Developing a Monitoring Program for Invertebrates: Guidelines and a Case Study

Abstract:  Invertebrates provide the majority of ecosystem services; thus, it is important that they be inventoried, monitored, and protected. Nevertheless, inventories, monitoring, and management generally focus on vertebrates and flowering plants. Consequently, there are few guidelines or case studies for invertebrates. We present a procedure for developing a monitoring program for species-rich invertebrates that entails (1) characterizing the community; (2) identifying surrogates for biodiversity; and (3) establishing efficient methods to monitor surrogates and any ecologically important or sensitive taxa. We used these procedures, biodiversity-based statistical advances, and a survey of arthropods to develop a monitoring plan for the forests of Shenandoah National Park, Virginia (U.S.A.). Our case study revealed that mixed hardwood and hemlock forests had significantly different compositions of arthropods in their soil and understory strata. Of the 10 orders tested Coleoptera and Hymenoptera were the only two to pass most of the five surrogate tests, and their combination improved predictions of overall arthropod diversity. Because arthropods represent the majority of macroscopic species in most ecosystems, the ability of this assemblage to predict overall arthropod diversity makes it a powerful surrogate. Of the 11 collecting methods used, the beat-sheet method was the most efficient for monitoring this surrogate assemblage. To complement this coarse-filter approach to monitoring at-risk, invasive, or other important taxa (fine filter), we used ordination analyses to match 66 taxa with the methods that most effectively sampled them. Our methods serve as a model for developing an invertebrate monitoring plan and should facilitate linking such monitoring with ecosystem functions and management.     

Variability in Population Abundance and the Classification of Extinction Risk

Abstract: Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50–80%; vulnerable, decline in abundance of 30–50%; least concern or near threatened, decline in abundance of 0–30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state‐space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state‐space models were low (maximum 3–14%), but 33–75% of small declines (30–50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.     

National Red Listing Beyond the 2010 Target

Abstract: Following creation of the 2010 Biodiversity Target under the Convention on Biological Diversity and adoption of the United Nations Millennium Development Goals, information on status and trends of biodiversity at the national level has become increasingly important to both science and policy. National red lists (NRLs) of threatened species may provide suitable data for reporting on progress toward these goals and for informing national conservation priority setting. This information will also become increasingly important for developing species‐ and ecosystem‐based strategies for climate change adaptation. We conducted a thorough global review of NRLs in 109 countries and analyzed gaps in NRL coverage in terms of geography and taxonomy to determine priority regions and taxonomic groups for further investment. We then examined correlations between the NRL data set and gross domestic product (GDP) and vertebrate species richness. The largest geographic gap was in Oceania, followed by middle Africa, the Caribbean, and western Africa, whereas the largest taxonomic gaps were for invertebrates, fungi, and lichens. The comprehensiveness of NRL coverage within a given country was positively correlated with GDP and negatively correlated with total vertebrate richness and threatened vertebrate richness. This supports the assertion that regions with the greatest and most vulnerable biodiversity receive the least conservation attention and indicates that financial resources may be an integral limitation. To improve coverage of NRLs, we propose a combination of projects that target underrepresented taxa or regions and projects that provide the means for countries to create or update NRLs on their own. We recommend improvements in knowledge transfer within and across regions as a priority for future investment.     

Synergies and trade‐offs in achieving global biodiversity targets

After their failure to achieve a significant reduction in the global rate of biodiversity loss by 2010, world governments adopted 20 new ambitious Aichi biodiversity targets to be met by 2020. Efforts to achieve one particular target can contribute to achieving others, but different targets may sometimes require conflicting solutions. Consequently, lack of strategic thinking might result, once again, in a failure to achieve global commitments to biodiversity conservation. We illustrate this dilemma by focusing on Aichi Target 11. This target requires an expansion of terrestrial protected area coverage, which could also contribute to reducing the loss of natural habitats (Target 5), reducing human‐induced species decline and extinction (Target 12), and maintaining global carbon stocks (Target 15). We considered the potential impact of expanding protected areas to mitigate global deforestation and the consequences for the distribution of suitable habitat for >10,000 species of forest vertebrates (amphibians, birds, and mammals). We first identified places where deforestation might have the highest impact on remaining forests and then identified places where deforestation might have the highest impact on forest vertebrates (considering aggregate suitable habitat for species). Expanding protected areas toward locations with the highest deforestation rates (Target 5) or the highest potential loss of aggregate species’ suitable habitat (Target 12) resulted in partially different protected area network configurations (overlapping with each other by about 73%). Moreover, the latter approach contributed to safeguarding about 30% more global carbon stocks than the former. Further investigation of synergies and trade‐offs between targets would shed light on these and other complex interactions, such as the interaction between reducing overexploitation of natural resources (Targets 6, 7), controlling invasive alien species (Target 9), and preventing extinctions of native species (Target 12). Synergies between targets must be identified and secured soon and trade‐offs must be minimized before the options for co‐benefits are reduced by human pressures.     

A strategy for the next decade to address data deficiency in neglected biodiversity

Measuring progress toward international biodiversity targets requires robust information on the conservation status of species, which the International Union for Conservation of Nature (IUCN) Red List of Threatened Species provides. However, data and capacity are lacking for most hyperdiverse groups, such as invertebrates, plants, and fungi, particularly in megadiverse or high-endemism regions. Conservation policies and biodiversity strategies aimed at halting biodiversity loss by 2020 need to be adapted to tackle these information shortfalls after 2020. We devised an 8-point strategy to close existing data gaps by reviving explorative field research on the distribution, abundance, and ecology of species; linking taxonomic research more closely with conservation; improving global biodiversity databases by making the submission of spatially explicit data mandatory for scientific publications; developing a global spatial database on threats to biodiversity to facilitate IUCN Red List assessments; automating preassessments by integrating distribution data and spatial threat data; building capacity in taxonomy, ecology, and biodiversity monitoring in countries with high species richness or endemism; creating species monitoring programs for lesser-known taxa; and developing sufficient funding mechanisms to reduce reliance on voluntary efforts. Implementing these strategies in the post-2020 biodiversity framework will help to overcome the lack of capacity and data regarding the conservation status of biodiversity. This will require a collaborative effort among scientists, policy makers, and conservation practitioners.     

Improving collaboration in the implementation of global biodiversity conventions

Eight conventions make up the biodiversity cluster of multilateral environmental agreements (MEAs) that provide the critical international legal framework for the conservation and sustainable use of nature. However, concerns about the rate of implementation of the conventions at the national level have triggered discussions about the effectiveness of these MEAs in halting the loss of biodiversity. Two main concerns have emerged: lack of capacity and resources and lack of coherence in implementing multiple conventions. We focused on the latter and considered the mechanisms by which international conventions are translated into national policy. Specifically, we examined how the Strategic Plan for Biodiversity 2011–2020 and the associated Aichi Biodiversity Targets have functioned as a unifying grand plan for biodiversity conservation. This strategic plan has been used to coordinate and align targets to promote and enable more effective implementation across all biodiversity-related conventions. Results of a survey of 139 key stakeholders from 88 countries suggests streamlining across ministries and agencies, improved coordination mechanisms with all relevant stakeholders, and better knowledge sharing between conventions could improve cooperation among biodiversity-related conventions. The roadmap for improving synergies among conventions agreed to at the 13th Convention on Biological Diversity's Conference of Parties in 2016 includes actions such as mechanisms to avoid duplication in national reporting and monitoring on conventions and capacity building related to information and knowledge sharing. We suggest the scientific community can actively engage and contribute to the policy process by establishing a science-policy platform to address knowledge gaps; improving data gathering, reporting, and monitoring; developing indicators that adequately support implementation of national plans and strategies; and providing evidence-based recommendations to policy makers. The latter will be particularly important as 2020 approaches and work to develop a new biodiversity agenda for the next decade is beginning.     

Matching biodiversity indicators to policy needs

At the global scale, biodiversity indicators are typically used to monitor general trends, but are rarely implemented with specific purpose or linked directly to decision making. Some indicators are better suited to predicting future change, others are more appropriate for evaluating past actions, but this is seldom made explicit. We developed a conceptual model for assigning biodiversity indicators to appropriate functions based on a common approach used in economics. Using the model, indicators can be classified as leading (indicators that change before the subject of interest, informing preventative actions), coincident (indicators that measure the subject of interest), or lagging (indicators that change after the subject of interest has changed and thus can be used to evaluate past actions). We classified indicators based on ecological theory on biodiversity response times and management objectives in 2 case studies: global species extinction and marine ecosystem collapse. For global species extinctions, indicators of abundance (e.g., the Living Planet Index or biodiversity intactness index) were most likely to respond first, as leading indicators that inform preventative action, while extinction indicators were expected to respond slowly, acting as lagging indicators flagging the need for evaluation. For marine ecosystem collapse, indicators of direct responses to fishing were expected to be leading, while those measuring ecosystem collapse could be lagging. Classification defines an active role for indicators within the policy cycle, creates an explicit link to preventative decision-making, and supports preventative action.