Evaluating pollution-related damage and restoration success in urban forests with participatory monitoring and digital tools

Peri-urban forest monitoring requires indicators of vegetation damage. An example is the sacred fir (Abies religiosa) forests surrounding Mexico City, which have been heavily exposed to tropospheric ozone, a harmful pollutant, for over 4 decades. We developed a participatory monitoring system with which local community members and scientists generated data on ozone tree damage. Santa Rosa Xochiac rangers (13) used the digital tool KoboToolBox to record ozone damage to trees, tree height, tree ages, tree condition, tree position, and whether the tree had been planted. Thirty-five percent of the trees (n = 1765) had ozone damage. Younger trees had a lower percentage of foliage damaged by ozone than older trees (p < 0.0001), and asymptomatic trees tended to be younger (p < 0.0001). Symptomatic trees were taller than asymptomatic trees of the same age (R²_c = 0.43, R²_m = 0.27). Involving local communities facilitated forest monitoring and using digital technology improved data quality. This participatory system can be used to monitor forest condition change over time and thus aids restoration efforts driven by government or local communities’ interests, facilitating local decision-making.     

Estimating the footprint of pollution on coral reefs with models of species turnover

Ecological communities typically change along gradients of human impact, although it is difficult to estimate the footprint of impacts for diffuse threats such as pollution. We developed a joint model (i.e., one that includes multiple species and their interactions with each other and environmental covariates) of benthic habitats on lagoonal coral reefs and used it to infer change in benthic composition along a gradient of distance from logging operations. The model estimated both changes in abundances of benthic groups and their compositional turnover, a type of beta diversity. We used the model to predict the footprint of turbidity impacts from past and recent logging. Benthic communities far from logging were dominated by branching corals, whereas communities close to logging had higher cover of dead coral, massive corals, and soft sediment. Recent impacts were predicted to be small relative to the extensive impacts of past logging because recent logging has occurred far from lagoonal reefs. Our model can be used more generally to estimate the footprint of human impacts on ecosystems and evaluate the benefits of conservation actions for ecosystems.     

Cost‐Effectiveness of Using Small Vertebrates as Indicators of Disturbance

In species‐rich tropical forests, effective biodiversity management demands measures of progress, yet budgetary limitations typically constrain capacity of decision makers to assess response of biological communities to habitat change. One approach is to identify ecological‐disturbance indicator species (EDIS) whose monitoring is also monetarily cost‐effective. These species can be identified by determining individual species’ responses to disturbance across a gradient; however, such responses may be confounded by factors other than disturbance. For example, in mountain environments the effects of anthropogenic habitat alteration are commonly confounded by elevation. EDIS have been identified with the indicator value (IndVal) metric, but there are weaknesses in the application of this approach in complex montane systems. We surveyed birds, small mammals, bats, and leaf‐litter lizards in differentially disturbed cloud forest of the Ecuadorian Andes. We then incorporated elevation in generalized linear (mixed) models (GL(M)M) to screen for EDIS in the data set. Finally, we used rarefaction of species accumulation data to compare relative monetary costs of identifying and monitoring EDIS at equal sampling effort, based on species richness. Our GL(M)M generated greater numbers of EDIS but fewer characteristic species relative to IndVal. In absolute terms birds were the most cost‐effective of the 4 taxa surveyed. We found one low‐cost bird EDIS. In terms of the number of indicators generated as a proportion of species richness, EDIS of small mammals were the most cost‐effective. Our approach has the potential to be a useful tool for facilitating more sustainable management of Andean forest systems. Rentabilidad del Uso de Pequeños Vertebrados como Indicadores de Perturbaciones     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos     

Mechanistic understanding of human–wildlife conflict through a novel application of dynamic occupancy models

Crop and livestock depredation by wildlife is a primary driver of human–wildlife conflict, a problem that threatens the coexistence of people and wildlife globally. Understanding mechanisms that underlie depredation patterns holds the key to mitigating conflicts across time and space. However, most studies do not consider imperfect detection and reporting of conflicts, which may lead to incorrect inference regarding its spatiotemporal drivers. We applied dynamic occupancy models to elephant crop depredation data from India between 2005 and 2011 to estimate crop depredation occurrence and model its underlying dynamics as a function of spatiotemporal covariates while accounting for imperfect detection of conflicts. The probability of detecting conflicts was consistently <1.0 and was negatively influenced by distance to roads and elevation gradient, averaging 0.08–0.56 across primary periods (distinct agricultural seasons within each year). The probability of crop depredation occurrence ranged from 0.29 (SE 0.09) to 0.96 (SE 0.04). The probability that sites raided by elephants in primary period t would not be raided in primary period t + 1 varied with elevation gradient in different seasons and was influenced negatively by mean rainfall and village density and positively by distance to forests. Negative effects of rainfall variation and distance to forests best explained variation in the probability that sites not raided by elephants in primary period t would be raided in primary period t + 1. With our novel application of occupancy models, we teased apart the spatiotemporal drivers of conflicts from factors that influence how they are observed, thereby allowing more reliable inference on mechanisms underlying observed conflict patterns. We found that factors associated with increased crop accessibility and availability (e.g., distance to forests and rainfall patterns) were key drivers of elephant crop depredation dynamics. Such an understanding is essential for rigorous prediction of future conflicts, a critical requirement for effective conflict management in the context of increasing human–wildlife interactions.     

Spatial predictions of phylogenetic diversity in conservation decision making

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative.     

Predicted Climate‐Driven Bird Distribution Changes and Forecasted Conservation Conflicts in a Neotropical Savanna

Abstract: Climate‐change scenarios project significant temperature changes for most of South America. We studied the potential impacts of predicted climate‐driven change on the distribution and conservation of 26 broad‐range birds from South America Cerrado biome (a savanna that also encompass tracts of grasslands and forests). We used 12 temperature or precipitation‐related bioclimatic variables, nine niche modeling techniques, three general circulation models, and two climate scenarios (for 2030, 2065, 2099) for each species to model distribution ranges. To reach a consensus scenario, we used an ensemble‐forecasting approach to obtain an average distribution for each species at each time interval. We estimated the range extent and shift of each species. Changes in range size varied across species and according to habitat dependency; future predicted range extent was negatively correlated with current predicted range extent in all scenarios. Evolution of range size under full or null dispersal scenarios varied among species from a 5% increase to an 80% decrease. The mean expected range shifts under null and full‐dispersal scenarios were 175 and 200 km, respectively (range 15–399 km), and the shift was usually toward southeastern Brazil. We predicted larger range contractions and longer range shifts for forest‐ and grassland‐dependent species than for savanna‐dependent birds. A negative correlation between current range extent and predicted range loss revealed that geographically restricted species may face stronger threat and become even rarer. The predicted southeasterly direction of range changes is cause for concern because ranges are predicted to shift to the most developed and populated region of Brazil. Also, southeastern Brazil is the least likely region to contain significant dispersal corridors, to allow expansion of Cerrado vegetation types, or to accommodate creation of new reserves.     

Re-creating missing population baselines for Pacific reef sharks

Sharks and other large predators are scarce on most coral reefs, but studies of their historical ecology provide qualitative evidence that predators were once numerous in these ecosystems. Quantifying density of sharks in the absence of humans (baseline) is, however, hindered by a paucity of pertinent time-series data. Recently researchers have used underwater visual surveys, primarily of limited spatial extent or nonstandard design, to infer negative associations between reef shark abundance and human populations. We analyzed data from 1607 towed-diver surveys (>1 ha transects surveyed by observers towed behind a boat) conducted at 46 reefs in the central-western Pacific Ocean, reefs that included some of the world's most pristine coral reefs. Estimates of shark density from towed-diver surveys were substantially lower (<10%) than published estimates from surveys along small transects (<0.02 ha), which is not consistent with inverted biomass pyramids (predator biomass greater than prey biomass) reported by other researchers for pristine reefs. We examined the relation between the density of reef sharks observed in towed-diver surveys and human population in models that accounted for the influence of oceanic primary productivity, sea surface temperature, reef area, and reef physical complexity. We used these models to estimate the density of sharks in the absence of humans. Densities of gray reef sharks (Carcharhinus amblyrhynchos), whitetip reef sharks (Triaenodon obesus), and the group "all reef sharks" increased substantially as human population decreased and as primary productivity and minimum sea surface temperature (or reef area, which was highly correlated with temperature) increased. Simulated baseline densities of reef sharks under the absence of humans were 1.1-2.4/ha for the main Hawaiian Islands, 1.2-2.4/ha for inhabited islands of American Samoa, and 0.9-2.1/ha for inhabited islands in the Mariana Archipelago, which suggests that density of reef sharks has declined to 3-10% of baseline levels in these areas.     

Software for prioritizing conservation actions based on probabilistic information

Marxan is the most common decision-support tool used to inform the design of protected-area systems. The original version of Marxan does not consider risk and uncertainty associated with threatening processes affecting protected areas, including uncertainty about the location and condition of species’ populations and habitats now and in the future. We described and examined the functionality of a modified version of Marxan, Marxan with Probability. This software explicitly considers 4 types of uncertainty: probability that a feature exists in a particular place (estimated based on species distribution models or spatially explicit population models); probability that features in a site will be lost in the future due to a threatening process, such as climate change, natural catastrophes, and uncontrolled human interventions; probability that a feature will exist in the future due to natural successional processes, such as a fire or flood; and probability the feature exists but has been degraded by threatening processes, such as overfishing or pollution, and thus cannot contribute to conservation goals. We summarized the results of 5 studies that illustrate how each type of uncertainty can be used to inform protected area design. If there were uncertainty in species or habitat distribution, users could maximize the chance that these features were represented by including uncertainty using Marxan with Probability. Similarly, if threatening processes were considered, users minimized the chance that species or habitats were lost or degraded by using Marxan with Probability. Marxan with Probability opens up substantial new avenues for systematic conservation planning research and application by agencies.     

Conserving alpha and beta diversity in wood-production landscapes

International demand for wood and other forest products continues to grow rapidly, and uncertainties remain about how animal communities will respond to intensifying resource extraction associated with woody bioenergy production. We examined changes in alpha and beta diversity of bats, bees, birds, and reptiles across wood production landscapes in the southeastern United States, a biodiversity hotspot that is one of the principal sources of woody biomass globally. We sampled across a spatial gradient of paired forest land-uses (representing pre and postharvest) that allowed us to evaluate biological community changes resulting from several types of biomass harvest. Short-rotation practices and residue removal following clearcuts were associated with reduced alpha diversity (−14.1 and −13.9 species, respectively) and lower beta diversity (i.e., Jaccard dissimilarity) between land-use pairs (0.46 and 0.50, respectively), whereas midrotation thinning increased alpha (+3.5 species) and beta diversity (0.59). Over the course of a stand rotation in a single location, biomass harvesting generally led to less biodiversity. Cross-taxa responses to resource extraction were poorly predicted by alpha diversity: correlations in responses between taxonomic groups were highly variable (−0.2 to 0.4) with large uncertainties. In contrast, beta diversity patterns were highly consistent and predictable across taxa, where correlations in responses between taxonomic groups were all positive (0.05–0.4) with more narrow uncertainties. Beta diversity may, therefore, be a more reliable and information-rich indicator than alpha diversity in understanding animal community response to landscape change. Patterns in beta diversity were primarily driven by turnover instead of species loss or gain, indicating that wood extraction generates habitats that support different biological communities.