Peer-reviewed publications

Mangrove forests support unique biodiversity and provide a suite of ecosystem services (ES) that benefit people. Decades of continual mangrove loss and degradation have necessitated global efforts to protect and restore this important ecosystem. Generating and evaluating asset maps of biodiversity and ES is an important precursor to identifying locations that can deliver conservation outcomes across varying scales, such as maximising the co-occurrence of specific ES. We bring together global datasets on mangrove-affiliated biodiversity, carbon stocks, fish and invertebrate production, and coastal protection to provide insight into potential trade-offs, synergies and opportunities from mangrove conservation. We map opportunities where high ES provision co-occurs with these areas that could be leveraged in conservation planning, and identify potential high-value opportunities for single ES that might otherwise be missed with a biodiversity focus. Hotspots of single ES, co-occurrence of multiple ES, and opportunities to simultaneously leverage biodiversity and ES occurred throughout the world. For example, efforts that focus on conserving or restoring mangroves to store carbon can be targed to deliver multiple ES benefits. Some nations, such as Vietnam, Oman, Ecuador and China, showed consistent (although not necessarily strong) correlations between ES pairs. A lack of clear or consistent spatial trends elsewhere suggests that some nations will likely benefit more from complementarity-based approaches that focus on multiple sites with high provision of different services. Individual sites within these nations, however, such as Laguna de Terminos in Mexico still provide valuable opportunities to leverage co-benefits. Ensuring that an ES focused approach is complemented by strategic spatial planning is a priority, and our analyses provide a precursor towards decisions about where and how to invest.

We thank Scholte, Kamgang, & Sabuhoro (2023) and Hausmann (2023) for their perceptive commentaries on our study of what attracts ecotourists to Sub-Saharan African protected areas (Eyster, Naidoo, & Chan, 2022).

Monitoring of terrestrial and aquatic species assemblages at large spatial scales based on environmental DNA (eDNA) has the potential to enable evidence-based environmental policymaking. The spatial coverage of eDNA-based studies varies substantially, and the ability of eDNA metabarcoding to capture regional biodiversity remains to be assessed; thus, questions about best practices in the sampling design of entire landscapes remain open. We tested the extent to which eDNA sampling can capture the diversity of a region with highly heterogeneous habitat patches across a wide elevation gradient for five days through multiple hydrological catchments of the Swiss Alps. Using peristaltic pumps, we filtered 60 L of water at five sites per catchment for a total volume of 1800 L. Using an eDNA metabarcoding approach focusing on vertebrates and plants, we detected 86 vertebrate taxa spanning 41 families and 263 plant taxa spanning 79 families across ten catchments. For mammals, fishes, amphibians and plants, the detected taxa covered some of the most common species in the region according to long-term records while including a few more rare taxa. We found marked turnover among samples from distinct elevational classes indicating that the biological signal in alpine rivers remains relatively localised and is not aggregated downstream. Accordingly, species compositions differed between catchments and correlated with catchment-level forest and grassland cover. Biomonitoring schemes based on capturing eDNA across rivers within biologically integrated catchments may pave the way toward a spatially comprehensive estimation of biodiversity.

Based on a review of >50,000 academic publications, policy documents, and Indigenous and local knowledge sources, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Values Assessment examined current knowledge on the diversity of nature’s values and valuation methods to gain insights into their role in policymaking and to their fuller integration into decisions. It presents an inclusive typology of the multiple conceptualizations of nature’s values and their interrelationships, and shows global patterns of where and how valuation studies have been conducted. It reveals that >50 well established valuation methods from diverse disciplines and traditions are available to decision makers. Yet, <5% of published studies document uptake of values information into decisions, and few valuation efforts adequately represent or engage stakeholder diversity in valuation processes. Furthermore, evidence shows that people and nature benefit when nature’s multiple values are considered in the design and implementation of biodiversity conservation and territorial management policies (e.g., protected areas, payments for ecosystem services). Theassessment concludes that combinations of values-centred approaches can leverage transformative changes towards more just and sustainable futures.

Over the past few decades, the use of camera-traps has revolutionized our ability to monitor populations of wild terrestrial mammals. While methods to estimate abundance from individually-identifiable animals are well-established, they are mostly restricted to species with clear natural markings or else necessitate invasive and often costly animal tagging campaigns. Estimating abundance or density from unmarked animals remains challenging. Several models recently developed to deal with this issue are promising, but are not widely used by field ecologists. Here, we developed a framework for applying the Space-To-Event (STE) model—originally designed to be used with time-lapse images—on motion-triggered camera-trap data. Our approach involves performing bootstrap resampling on the photographic dataset to generate multiple datasets that are then used as input to the STE model. We tested our approach on 29 datasets, including 17 ungulate species from eight sites, in six different countries and various ecosystems. Then, we conducted a regression analysis to evaluate how variations in ecological and sampling conditions across studies affected the bias and precision of our STE density estimates. Our study shows that with a bootstrap resampling approach and information on animal activity and effective detection distances to animals, the STE model can be used to analyze motion-trigger datasets and provide population density estimates that are similar to those from other methods. We found that measuring the camera viewshed was critical to prevent major negative biases in density estimates. Moreover, using a 1-s sampling window was important to avoid the positive bias that results from violating the instantaneous-sampling assumption. We found that precision increased with greater sampling effort and higher density populations. Based on these results, we highlight several issues from past studies that have applied the original timelapse-based STE to motion-trigger datasets, issues that our bootstrap resampling approach addresses. We caution that the STE model, whether applied to timelapse or motion-triggered datasets, relies on strict assumptions. Any violations of these assumptions, such as non-instantaneous sampling or the application of angle and distance of detection provided by the camera manufacturer, can cause biases in multiple directions that may be difficult to differentiate.

Study Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided machine learning model) in a data-sparse region. New Hydrological Insights for the Region: The machine learning model (HydroForecast) generally performs better – in terms of statistical fit between simulated and observed flows – than the conceptual model (Pitman). For the Kwando River, the conceptual model explicitly simulates the expected attenuation effects of a large floodplain, while the machine learning model represents this and other processes implicitly. The two models quantify the Kwando sub-basin flow contributions differently, with the conceptual model calibrated manually to align with the available qualitative information that suggests that the majority of the runoff is generated in the upstream sub-basin and then attenuated in the downstream floodplain. Generally, this work offers insight into how the two very different models can simulate historical flows in a large basin when streamflow observations and the forcing rainfall data are limited and of unknown quality, and suggests that a machine learning model better leverages information from multiple training parameters to reproduce the measured streamflows.

Marine Protected Areas (MPAs) are key management tools that contribute to the conservation of worldwide marine ecosystems, increasing nature's contributions to people derived from ecosystem services. These ecosystem services include key processes such as the release of oxygen, but also leisure opportunities, cultural inspiration, and food and medicine provision that improve the health and well-being of millions of people. In this Chapter we explain how natural processes and components in MPAs are valued by different groups of people; and how these can be addressed to maximize their effectiveness, avoiding negative socio-economic effects like social conflicts, or inequitable distribution of benefits. We recommend that creation and management decision-making in MPAs include the collection and integration of interdisciplinary data to develop pluralistic methods of valuation and fostering social equity by involving local stakeholders.

The HydroSHEDS database provides seamless hydrographic data to support hydro-ecological research and applications on a regional to global scale. A steadily increasing availability and accuracy of remote sensing data promotes the development of a second and refined version of HydroSHEDS, which is based on the TanDEM-X dataset. To derive hydrographic information from the topographic data, the TanDEM-X digital elevation model (DEM) requires editing, which is summarized in the so-called hydrologic pre-conditioning. The processing steps include an infill of voids and outliers in the DEM and the delineation of a global high-resolution coastline. Furthermore, a global water mask is generated to edit rough and noisy appearing open water surfaces in the DEM. An urban correction layer is calculated to reduce distortions in river flow paths due to built-up areas. Within the HydroSHEDS workflow, the preconditioning will be complemented with refined hydrological optimization and correction algorithms. Compared to HydroSHEDS v1, the resulting hydrologically conditioned DEM ensures a more accurate derivation of flow direction and flow accumulation maps.

Increasing human pressures are driving a global loss of biodiversity and of Nature’s Contributions to People (NCP)—the contributions of living nature to people’s quality of life. Understanding the spatial relationship between biodiversity and NCP is essential for securing Earth’s life support systems. Here we estimate the importance of high-biodiversity regions in maintaining the provision of three NCP under four scenarios of climate change. We focus on critical regulatory NCP which are currently facing decline: regulation of air quality, climate and freshwater quantity. We estimate the current and future value of NCP using a suite of environmental indicators and evaluate whether risk from environmental change is higher or lower within high-biodiversity regions compared with control regions. We find higher levels of NCP within high-biodiversity regions both in the present and the future for all indicators, which highlights the spatial congruence between biodiversity and NCP. Moreover, air quality and climate regulation indicators show rapidly increasing levels within high-biodiversity regions, especially under higher-emission scenarios. Our results point to a substantial contribution of high-biodiversity areas to the provision of NCP. Protecting areas of high biodiversity value will synergistically contribute to the preservation of many of nature’s contributions humanity depends on.

In the pathway toward environmental sustainability, it is important that we understand how individuals can make a difference through diverse types of engagement. Theories suggest that transformative change toward a sustainable future requires individuals to engage in not only private actions (e.g. household energy saving, recycling) but also social-signalling and system-changing civic actions (e.g. opinion sharing, voting, petition signing and protesting). Yet, past research on pro-environmental behaviour has primarily focused on private actions, while overlooking individual contributions to facilitating widespread change through civic actions.We use the exotic pet trade as a focal case to understand how individuals may act to promote environmental sustainability through different patterns of engagement and what factors might explain these distinct patters of action.Results from an online survey about behavioural intentions in the United States (n = 527) revealed three types of individual action that could transform the exotic pet trade.Private actions clustered separately from civic actions. Within the category of civic actions, a distinction emerged between lower social-commitment actions and higher social-commitment actions, based on the perceived level of social engagement and personal efforts involved.We also found that each type of action was associated with unique factors, highlighting the importance of attitudes, perceived social norms, and relational values for variously promoting individual engagement among the U.S. public.Our findings suggest that these distinct types of action should be treated differently when designing future wildlife conservation campaigns and behaviour change interventions.