Frontiers in Ecology and the Environment最新文献

Successful conservation of migratory birds relies on coordinated management across international borders. Here, we determined the geographic and taxonomic coverage of international agreements aimed at protecting migratory birds. We identified 49 international migratory bird agreements spanning 187 countries and covering 1,677 (86%) of the world’s 1,958 migratory bird species. Fewer such agreements were located in middle-income countries characterized by less effective governance, small size, and few bordering countries. Threatened species were listed in fewer agreements than non-threatened species. Waterbird species tended to be listed individually by species name, while non-waterbird species tended to be covered implicitly through the listing of higher taxonomic ranks such as Families or Orders. Of the migratory bird species, only 28% had all their range countries participating in at least one agreement, while 14% had none. With large geographic gaps remaining, much work needs to be done to expand the global network of migratory bird agreements.

The value of scientists engaging with community members and other public audiences is widely recognized, and there is a growing literature devoted to the theory and practice of public engagement with science. However, as a group of professionals concerned with how public engagement is understood and practiced in the fields of ecology and environmental science, we see a need for accessible guidance for scientists who want to engage effectively, and for scientific leaders who want to support successful public engagement programs in their institutions. Here, we highlight six attributes of successful public engagement efforts led by scientists and scientific institutions: (1) strategic, (2) cumulative, (3) reciprocal, (4) reflexive, (5) equitable, and (6) evidence-based. By designing and developing practices that incorporate these attributes, scientists and scientific organizations will be better poised to build two-way linkages with communities that, over time, support science-informed decision-making in society and societally informed decision-making in science.

Over the past few decades, we’ve witnessed an explosion in the amount of data available to ecologists. We can now measure the greenness of the planet from satellites; track the movements of individual organisms across the globe; and obtain real-time, high-frequency information from sensor networks distributed across land, air, and aquatic systems. But is the current interest in big data distracting us from measuring what truly matters?

Clearly, so much ecological research involves careful experimental design and considerations of statistical power. But not every hypothesis can be tested with experiments. Here, I am more focused on observational studies with large, often publicly available, datasets. Much of my own research has concentrated on this type of work. Monitoring for the sake of monitoring is important as it can lead to surprising results or new questions we never envisioned. At the same time, I believe that, at both individual and institutional levels, we need to be thoughtful about how we design new monitoring programs or use data from existing programs.

In some cases, the right variables often prove difficult to measure, while the wrong ones remain within easy reach. For example, imagine you are studying what may be driving invertebrate population dynamics in a temperate estuary. Temperature loggers cost little to deploy, and temperature data may already be available from existing monitoring programs. Each logger can collect millions of datapoints over a short time window, even if there is little variation over time. In addition, we may have equal rationale to consider other variables, such as dissolved oxygen or pH, which are harder and costlier to monitor. The sheer volume of temperature data and relative ease in its collection can create the illusion of importance, but convenience is not the same as relevance. What’s more, when our response variables and predictors are constrained by what data are available, the scope of questions we can ask is also limited.

The same dynamic plays out with new technologies—from eDNA to acoustic recorders to GPS tags—that generate reams of new data. These tools expand what we can measure, but they don’t tell us what we should measure. Too often, our technological tunnel vision drives the questions we ask, drawing attention away from the data that may be harder to collect but ultimately more important.

The abundance of data also brings new challenges. With large datasets, issues of data quality and bias can easily go unnoticed, creating a false sense of confidence that more data automatically translates into better science. In hypothesis testing, very large samples reduce standard errors, making even trivial relationships appear statistically significant—though they may have little or no biological meaning.

To overcome these challenges, we need to return to the roots of our discipline. What questions do we want to address? By choosing the questions ourselves, i

Collaborations between biodiversity conservation and the arts can lead to synergies and fresh approaches to intractable problems. These collaborations can yield diverse mutual benefits, such as offering reciprocal sources of inspiration, information, and learning; providing one another with new tools and resources for synthesis and innovation; securing funding; and contributing to increased visibility and influence. The arts may be uniquely poised to raise awareness, influence behavioral change, improve well-being, and assist with developing conservation tools and materials. Likewise, conservation can provide artists with relevant expertise, nature-based art material, samples, and resources, as well as inform sustainability aspects of the arts. Effective synergies between the arts and conservation will necessitate greater funding and institutional support, improved willingness to collaborate, better recognition of the benefits of artists’ involvement in interdisciplinary conservation teams, and sound empirical methods to gauge such collaborations.

Climate-change adaptation planning processes and tools are increasing in number and evolving rapidly. During times of innovation and proliferation, a potential danger is incoherence, when well-intended contributions can overwhelm, create confusion, or mask complementarities. A shared vision is needed to avoid duplication, reduce misunderstandings, and facilitate work across jurisdictions to steward resources undergoing profound changes. Such a vision would document fundamental tools and approaches while allowing flexibility to match highly varied management contexts and organizational missions. Fortunately, the preconditions for coherence exist. Here, we illustrate how climate-change adaptation tools—including scenario planning, conceptual frameworks, structured decision making, and impact-evaluation methods—can be (and are) used in tandem to support adaptation planning that accounts for uncertainty, considers a broad range of strategies and actions, makes transparent and robust choices, evaluates outcomes, and is flexible and responsive to the decision context.

Strict protection is a fundamental component of any strategy for biodiversity protection and ecosystem restoration. Most Western countries, however, currently fail to acknowledge its relevance in their conservation programs. The European Green Deal (EGD)—the climate neutrality strategy launched by the European Union (EU) in 2019—is a marked exception. By identifying strict protection as one of the key solutions to ensure ecosystem health, the EGD reasserts the importance of strict protection both at the regional and global level. However, its approach is far from perfect. To harness the full potential of strict protection, the EU must clarify the existing regulatory framework and strengthen its commitment to ecological sustainability.

Strict protection is a specific type of ecosystem management, one characterized by control and limitation of human visitation, use, and impacts in particular areas, with a view to ensure protection of their high biodiversity value and geological and geomorphological features (Dudley 2008).

Strict protection falls under land-sparing approaches, which can be integrated with diverse land-sharing management solutions to form a cohesive conservation strategy. But it is particularly relevant to ecosystems that require, by reason of their distinguishing features, a high degree of ecological protection and a restrictive management approach—as is the case with primary and old-growth forests and other wilderness areas, such as wetlands, peatlands, and grasslands. Strict reserves are recognized as the most effective solution in area-based conservation, offering better protection for natural spaces against human pressures (eg Jones et al. 2018). While some local economic activities might face initial adjustments, the success of strict protection hinges on effective enforcement and community engagement, fostered through clear communication and financial mechanisms like payments for ecosystem services (Wang et al. 2024).

Despite its potential, however, strict protection does not enjoy great consideration in the Western world, particularly in relation to primary and old-growth forests, which continue to decline at alarming rates both in Europe and globally (Mikolāš et al. 2023). Despite the implementation of important international initiatives, such as the inclusion of forests in natural and mixed UNESCO World Heritage sites, we are witnessing a daily erosion of the unique biodiversity heritage contained in the planet’s ecosystems, including but not limited to those in the tropical biome. The situation is particularly negative in the US, where the US Forest Service announced in January 2025 that it would no longer “prepare an environmental impact statement (EIS) for the Land Management Plan Direction for Old-Growth Forest Conditions Across the National Forest System”.

In this global context, the EU is a remarkable exception. As open

Distributed experimental networks have emerged as a powerful approach in field ecology, enabling experimental replication across global gradients. These networks use standardized treatments at dispersed sites to identify factors like climate or soil that shape biotic responses. Reserving space for future “add-on” work fosters discovery by transforming distributed networks into distributed experimental infrastructure. However, challenges include balancing feasibility, plot impacts, and demands on site scientists. Using the Disturbance and Recovery Across Grasslands Network (DRAGNet) as a case study informed by lessons learned in the Nutrient Network (NutNet), we outline effective practices for designing add-on work to retain the original experiment’s integrity while effectively using the resources of the network participants. By following guidelines for hypothesis-driven, inclusive research that engages contributors intellectually, minimizes plot impacts using field-tested protocols, and maximizes scientific impact and inclusion, distributed networks can become valuable infrastructure for advancing ecological understanding.

Protected areas influence their surroundings in a variety of ways. These “spillover effects” can change an area’s conservation value and affect its social license. Advanced statistical tools for quantifying protected area spillovers are well established, but underlying assumptions about spillover geography and measurement often lack clarity. Although spillover effects should decline with increasing distance from a protected area, there are no published guidelines for determining the rate, magnitude, or scale of decline and there is no conceptual framework to guide tests of alternative hypotheses. Current practices heavily increase the risk of Type II errors (detecting spillover where none exists), particularly at distances far from protected areas. I propose an approach that recognizes alternative forms for ecological and biophysical spillovers and background variance as competing hypotheses. In particular, careful consideration must be given to the question of how many measurements are needed to rigorously distinguish spillovers from background variance in study environments.