The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance Manuscript spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplank-ton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the shortwave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radio-metric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.
We developed a species-by-species approach for selecting protected areas for conservation of native freshwater fishes in semiarid regions, with catchment as the fundamental landscape unit for conservation efforts. Input data were composed of occurrences of freshwater fishes and landscape variables, and general quantification of occurrence, abundance, and endemicity of each fish species, to derive an index of conservation value for each species. Probability of occurrence of each species was performed with logistic-regression analysis based on the landscape variables and extrapolated to the entire study area with a geographic information system. We estimated the conservation value of a stream reach by summing the predicted probability of occurrence of each species multiplied by its corresponding conservation value. To define and select reserves, we used a threshold that maximizes conservation value of the stream reaches but identifies the minimum number of reaches for protection. The approach was applied to native freshwater fishes in the Guadiana River basin (southern Iberian Peninsula), which are threatened by the construction of two major dams. We used the data from 1 sampling year (1999) to produce the models, which were validated based on data collected in 2000 and 2001. We used variables of climate (3), geomorphology (4), hydrology (7), and human influence (6) to build the predictive models, which revealed that native species occur over a wide range of riverine habitats, with stream order and location in the basin the most frequently selected variables. The conservation value of species varied considerably, with Anaecypris hispanica the highest-ranked species. The catchments selected for reserves were the mainstream of the Guadiana River (upstream and downstream of the Alqueva and Pedrogão reservoirs) and the Degebe, Ardila, and Enxoé catchments. Our approach is a pragmatic way to address the urgent need to protect Guadiana native fish species in light of the ongoing anthropogenic degradation of aquatic environments.Selección deÁreas Prioritarias para la Conservación de Peces en la cuenca del Río Guadiana, Península Ibérica Resumen: Desarrollamos un método especie-específico para seleccionaráreas protegidas para la conservación de peces nativos de agua dulce en regiones semiáridas, con la subcuenca como unidad paisajística fundamental para los esfuerzos de conservación. Los datos de entrada fueron las ocurrencias de peces de agua dulce y las variables de paisaje, una cuantificación general de la ocurrencia, abundancia y endemicidad de cada especie piscícola para obtener uníndice del valor de conservación para cada especie. La probabilidad de ocurrencia de cada especie se obtuvo con análisis de regresión logística basado en las variables del paisaje y extrapolado a toda elárea de estudio con un sistema de información geográfica. Estimamos el valor de conservación de un tramo sumando la probabilidad de ocurrencia de cada especie multiplicado por su valor de conservación correspondiente. Para definir y seleccio...
Opportunistic citizen science databases are becoming an important way of gathering information on species distributions. These data are temporally and spatially dispersed and could have limitations regarding biases in the distribution of the observations in space and/or time. In this work, we test the influence of landscape variables in the distribution of citizen science observations for eight taxonomic groups. We use data collected through a Portuguese citizen science database (biodiversity4all.org). We use a zero-inflated negative binomial regression to model the distribution of observations as a function of a set of variables representing the landscape features plausibly influencing the spatial distribution of the records. Results suggest that the density of paths is the most important variable, having a statistically significant positive relationship with number of observations for seven of the eight taxa considered. Wetland coverage was also identified as having a significant, positive relationship, for birds, amphibians and reptiles, and mammals. Our results highlight that the distribution of species observations, in citizen science projects, is spatially biased. Higher frequency of observations is driven largely by accessibility and by the presence of water bodies. We conclude that efforts are required to increase the spatial evenness of sampling effort from volunteers.
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