figure 1. A: Fruit fly trapping sites maintained at the Atomic Energy Research Establishment compound in Bangladesh in 2016–2017 (sites 1 to 10) and 2017–2018 (sites 1, 8, 9). B: Mean monthly rainfall and minimum and maximum temperature recorded in Dhaka, Bangladesh, during the study period.
Fig. 1 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 1: Map of the study area. The darker regions highlighted correspond to Spanish coastal Autonomous Communities.
Fig. 4 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 4: Economic indicator by: A) the main fishing modalities: spearfishing, shore-fishing and boat-fishing and B) spearfishing diving approach.
Fig. 3 in It is recreational but profitability also matters: A cost-effective economic approach to marine recreational fishing in Spain Abstract
Fig. 3: Daily expenses. A) Spearfishing by diving approach and B) Boat fishing (angling) by type of vessel. The percentage of responses by modality in brackets.
Figure 1 in A review of nonlethal and lethal control tools for managing the damage of invasive birds to human assets and economic activities
Figure 1. The number of studies (i.e., field, lab, and modeling) using A) lethal methods to control populations at nesting, foraging, loafing, and roosting sites, B) nonlethal methods to control damage at urban nesting, foraging, loafing, and roosting sites, and C) nonlethal methods to control damage at agricultural foraging sites, including those conducted in the native or introduced ranges of the following birds considered invasive in the United States: rock doves (Columba livia; RODO), Eurasian collared doves (Streptopelia decaocto; EUCD), rose-ringed parakeets (Psittacula krameri; RRPA), monk parakeets (Myiopsitta monachus; MOPA), common mynas (Acridotheres tristis; COMY), European starlings (Sturnus vulgaris; EUST), and house sparrows (Passer domesticus; HOSP). Above each column on the left-hand side is the number of studies that were conducted in the field (i.e., not laboratory or modeling studies; if a study used multiple tools it was counted for each tool). The right-hand side is the subset of field studies for each category (D–F) that included damage assessments in the results.
Figure 1 in Integrating landscape simulation models with economic and decision tools for invasive species control
Figure 1. Example state and transition simulation model for an invasive species. Landscape change is captured by defining the processes (transitions) that can move a cell from one state to another. These include both natural transitions (e.g., species dispersal, establishment, growth, fire, disturbance) and management transitions (e.g., inventory, treatment, and other activities related to invasion control). In this example, modified from Jarnevich et al. (2015), each box represents the state of a simulation cell with respect to invasive species cover (uninvaded, <5% cover, 5–50% cover, or> 50% cover; left to right) and detection (undetected or detected; top to bottom). The different color-coded arrows represent different types of transitions including growth (invasion, establishment, spread), detection (failure and success), and management (treatment and maintenance failure and success). Solid lines represent success; dotted lines represent failure.
Figure 2 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
Figure 2. Cumulative percent shatter over four time periods (maturity, maturity + 2 wk, maturity + 3 wk, maturity + 4 wk) for each species. The darker the bar, the greater percent of sampled site-years that corresponded to the percent shatter value. This normalizes across species with different sampling efforts. Species sampled in just a single site-year are indicated by a single black square, which represents 100% of the sampling effort. Species are denoted by their EPPO codes
Figure 3 in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
Figure 3. Cumulative percent seed shatter for all species from planting date to soybean physiological maturity (black vertical line) across the participating states in 2016 and 2017.
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a in Seed-shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species
Figure 1. Heat map indicating the cumulative percent seed shatter across the participating states for a window starting from soybean physiological maturity to 4 wk past physiological maturity in 2016 and 2017. States were included in these maps only if they conducted sampling during the week indicated. (e.g., In 2017, Arkansas sampled on October 2, October 18, and November 3, none of which are within ±3 d of the October 10 maturity date or maturity +2 wk on October 24 in the state that year. Hence only data from maturity +3 wk are for Arkansas for 2017.)
Figure 2 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 2. Sustainability of the programs with stacked HT traits or residual herbicides, as influenced by application time (PRE and POST) and number of herbicide SOAs on (A) weed density and (B) resistance evolution. Resistance evolution is presented as % individuals that are resistant to at least one of the herbicides excluding H, either in the form of single or multiple resistance.The populations consist of 80% individuals resistant to H initially. Herbicide scenarios are detailed in Table 2. The simulations were set to stop when weed density exceeded 1 plant m−2, hence the incomplete lines of scenario EWM(i).
Figure 1 in Modeling the sustainability and economics of stacked herbicide-tolerant traits and early weed management strategy for waterhemp (Amoronthus tuberculotus) control
Figure 1. Sustainability of the POST-only programs,as influenced by the number of herbicide SOAs and the initial level of quantitative resistance to herbicide H. Cross-resistance between herbicides H and X is included in D–F. Results are presented as the year of weed control failure; bars represent the mean, and error bars represent the range of 100 replicates. Herbicide scenarios are detailed in Table 2. r-HX, correlation coefficient between phenotypic values of H and X.
Expanding the plant economics spectrum with root nitrogen reallocation
<p>Harnessing root nitrogen reallocation (RNR) for optimization of plant productivity commences with positioning RNR in root economics space about which we still know little. We conducted a global synthesis linking RNR to root traits, combined with a two-year <sup>15</sup>N-labelling field experiment to position RNR in plant economics spectrum under acidification. RNR correlated negatively with specific root length (SRL) and mycorrhizal colonization globally, suggesting that RNR is a conservative trait. Sedges, grasses and forbs coordinated root traits (e.g., SRL) from acquisitive to conservative and from low to high RNR reliance (and <em>vice versa</em> for their direct-root N uptake) in the <sup>15</sup>N-tracing experiment. Specifically, sedges and forbs exhibited the lowest and highest RNR that increased and decreased with acidification, respectively. Grasses cooperated well with mycorrhizas, showing moderate RNR and root traits. Our results demonstrated the significance of RNR in plant growth, and the necessity of considering RNR as a conservative trait.</p>
Analysing socio-economic and environmental circumstances of rural communities
<table> <tbody> <tr> <td>The data presented are derived from desk studies, and were used to assess landscape characteristics, socio-economic conditions, farming practices, needs, and perceptions regarding agroecology, adaptive capacity, vulnerability to climate change, and capacity-building needs. The data were based on primary data collected in the field and the biophysical characteristics of each ALL.</td> </tr> </tbody> </table>
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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.