Both secondary metabolites and inorganic acids have been hypothesized to protect adult ascidians from predation, raising the possibility of alternative defensive strategies in these sessile, soft-bodied, benthic invertebrates. The objective of this investigation was to determine if ascidian species from the Western Atlantic have these chemical defenses against fish predators, and if so, to determine their location within the body of the ascidian. The palatability of crude organic extracts of whole ascidians, as well as the dissected tunics, viscera, and gonads (when possible) were determined at natural volumetric concentrations using laboratory feeding assays with the bluehead wrasse, Thalassoma bifasciatum. Acidified food pellets were also assayed to determine the effect of lowered pH on predation. Sixteen of the 17 species tested had deterrent organic extracts from some region of the body (Aplidium constellatum, Aplidium stellatum, Ascidia interrupta, Ascidia nigra, Botrylloides sp., Clavellina picta, Didemnum candidum, Didemnum vanderhosti, Diplosoma listerianum, Ecteinasci-dia turbinata, Eudistoma capsulatum, Eudistoma hepaticum, Rhopalaea abdominalis, Styela plicata, Symplegma rubra, and Trididemnum solidum). The location of the deterrent secondary metabolites was isolated in the gonad in all three solitary species, raising the possibility that these defenses are passed on to eggs or larvae. Nine ascidian species sequestered acid in their tunics (A. interrupta, A. nigra, A. stellatum, D. candidum, D. vanderhosti, E. capsulatum, E. hepaticum, R. abdominalis, and T. solidum) at levels that were effective in deterring fish predation (pH V 3.0). Only one species (Botrylloides nigrum) had neither chemical defense. Results of this study indicate that there is not a clear trade-off between the presence of secondary metabolites and inorganic acid defenses in ascidians, suggesting that 0022-0981/02/$ -see front matter D 2002 Elsevier Science B.V. All rights reserved. PII: S 0 0 2 2 -0 9 8 1 ( 0 2 ) 0 0 0 2 3 -0

To locate food, mobile consumers in aquatic habitats perceive and move towards sources of attractive chemicals. There has been much progress in understanding how consumers use chemicals to identify and locate prey despite the elusive identity of odor signals and the complex effects of turbulence on chemical dispersion. This review highlights how integrative studies on behavior, fluid physics, and chemical isolation can be fundamental in elucidating mechanisms that regulate species composition and distribution. We suggest three areas where further research may yield important ecological insights. First, although basic aspects of stimulatory molecules are known, our understanding of how consumers identify prey from a distance remains poor, and the lack of studies examining the influence of distance perception on food preference may result in inaccurate estimation of foraging behavior in the field. Second, the ability of many animals to find prey is greatest in unidirectional, low turbulence flow environments, although recent evidence indicates a trade-off in movement speed versus tracking ability in turbulent conditions. This suggests that predator foraging mode may affect competitive interactions among consumers, and that turbulence provides a hydrodynamic refuge in space or time, leading to particular associations between predator success, prey distributions, and flow. Third, studies have been biased towards examining predator tracking. Current data suggest a variety of mechanisms prey may use to disguise their presence and avoid predation; these mechanisms also may produce associations between prey distributions and flow environments. These examples of how chemical attraction may mediate interactions between consumers and their resources suggest that the ecology of chemically mediated prey perception may be as fundamental to the organization of aquatic communities as the ecology of chemical deterrence.

Given the impact that climate change is projected to have on agriculture, it is essential to understand the mechanisms and conditions that drive agricultural land suitability. However, existing literature does not provide sufficient guidance on the best modeling methodology to study crop suitability, and there is even less research on how to evaluate the accuracy of such models. Further, studies have yet to demonstrate the use of the Maximum Entropy (Maxent) model in predicting presence and yield of large-scale field crops in the United States. In this study, we investigate the application of the Maxent model to predict crop suitability and present novel methods of evaluating its predictive ability. Maxent is a correlative machine learning model often used to predict cropland suitability. In this study, we used Maxent to model land suitability for corn production in the contiguous United States under current bioclimatic conditions. We developed methods for evaluating Maxent’s predictive ability through three comparisons: (i) classification of suitable land units and comparison of results with another similar species distribution model (Random Forest Classification), (ii) comparison of output response curves with existing literature on corn suitability thresholds, and (iii) with correlation of predicted suitability with observed extent and yield. We determined that Maxent was superior to Random Forest, especially in its modeling of areas in which land was likely suitable for corn but was not currently associated with observed corn presence. We also determined that Maxent’s predictions correlated strongly with observed yield statistics and were consistent with existing literature regarding the range of bioclimatic variable values associated with suitable production conditions for corn. We concluded that Maxent was an effective method for modeling current cropland suitability and could be applied to broader issues of agriculture–climate relationships.

<p>Land use / land cover (LULC) maps provide critical information to governments, land use planners, and decision-makers about the spatial layout of the environment and how it is changing.  While a variety of LULC products exist, they are often coarse in resolution, not updated regularly, or require manual editing to be useful.  In partnership, Esri, Microsoft Planetary Computer, and Impact Observatory created the world’s first publicly available 10-m LULC map by automating and sharing a deep-learning model that was run on over 450,000 Sentinel-2 scenes.  The resulting map, released freely on Esri’s Living Atlas in June 2021, displays ten classes across the globe: built area, trees, scrub/shrub, cropland, bare ground, flooded vegetation, water, grassland, permanent snow/ice, clouds.  Here, we discuss key findings from the resulting map, including a quantitative analysis of how 10-m resolution allows us to assess small, low density urban areas compared to other LULC products, including the Copernicus CGLS-LC100 100-m resolution global map.  We will also share how we support project-based, on-demand LULC mapping and will present preliminary findings from a new globally consistent 2017-2021 annual LULC dataset across the entire Sentinel-2 archive.</p>