Towards a framework for assessment and management of cumulative human impacts on marine food webs

Effective ecosystem‐based management requires understanding ecosystem responses to multiple human threats, rather than focusing on single threats. To understand ecosystem responses to anthropogenic threats holistically, it is necessary to know how threats affect different components within ecosystems and ultimately alter ecosystem functioning. We used a case study of a Mediterranean seagrass (Posidonia oceanica) food web and expert knowledge elicitation in an application of the initial steps of a framework for assessment of cumulative human impacts on food webs. We produced a conceptual seagrass food web model, determined the main trophic relationships, identified the main threats to the food web components, and assessed the components’ vulnerability to those threats. Some threats had high (e.g., coastal infrastructure) or low impacts (e.g., agricultural runoff) on all food web components, whereas others (e.g., introduced carnivores) had very different impacts on each component. Partitioning the ecosystem into its components enabled us to identify threats previously overlooked and to reevaluate the importance of threats commonly perceived as major. By incorporating this understanding of system vulnerability with data on changes in the state of each threat (e.g., decreasing domestic pollution and increasing fishing) into a food web model, managers may be better able to estimate and predict cumulative human impacts on ecosystems and to prioritize conservation actions.     

Eliciting expert knowledge in conservation science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.     

Using community‐level metrics to monitor the effects of marine protected areas on biodiversity

Marine protected areas (MPAs) are used to protect species, communities, and their associated habitats, among other goals. Measuring MPA efficacy can be challenging, however, particularly when considering responses at the community level. We gathered 36 abundance and 14 biomass data sets on fish assemblages and used meta‐analysis to evaluate the ability of 22 distinct community diversity metrics to detect differences in community structure between MPAs and nearby control sites. We also considered the effects of 6 covariates—MPA size and age, MPA size and age interaction, latitude, total species richness, and level of protection—on each metric. Some common metrics, such as species richness and Shannon diversity, did not differ consistently between MPA and control sites, whereas other metrics, such as total abundance and biomass, were consistently different across studies. Metric responses derived from the biomass data sets were more consistent than those based on the abundance data sets, suggesting that community‐level biomass differs more predictably than abundance between MPA and control sites. Covariate analyses indicated that level of protection, latitude, MPA size, and the interaction between MPA size and age affect metric performance. These results highlight a handful of metrics, several of which are little known, that could be used to meet the increasing demand for community‐level indicators of MPA effectiveness.     

Metrics for quantifying how much different threats contribute to red lists of species and ecosystems

Red lists are a crucial tool for the management of threatened species and ecosystems. Among the information red lists provide, the threats affecting the listed species or ecosystem, such as pollution or hunting, are of special relevance. This information can be used to quantify the relative contribution of different threat factors to biodiversity loss by disaggregating the cumulative extinction risk across species into components that can be attributed to certain threats. We devised and compared 3 metrics that accomplish this and may be used as indicators. The first metric calculates the portion of the temporal change in red list index (RLI) values that is caused by each threat. The second metric attributes the deviation of an RLI value from its reference value to different threats. The third metric uses extinction probabilities that are inferred from red list categories to estimate the contribution of a threat to the expected loss of species or ecosystems within 50 years. We used data from Norwegian Red Lists to test and evaluate these metrics. The first metric captured only a minor portion of the biodiversity loss caused by threats because it ignores species whose red list category does not change. Management authorities will often be interested in the contribution of a given threat to the total deviation from the optimal state. This was measured by the remaining metrics. The second metric was best suited for comparisons across countries or taxonomic groups. The third metric conveyed the same information but uses numbers of species or ecosystem as its unit, which is likely more intuitive to lay people and may be preferred when communicating with stakeholders or the general public.     

A generalized approach for producing,quantifying, and validating citizen science data from wildlife images

Citizen science has the potential to expand the scope and scale of research in ecology and conservation, but many professional researchers remain skeptical of data produced by nonexperts. We devised an approach for producing accurate, reliable data from untrained, nonexpert volunteers. On the citizen science website www.snapshotserengeti.org , more than 28,000 volunteers classified 1.51 million images taken in a large‐scale camera‐trap survey in Serengeti National Park, Tanzania. Each image was circulated to, on average, 27 volunteers, and their classifications were aggregated using a simple plurality algorithm. We validated the aggregated answers against a data set of 3829 images verified by experts and calculated 3 certainty metrics—level of agreement among classifications (evenness), fraction of classifications supporting the aggregated answer (fraction support), and fraction of classifiers who reported “nothing here” for an image that was ultimately classified as containing an animal (fraction blank)—to measure confidence that an aggregated answer was correct. Overall, aggregated volunteer answers agreed with the expert‐verified data on 98% of images, but accuracy differed by species commonness such that rare species had higher rates of false positives and false negatives. Easily calculated analysis of variance and post‐hoc Tukey tests indicated that the certainty metrics were significant indicators of whether each image was correctly classified or classifiable. Thus, the certainty metrics can be used to identify images for expert review. Bootstrapping analyses further indicated that 90% of images were correctly classified with just 5 volunteers per image. Species classifications based on the plurality vote of multiple citizen scientists can provide a reliable foundation for large‐scale monitoring of African wildlife.     

Meta-analysis of the effects of upstream land cover on stream recovery

Unpredictable or variable ecosystem recovery from disturbance presents a challenge to conservation, particularly as the scale of human disturbance continues to increase. Theory suggests land-cover and disturbance characteristics affect recovery, but individual studies of disturbance and recovery frequently struggle to uncover generalizable patterns because of high levels of site-specific variation. To understand how land-cover, disturbance type, and disturbance duration influence ecosystem recovery, we used studies documenting recovery of 50 streams to perform a global meta-analysis of stream recovery from disturbances that affect water quality (e.g., oil spill, fire, wastewater). We extracted upstream natural and urban land-cover percentages for each site and performed model selection and averaging to identify influences on recovery completeness. Most streams improved following the end of a disturbance (median 240% of disturbed condition) but did not recover fully to baseline predisturbance condition within the studied period (median study period 2 years; median recovery 60% of baseline). Scale of disturbance in time and space did not predict recovery, but sites with higher percentages of upstream natural land cover had less complete recovery relative to sites with more urban or agricultural cover, possibly due to higher baseline conditions in these streams. Our findings suggest impacts to systems with low anthropogenic stress may be more irreversible than impacts to already modified systems. We call for more long-term evaluations of ecosystem response to disturbance and the inclusion of regional references and predisturbance reference conditions for comparison. A more thorough understanding of the role of the surrounding landscape in shaping stream response to disturbance can help managers calibrate expectations for recovery and prioritize protection.     

How expert insight into alpine peatland conservation complements global scientific evidence

Experts can provide valuable information to fill knowledge gaps in published research on management effectiveness, particularly for threatened ecosystems, for which there is often limited evidence and the need for prompt intervention to ensure their persistence. One such ecosystem, alpine peatland, is threatened by climate change and other pressures, provides vital ecosystem services, and supports unique biodiversity. In a workshop, we gathered and synthesized into an accessible format information from experts on interventions used, threat context, and intervention effectiveness for Australian alpine peatland and used this knowledge to evaluate local relevance of the global literature for this threatened ecosystem. Experts identified 15 interventions used to conserve Australian peatlands, most of which enhanced or restored peatland condition and effectively addressed diverse threats. Experts’ perspectives and global studies were strongly aligned, suggesting that research on peatland management may be broadly relevant across contexts, despite the distinct characteristics of Australian systems. Our workshop-based expert elicitation approach provided insights into current management practices unavailable in the literature.     

Setting conservation priorities for migratory networks under uncertainty

Conserving migratory species requires protecting connected habitat along the pathways they travel. Despite recent improvements in tracking animal movements, migratory connectivity remains poorly resolved at a population level for the vast majority of species, thus conservation prioritization is hampered. To address this data limitation, we developed a novel approach to spatial prioritization based on a model of potential connectivity derived from empirical data on species abundance and distance traveled between sites during migration. We applied the approach to migratory shorebirds of the East Asian‐Australasian Flyway. Conservation strategies that prioritized sites based on connectivity and abundance metrics together maintained larger populations of birds than strategies that prioritized sites based only on abundance metrics. The conservation value of a site therefore depended on both its capacity to support migratory animals and its position within the migratory pathway; the loss of crucial sites led to partial or total population collapse. We suggest that conservation approaches that prioritize sites supporting large populations of migrants should, where possible, also include data on the spatial arrangement of sites.     

Testing Hypotheses of Bird Extinctions at Rio Palenque,Ecuador, with Informal Species Lists

Abstract: Informally gathered species lists are a potential source of data for conservation biology, but most remain unused because of questions of reliability and statistical issues. We applied two alternative analytical methods (contingency tests and occupancy modeling) to a 35‐year data set (1973–2007) to test hypotheses about local bird extinction. We compiled data from bird lists collected by expert amateurs and professional scientists in a 2‐km² fragment of lowland tropical forest in coastal Ecuador. We tested the effects of the following on local extinction: trophic level, sociality, foraging specialization, light tolerance, geographical range area, and biogeographic source. First we assessed extinction on the basis of the number of years in which a species was not detected on the site and used contingency tests with each factor to compare the frequency of expected and observed extinction events among different species categories. Then we defined four multiyear periods that reflected different stages of deforestation and isolation of the study site and used occupancy modeling to test extinction hypotheses singly and in combination. Both types of analyses supported the biogeographic source hypothesis and the species‐range hypothesis as causes of extinction; however, occupancy modeling indicated the model incorporating all factors except foraging specialization best fit the data.     

Using a choice experiment and birder preferences to guide bird‐conservation funding

Conservation of biodiversity, including birds, continues to challenge natural‐area managers. Stated‐preference methods (e.g., choice experiment [CE]) are increasingly used to provide data for valuation of natural ecosystems. We used a CE to calculate birders’ willingness to pay for different levels of bioecological attributes (threatened species, endemic species, and diversity) of birding sites with hypothetical entry fees. The CE was delivered at popular birding and avitourism sites in Australia and the United Kingdom. Latent‐class modeling results revealed heterogeneous preferences among birders and correspondingly variable willingness to pay. Four clear groups were apparent: quantity‐driven birders, special‐birds seekers, confused respondents, and price‐is‐no‐object birders. Quantity‐driven birders were attracted to sites that deliver high levels of diversity and endemic species for which they were willing to pay $135 and $66 to visit, respectively, above what they were willing to pay to visit a site with low levels of diversity and few endemic and threatened species . Special‐bird seekers valued threatened species and high levels of endemic species most (willingness to pay $45 and $46, respectively). Confused respondents’ preferences were difficult to determine, but they were the most sensitive to the hypothetical entry fees, unlike the price‐is‐no‐object birders, who were not at all sensitive to cost. Our findings demonstrate that birders are amenable to paying for their preferred birding experience. These payments could provide an alternative source of funding in some avitourism sites on both public and private land. Such alternative revenue streams should be explored and given full consideration in increasingly competitive conservation‐financing environments.     

Adapting the bioblitz to meet conservation needs

When conservation strategies require new, field‐based information, practitioners must find the best ways to rapidly deliver high‐quality survey data. To address this challenge, several rapid‐assessment approaches have been developed since the early 1990s. These typically involve large areas, take many months to complete, and are not appropriate when conservation‐relevant survey data are urgently needed for a specific locale. In contrast, bioblitzes are designed for quick collection of site‐specific survey data. Although bioblitzes are commonly used to achieve educational or public‐engagement goals, conservation practitioners are increasingly using a modified bioblitz approach to generate conservation‐relevant data while simultaneously enhancing research capacity and building working partnerships focused on conservation concerns. We term these modified events expert bioblitzes. Several expert bioblitzes have taken place on lands of conservation concern in Southern California and have involved collaborative efforts of government agencies, nonprofit organizations, botanic gardens, museums, and universities. The results of expert bioblitzes directly informed on‐the‐ground conservation and decision‐making; increased capacity for rapid deployment of expert bioblitzes in the future; and fostered collaboration and communication among taxonomically and institutionally diverse experts. As research and conservation funding becomes increasingly scarce, expert bioblitzes can play an increasingly important role in biodiversity conservation.     

Balancing Forest‐Regeneration Probabilities and Maintenance Costs in Dry Grasslands of High Conservation Priority

Abstract: Abandonment of agricultural land has resulted in forest regeneration in species‐rich dry grasslands across European mountain regions and threatens conservation efforts in this vegetation type. To support national conservation strategies, we used a site‐selection algorithm (MARXAN) to find optimum sets of floristic regions (reporting units) that contain grasslands of high conservation priority. We sought optimum sets that would accommodate 136 important dry‐grassland species and that would minimize forest regeneration and costs of management needed to forestall predicted forest regeneration. We did not consider other conservation elements of dry grasslands, such as animal species richness, cultural heritage, and changes due to climate change. Optimal sets that included 95–100% of the dry grassland species encompassed an average of 56–59 floristic regions (standard deviation, SD 5). This is about 15% of approximately 400 floristic regions that contain dry‐grassland sites and translates to 4800–5300 ha of dry grassland out of a total of approximately 23,000 ha for the entire study area. Projected costs to manage the grasslands in these optimum sets ranged from CHF (Swiss francs) 5.2 to 6.0 million/year. This is only 15–20% of the current total estimated cost of approximately CHF30–45 million/year required if all dry grasslands were to be protected. The grasslands of the optimal sets may be viewed as core sites in a national conservation strategy.     

Use of opportunistic sightings and expert knowledge to predict and compare Whooping Crane stopover habitat

Predicting a species’ distribution can be helpful for evaluating management actions such as critical habitat designations under the U.S. Endangered Species Act or habitat acquisition and rehabilitation. Whooping Cranes (Grus americana) are one of the rarest birds in the world, and conservation and management of habitat is required to ensure their survival. We developed a species distribution model (SDM) that could be used to inform habitat management actions for Whooping Cranes within the state of Nebraska (U.S.A.). We collated 407 opportunistic Whooping Crane group records reported from 1988 to 2012. Most records of Whooping Cranes were contributed by the public; therefore, developing an SDM that accounted for sampling bias was essential because observations at some migration stopover locations may be under represented. An auxiliary data set, required to explore the influence of sampling bias, was derived with expert elicitation. Using our SDM, we compared an intensively managed area in the Central Platte River Valley with the Niobrara National Scenic River in northern Nebraska. Our results suggest, during the peak of migration, Whooping Crane abundance was 262.2 (90% CI 40.2?3144.2) times higher per unit area in the Central Platte River Valley relative to the Niobrara National Scenic River. Although we compared only 2 areas, our model could be used to evaluate any region within the state of Nebraska. Furthermore, our expert‐informed modeling approach could be applied to opportunistic presence‐only data when sampling bias is a concern and expert knowledge is available.     

Use of Coarse‐Resolution Models of Species’ Distributions to Guide Local Conservation Inferences

Abstract: Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus), and a more widespread carnivore, the Eurasian otter (Lutra lutra), in the Iberian Peninsula. The models, built from presence–absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time‐consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse‐resolution distribution data are available to define high‐quality areas at a scale that is practical for the application of concrete conservation measures.     

Multimethod,multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large‐carnivore management is to obtain objective, reliable, and cost‐effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical‐site‐occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost‐effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well‐coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population‐parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

Modeling population effects of the Deepwater Horizon oil spill on a long-lived species

The 2010 Deepwater Horizon (DWH) oil spill exposed common bottlenose dolphins (Tursiops truncatus) in Barataria Bay, Louisiana to heavy oiling that caused increased mortality and chronic disease and impaired reproduction in surviving dolphins. We conducted photographic surveys and veterinary assessments in the decade following the spill. We assigned a prognostic score (good, fair, guarded, poor, or grave) for each dolphin to provide a single integrated indicator of overall health, and we examined temporal trends in prognostic scores. We used expert elicitation to quantify the implications of trends for the proportion of the dolphins that would recover within their lifetime. We integrated expert elicitation, along with other new information, in a population dynamics model to predict the effects of observed health trends on demography. We compared the resulting population trajectory with that predicted under baseline (no spill) conditions. Disease conditions persisted and have recently worsened in dolphins that were presumably exposed to DWH oil: 78% of those assessed in 2018 had a guarded, poor, or grave prognosis. Dolphins born after the spill were in better health. We estimated that the population declined by 45% (95% CI 14–74) relative to baseline and will take 35 years (95% CI 18–67) to recover to 95% of baseline numbers. The sum of annual differences between baseline and injured population sizes (i.e., the lost cetacean years) was 30,993 (95% CI 6607–94,148). The population is currently at a minimum point in its recovery trajectory and is vulnerable to emerging threats, including planned ecosystem restoration efforts that are likely to be detrimental to the dolphins’ survival. Our modeling framework demonstrates an approach for integrating different sources and types of data, highlights the utility of expert elicitation for indeterminable input parameters, and emphasizes the importance of considering and monitoring long-term health of long-lived species subject to environmental disasters. Article impact statement: Oil spills can have long-term consequences for the health of long-lived species; thus, effective restoration and monitoring are needed.     

Incorporating putatively neutral and adaptive genomic data into marine conservation planning

The availability of genomic data for an increasing number of species makes it possible to incorporate evolutionary processes into conservation plans. Recent studies show how genetic data can inform spatial conservation prioritization (SCP), but they focus on metrics of diversity and distinctness derived primarily from neutral genetic data sets. Identifying adaptive genetic markers can provide important information regarding the capacity for populations to adapt to environmental change. Yet, the effect of including metrics based on adaptive genomic data into SCP in comparison to more widely used neutral genetic metrics has not been explored. We used existing genomic data on a commercially exploited species, the giant California sea cucumber (Parastichopus californicus), to perform SCP for the coastal region of British Columbia (BC), Canada. Using a RAD-seq data set for 717 P. californicus individuals across 24 sampling locations, we identified putatively adaptive (i.e., candidate) single nucleotide polymorphisms (SNPs) based on genotype–environment associations with seafloor temperature. We calculated various metrics for both neutral and candidate SNPs and compared SCP outcomes with independent metrics and combinations of metrics. Priority areas varied depending on whether neutral or candidate SNPs were used and on the specific metric used. For example, targeting sites with a high frequency of warm-temperature-associated alleles to support persistence under future warming prioritized areas in the southern coastal region. In contrast, targeting sites with high expected heterozygosity at candidate loci to support persistence under future environmental uncertainty prioritized areas in the north. When combining metrics, all scenarios generated intermediate solutions, protecting sites that span latitudinal and thermal gradients. Our results demonstrate that distinguishing between neutral and adaptive markers can affect conservation solutions and emphasize the importance of defining objectives when choosing among various genomic metrics for SCP.