Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R² = 0.65). Estimators based on a single MODIS NDVI image performed worse (R² ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.

The intensity and speed of human alterations to the planet’s ecosystems are yielding our static, ahistorical view of biodiversity obsolete. Human actions frequently trigger fast evolutionary responses, affect extant genetic variation and result in the establishment of new communities and co‐evolutionary networks for which we lack past analogues. Contemporary evolution interplays with ecological changes to determine the response of organisms and ecosystems to anthropogenic pressures. Examples on wild species include responses to harvest (e.g. fisheries, hunting, angling), habitat loss and fragmentation (e.g. genetic effects of isolation), biotic exchange (e.g. evolutionary responses to control measures), climate change (e.g. local adaptation and its interplay with dispersal processes) and the responses of endangered species to conservation measures. A review of international and EU biodiversity policies showed numerous opportunities for the integration of evolutionary knowledge, with the realistic prospect of improving their efficacy. Such opportunities should be extended to other sectoral policies of direct relevance for biodiversity – notably nature conservation, fisheries, agriculture, water resources, spatial planning and climate change. These avenues for improvement are, however, challenged by the low level of enforcement of biodiversity policies, linked to the nonbinding nature of most biodiversity‐policy documents, and the decreasing representation of biodiversity in EU’s research policy.

In the “digital conservation” age, big data from Earth observations and from social media have been increasingly used to tackle conservation challenges. Here, we combined information from those two digital sources in a multimodel inference framework to identify, map, and predict the potential for nature's cultural contributions to people in two contrasting UNESCO biosphere reserves: Doñana and Sierra Nevada (Spain). The content analysis of Flickr pictures revealed different cultural contributions, according to the natural and cultural values of the two reserves. Those contributions relied upon landscape variables computed from Earth observation data: the variety of colors and vegetation functioning that characterize Doñana landscapes, and the leisure facilities, accessibility features, and heterogeneous landscapes that shape Sierra Nevada. Our findings suggest that social media and Earth observations can aid in the cost‐efficient monitoring of nature's contributions to people, which underlie many Sustainable Development Goals and conservation targets in protected areas worldwide.

Traditional policies for water resources management and wetland conservation are often based on command-and-control approaches. The latter tend to drive the human–wetland–water system into pathological states, characterized by more vulnerable ecosystems and rigid institutions for governance. The overcoming of these states may rest in the development of flexible and adaptive institutional regimes that rely on adaptive governance and management. Because past factors might constrain the implementation of more flexible adaptive approaches to management, it is important to understand the historical mechanisms underlying the genesis of institutional rigidity. We first present the results of a historical analysis of Doñana, which can be characterized as a pathological water socio-ecosystem governed through rigid institutional regimes for water resources management and wetland conservation. In a second step, we analyze the advances achieved during a recent, large-scale restoration program for the Doñana wetlands, which adhered explicitly to the tenets of adaptive management. Our analysis indicated that the historical persistence of command-and-control approaches has been a path-dependent process that led to the emergence of a rigid institutional regime and caused it to enter a rigidity trap. However, the achievements of the restoration program suggest that a more flexible and adaptive regime could be developed through the introduction of adaptive management at the operational levels, using specifically tailored action research programs. To conclude, we speculate that the research strategy outlined could be extended to comply with, or complement, the requirements of the EU's Water Framework Directive in other European water socio-ecosystems