Transforming ecology and conservation biology through genome editing

As the conservation challenges increase, new approaches are needed to help combat losses in biodiversity and slow or reverse the decline of threatened species. Genome-editing technology is changing the face of modern biology, facilitating applications that were unimaginable only a decade ago. The technology has the potential to make significant contributions to the fields of evolutionary biology, ecology, and conservation, yet the fear of unintended consequences from designer ecosystems containing engineered organisms has stifled innovation. To overcome this gap in the understanding of what genome editing is and what its capabilities are, more research is needed to translate genome-editing discoveries into tools for ecological research. Emerging and future genome-editing technologies include new clustered regularly interspaced short palindromic repeats (CRISPR) targeted sequencing and nucleic acid detection approaches as well as species genetic barcoding and somatic genome-editing technologies. These genome-editing tools have the potential to transform the environmental sciences by providing new noninvasive methods for monitoring threatened species or for enhancing critical adaptive traits. A pioneering effort by the conservation community is required to apply these technologies to real-world conservation problems.     

A genoscape-network model for conservation prioritization in a migratory bird

Migratory animals are declining worldwide and coordinated conservation efforts are needed to reverse current trends. We devised a novel genoscape-network model that combines genetic analyses with species distribution modeling and demographic data to overcome challenges with conceptualizing alternative risk factors in migratory species across their full annual cycle. We applied our method to the long distance, Neotropical migratory bird, Wilson's Warbler (Cardellina pusilla). Despite a lack of data from some wintering locations, we demonstrated how the results can be used to help prioritize conservation of breeding and wintering areas. For example, we showed that when genetic, demographic, and network modeling results were considered together it became clear that conservation recommendations will differ depending on whether the goal is to preserve unique genetic lineages or the largest number of birds per unit area. More specifically, if preservation of genetic lineages is the goal, then limited resources should be focused on preserving habitat in the California Sierra, Basin Rockies, or Coastal California, where the 3 most vulnerable genetic lineages breed, or in western Mexico, where 2 of the 3 most vulnerable lineages overwinter. Alternatively, if preservation of the largest number of individuals per unit area is the goal, then limited conservation dollars should be placed in the Pacific Northwest or Central America, where densities are estimated to be the highest. Overall, our results demonstrated the utility of adopting a genetically based network model for integrating multiple types of data across vast geographic scales and better inform conservation decision-making for migratory animals.     

Scaling range sizes to threats for robust predictions of risks to biodiversity

Assessments of risk to biodiversity often rely on spatial distributions of species and ecosystems. Range‐size metrics used extensively in these assessments, such as area of occupancy (AOO), are sensitive to measurement scale, prompting proposals to measure them at finer scales or at different scales based on the shape of the distribution or ecological characteristics of the biota. Despite its dominant role in red‐list assessments for decades, appropriate spatial scales of AOO for predicting risks of species’ extinction or ecosystem collapse remain untested and contentious. There are no quantitative evaluations of the scale‐sensitivity of AOO as a predictor of risks, the relationship between optimal AOO scale and threat scale, or the effect of grid uncertainty. We used stochastic simulation models to explore risks to ecosystems and species with clustered, dispersed, and linear distribution patterns subject to regimes of threat events with different frequency and spatial extent. Area of occupancy was an accurate predictor of risk (0.81<|r|<0.98) and performed optimally when measured with grid cells 0.1–1.0 times the largest plausible area threatened by an event. Contrary to previous assertions, estimates of AOO at these relatively coarse scales were better predictors of risk than finer‐scale estimates of AOO (e.g., when measurement cells are <1% of the area of the largest threat). The optimal scale depended on the spatial scales of threats more than the shape or size of biotic distributions. Although we found appreciable potential for grid‐measurement errors, current IUCN guidelines for estimating AOO neutralize geometric uncertainty and incorporate effective scaling procedures for assessing risks posed by landscape‐scale threats to species and ecosystems.     

Five lessons to guide more effective biodiversity conservation message framing

Communication and advocacy approaches that influence attitudes and behaviors are key to addressing conservation problems, and the way an issue is framed can affect how people view, judge, and respond to an issue. Responses to conservation interventions can also be influenced by subtle wording changes in statements that may appeal to different values, activate social norms, influence a person's affect or mood, or trigger certain biases, each of which can differently influence the resulting engagement, attitudes, and behavior. We contend that by strategically considering how conservation communications are framed, they can be made more effective with little or no additional cost. Key framing considerations include, emphasizing things that matter to the audience, evoking helpful social norms, reducing psychological distance, leveraging useful biases, and, where practicable, testing messages. These lessons will help communicators think strategically about how to frame messages for greater effect.     

Integrating intraseasonal grassland dynamics in cross-scale distribution modeling to support waterbird recovery plans

Despite much discussion about the utility of remote sensing for effective conservation, the inclusion of these technologies in species recovery plans remains largely anecdotal. We developed a modeling approach for the integration of local, spatially measured ecosystem functional dynamics into a species distribution modeling (SDM) framework in which other ecologically relevant factors are modeled separately at broad scales. To illustrate the approach, we incorporated intraseasonal water-vegetation dynamics into a cross-scale SDM for the Common Snipe (Gallinago gallinago), which is highly dependent on water and vegetation dynamics. The Common Snipe is an Iberian grassland waterbird characteristic of European agricultural meadows and a member of one of the most threatened bird guilds. The intraseasonal dynamics of water content of vegetation were measured using the standard deviation of the normalized difference water index time series computed from bimonthly images of the Sentinel-2 satellite. The recovery plan for the Common Snipe in Galicia (northwestern Iberian Peninsula) provided an opportunity to apply our modeling framework. Model accuracy in predicting the species’ distribution at a regional scale (resulting from integration of downscaled climate projections with regional habitat–topographic suitability models) was very high (area under the curve [AUC] of 0.981 and Boyce's index of 0.971). Local water-vegetation dynamic models, based exclusively on Sentinel-2 imagery, were good predictors (AUC of 0.849 and Boyce's index of 0.976). The predictive power improved (AUC of 0.92 and Boyce's index of 0.98) when local model predictions were restricted to areas identified by the continental and regional models as priorities for conservation. Our models also performed well (AUC of 0.90 and Boyce's index of 0.93) when projected to updated water-vegetation conditions. Our modeling framework enabled incorporation of key ecosystem processes closely related to water and carbon cycles while accounting for other factors ecologically relevant to endangered grassland waterbirds across different scales, allowed identification of priority areas for conservation, and provided an opportunity for cost-effective recovery planning by monitoring management effectiveness from space.     

Bridging information domains to improve ecological understanding of biological invasions in a marine ecosystem

We devised a practical method for integrating information on 2 marine invasive species using 3 different approaches: standardized ecological monitoring, online-reporting databases, and surveys of anglers and crabbers. Focusing on 2 recently introduced species with different characteristics, the Asian shore crab (Hemigrapsus sanguineus) and Chinese mitten crab (Eriocheir sinensis), in the Hudson-Raritan watershed of New York and New Jersey, we used sensitivity analyses to explore the relative contribution of each information source to knowledge of species abundance and distribution. All 3 information sources contributed something unique to understanding abundance and distribution of the introduced crabs. Online and survey data on Asian shore crabs significantly affected predictions of abundance, whereas monitoring data did not. When survey data were omitted, abundance estimates were unchanged over time, but when they were included, the model predicted an increased abundance in 2012. All 3 data sets for the Asian shore crab significantly affected estimates of species coverage; surveys had the biggest influence, increasing range size by 4097.25 km². For the catadromous Chinese mitten crab, ecological monitoring data collected in freshwater shortly after the original sighting significantly shaped model estimates for abundance and documented the establishment phase of the mitten crab in an area outside the spatial scope of the surveyed resource users. However, the survey data significantly enlarged mitten crab range-size estimates by 6498.01 km². By demonstrating that data integration produced an image of the invasion process that would not have emerged had we used any 1 method individually, model results provide evidence for the advantages of an interdisciplinary approach.     

Extinction rate of discovered and undiscovered plants in Singapore

Extinction is a key issue in the assessment of global biodiversity. However, many extinction rate measures do not account for species that went extinct before they could be discovered. The highly developed island city–state of Singapore has one of the best-documented tropical floras in the world. This allowed us to estimate the total rate of floristic extinctions in Singapore since 1822 after accounting for sampling effort and crypto extinctions by collating herbaria records. Our database comprised 34,224 specimens from 2076 native species, of which 464 species (22%) were considered nationally extinct. We assumed that undiscovered species had the same annual per-species extinction rates as discovered species and that no undiscovered species remained extant. With classical and Bayesian algorithms, we estimated that 304 (95% confidence interval, 213–414) and 412 (95% credible interval, 313–534) additional species went extinct before they could be discovered, respectively; corresponding total extinction rate estimates were 32% and 35% (range 30–38%). We detected violations of our 2 assumptions that could cause our extinction estimates, particularly the absolute numbers, to be biased downward. Thus, our estimates should be treated as lower bounds. Our results illustrate the possible magnitudes of plant extirpations that can be expected in the tropics as development continues.     

Negative effects of nitrogen deposition on Swiss butterflies

Nitrogen (N) deposition from agriculture and combustion of fossil fuels is a major threat to plant diversity, but its effects on organisms at higher trophic levels are unclear. We investigated how N deposition may affect species richness and abundance (number of individuals per species) in butterflies. We reviewed the peer-reviewed literature on variables used to explain spatial variation in butterfly species richness and found that vegetation variables appeared to be as important as climate and habitat variables in explaining butterfly species richness. It thus seemed likely that increased N deposition could indirectly affect butterfly communities via its influence on plant communities. To test this prediction, we analyzed data from the Swiss biodiversity monitoring program for vascular plants and butterflies in 383 study sites of 1 km² that are evenly distributed throughout Switzerland. The area has a modeled N deposition gradient of 2–44 kg N ha⁻¹ year⁻¹. We used traditional linear models and structural equation models to infer the drivers of the spatial variation in butterfly species richness across Switzerland. High N deposition was consistently linked to low butterfly diversity, suggesting a net loss of butterfly diversity through increased N deposition. We hypothesize that at low elevations, N deposition may contribute to a reduction in butterfly species richness via microclimatic cooling due to increased plant biomass. At higher elevations, negative effects of N deposition on butterfly species richness may also be mediated by reduced plant species richness. In most butterfly species, abundance was negatively related to N deposition, but the strongest negative effects were found for species of conservation concern. We conclude that in addition to factors such as intensified agriculture, habitat fragmentation, and climate change, N deposition is likely to play a key role in negatively affecting butterfly diversity and abundance.     

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures—ways of generating matching pairs at different scales—in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.     

Ethical considerations when conservation research involves people

Social science is becoming increasingly important in conservation, with more studies involving methodologies that collect data from and about people. Conservation science is a normative and applied discipline designed to support and inform management and practice. Poor research practice risks harming participants and, researchers, and can leave negative legacies. Often, those at the forefront of field-based research are early-career researchers, many of whom enter their first research experience ill-prepared for the ethical conundrums they may face. We draw on our own experiences as early-career researchers to illuminate how ethical challenges arise during conservation research that involves human participants. Specifically, we considered ethical review procedures, conflicts of values, and power relations, and devised broad recommendations on how to navigate ethical challenges when they arise during research. In particular, we recommend researchers apply reflexivity (i.e., thinking that allows researchers to recognize the effect researchers have on the research) to help navigate ethical challenges and encourage greater engagement with ethical review processes and the development of ethical guidelines for conservation research that involves human participants. Such guidelines must be accompanied by the integration of rigorous ethical training into conservation education. We believe our experiences are not uncommon and can be avoided and hope to spark discussion to contribute to a more socially just conservation.