Importance of timely metadata curation to the global surveillance of genetic diversity

Genetic diversity within species represents a fundamental yet underappreciated level of biodiversity. Because genetic diversity can indicate species resilience to changing climate, its measurement is relevant to many national and global conservation policy targets. Many studies produce large amounts of genome-scale genetic diversity data for wild populations, but most (87%) do not include the associated spatial and temporal metadata necessary for them to be reused in monitoring programs or for acknowledging the sovereignty of nations or Indigenous peoples. We undertook a distributed datathon to quantify the availability of these missing metadata and to test the hypothesis that their availability decays with time. We also worked to remediate missing metadata by extracting them from associated published papers, online repositories, and direct communication with authors. Starting with 848 candidate genomic data sets (reduced representation and whole genome) from the International Nucleotide Sequence Database Collaboration, we determined that 561 contained mostly samples from wild populations. We successfully restored spatiotemporal metadata for 78% of these 561 data sets (n = 440 data sets with data on 45,105 individuals from 762 species in 17 phyla). Examining papers and online repositories was much more fruitful than contacting 351 authors, who replied to our email requests 45% of the time. Overall, 23% of our email queries to authors unearthed useful metadata. The probability of retrieving spatiotemporal metadata declined significantly as age of the data set increased. There was a 13.5% yearly decrease in metadata associated with published papers or online repositories and up to a 22% yearly decrease in metadata that were only available from authors. This rapid decay in metadata availability, mirrored in studies of other types of biological data, should motivate swift updates to data-sharing policies and researcher practices to ensure that the valuable context provided by metadata is not lost to conservation science forever.     

Assessing Risk to Birds from Industrial Wind Energy Development via Paired Resource Selection Models

When wildlife habitat overlaps with industrial development animals may be harmed. Because wildlife and people select resources to maximize biological fitness and economic return, respectively, we estimated risk, the probability of eagles encountering and being affected by turbines, by overlaying models of resource selection for each entity. This conceptual framework can be applied across multiple spatial scales to understand and mitigate impacts of industry on wildlife. We estimated risk to Golden Eagles (Aquila chrysaetos) from wind energy development in 3 topographically distinct regions of the central Appalachian Mountains of Pennsylvania (United States) based on models of resource selection of wind facilities (n = 43) and of northbound migrating eagles (n = 30). Risk to eagles from wind energy was greatest in the Ridge and Valley region; all 24 eagles that passed through that region used the highest risk landscapes at least once during low altitude flight. In contrast, only half of the birds that entered the Allegheny Plateau region used highest risk landscapes and none did in the Allegheny Mountains. Likewise, in the Allegheny Mountains, the majority of wind turbines (56%) were situated in poor eagle habitat; thus, risk to eagles is lower there than in the Ridge and Valley, where only 1% of turbines are in poor eagle habitat. Risk within individual facilities was extremely variable; on average, facilities had 11% (SD 23; range = 0–100%) of turbines in highest risk landscapes and 26% (SD 30; range = 0–85%) of turbines in the lowest risk landscapes. Our results provide a mechanism for relocating high‐risk turbines, and they show the feasibility of this novel and highly adaptable framework for managing risk of harm to wildlife from industrial development. Evaluación del Riesgo para las Aves por el Desarrollo de Energía Eólica Industrial Mediante Modelos de Selección de Recursos Pareados.     

Michael Soulé (1936–2020) on spirituality,ethics, and conservation biology

Michael Soulé is best known for his scientific contributions and central role in founding the Society for Conservation Biology and its flagship journal. Less well known are his childhood experiences, his affinity for Zen Buddhism and Arne Naess’ deep ecology philosophy, and his contributions as an environmental activist to efforts to protect biodiversity and rewild ecosystems. Also less well known is the extent to which he was an interdisciplinary environmental studies scholar, struggling to understand what promotes and hinders proenvironmental behaviors. In this regard, his life and that of many other conservation scientists provide important clues, but no easy answers. By attempting to integrate the humanities, with its quest for a meaningful and fulfilling human existence, with naturalistic nature spirituality and ecocentric values, as well as the social and natural sciences, Soulé sought to solve the riddle as to why human beings seemed unable to understand, slow, and halt negative anthropogenic environmental change. He thus modeled what interdisciplinary environmental studies is at its best. Those advocating the conservation of biological diversity have much to learn from Michael Soulé, not only from his scientific findings but also from his way of seeing, the questions he asked, and his love of the living world.     

A multispecies test of source–sink indicators to prioritize habitat for declining populations

For species at risk of decline or extinction in source–sink systems, sources are an obvious target for habitat protection actions. However, the way in which source habitats are identified and prioritized can reduce the effectiveness of conservation actions. Although sources and sinks are conceptually defined using both demographic and movement criteria, simplifications are often required in systems with limited data. To assess the conservation outcomes of alternative source metrics and resulting prioritizations, we simulated population dynamics and extinction risk for 3 endangered species. Using empirically based habitat population models, we linked habitat maps with measured site‐ or habitat‐specific demographic conditions, movement abilities, and behaviors. We calculated source–sink metrics over a range of periods of data collection and prioritized consistently high‐output sources for conservation. We then tested whether prioritized patches identified the habitats that most affected persistence by removing them and measuring the population response. Conservation decisions based on different source–sink metrics and durations of data collection affected species persistence. Shorter time series obscured the ability of metrics to identify influential habitats, particularly in temporally variable and slowly declining populations. Data‐rich source–sink metrics that included both demography and movement information did not always identify the habitats with the greatest influence on extinction risk. In some declining populations, patch abundance better predicted influential habitats for short‐term regional persistence. Because source–sink metrics (i.e., births minus deaths; births and immigrations minus deaths and emigration) describe net population conditions and cancel out gross population counts, they may not adequately identify influential habitats in declining populations. For many nonequilibrium populations, new metrics that maintain the counts of individual births, deaths, and movement may provide additional insight into habitats that most influence persistence.     

Modeling the impacts of hunting on the population dynamics of red howler monkeys (Alouatta seniculus)

Overexploitation of wildlife populations occurs across the humid tropics and is a significant threat to the long-term survival of large-bodied primates. To investigate the impacts of hunting on primates and ways to mitigate them, we developed a spatially explicit, individual-based model for a landscape that included hunted and un-hunted areas. We used the large-bodied neotropical red howler monkey (Alouatta seniculus) as our case study species because its life history characteristics make it vulnerable to hunting. We modeled the influence of different rates of harvest and proportions of landscape dedicated to un-hunted reserves on population persistence, population size, social dynamics, and hunting yields of red howler monkeys. In most scenarios, the un-hunted populations maintained a constant density regardless of hunting pressure elsewhere, and allowed the overall population to persist. Therefore, the overall population was quite resilient to extinction; only in scenarios without any un-hunted areas did the population go extinct. However, the total and hunted populations did experience large declines over 100 years under moderate and high hunting pressure. In addition, when reserve area decreased, population losses and losses per unit area increased disproportionately. Furthermore, hunting disrupted the social structure of troops. The number of male turnovers and infanticides increased in hunted populations, while birth rates decreased and exacerbated population losses due to hunting. Finally, our results indicated that when more than 55% of the landscape was harvested at high (30%) rates, hunting yields, as measured by kilograms of biomass, were less than those obtained from moderate harvest rates. Additionally, hunting yields, expressed as the number of individuals hunted/year/km², increased in proximity to un-hunted areas, and suggested that dispersal from un-hunted areas may have contributed to hunting sustainability. These results indicate that un-hunted areas serve to enhance hunting yields, population size, and population persistence in hunted landscapes. Therefore, spatial regulation of hunting via a reserve system may be an effective management strategy for sustainable hunting, and we recommend it because it may also be more feasible to implement than harvest quotas or restrictions on season length.     

Ranking individual habitat patches as connectivity providers: Integrating network analysis and patch removal experiments

Here we propose an integrated framework for modeling connectivity that can help ecologists, conservation planners and managers to identify patches that, more than others, contribute to uphold species dispersal and other ecological flows in a landscape context. We elaborate, extend and partly integrate recent network-based approaches for modeling and supporting the management of fragmented landscapes. In doing so, experimental patch removal techniques and network analytical approaches are merged into one integrated modeling framework for assessing the role of individual patches as connectivity providers. In particular, we focus the analyses on the habitat availability metrics PC and IIC and on the network metric Betweenness Centrality. The combination and extension of these metrics jointly assess both the immediate connectivity impacts of the loss of a particular patch and the resulting increased vulnerability of the network to subsequent disruptions. In using the framework to analyze the connectivity of two real landscapes in Madagascar and Catalonia (NE Spain), we suggest a procedure that can be used to rank individual habitat patches and show that the combined metrics reveal relevant and non-redundant information valuable to assert and quantify distinctive connectivity aspects of any given patch in the landscape. Hence, we argue that the proposed framework could facilitate more ecologically informed decision-making in managing fragmented landscapes. Finally, we discuss and highlight some of the advantages, limitations and key differences between the considered metrics.     

A parsimonious optimal foraging model explaining mortality patterns in Serengeti wildebeest

Based on data collected over 24 years in the Serengeti in Tanzania, Sinclair and Arcese (1995) indicated that the sensitivity of blue wildebeest Connochaetes taurinus to predation risk by lions Panthera leo may cause them to change habitats between open (low risk) and wooded (risky) habitats. They found that, in poor rainfall years, predators kill wildebeest that are in better condition than those that die of natural causes. In good rainfall years, predators kill wildebeest that are in worse condition than those that die of natural causes. Sinclair and Arcese (1995) proposed the “predation-sensitive food” hypothesis. This hypothesis suggests that, as food becomes limiting, animals take greater risks to obtain more food, and some of these animals are killed. I propose a more parsimonious hypothesis based on the marginal value theorem that is consistent with the observations made by Sinclair and Arcese (1995). Wildebeest follow a single decision rule in good and poor rainfall years, viz. move when foraging elsewhere increases your rate of intake of nutritious food. Similarly, predators follow a single decision rule in good and poor rainfall years, viz. take the prey item that maximizes the intake of energy per unit effort expended. This parsimonious model does not require differences in predator sensitivity as required by Sinclair and Arcese's (1995) model. I indicate ways in which my model can be falsified.     

A new approach to the analysis of adjacencies: Potentials for landscape insights

Spatial adjacencies are a key-issue in environmental studies. Adjacency effects have been amply observed for biotic (plants and animals) and abiotic components of ecosystems. Particularly well-documented are the effects from human manufactures onto the contiguous vegetation mosaics of natural and semi-natural areas.In this work we first propose and reformulate association rules analysis (ARA), a relatively new data mining algorithm with very limited scientific applications so far, in the form of an in-depth investigation method of the spatial pattern of landcover and vegetation maps. We applied ARA to two very different study areas in Northern Italy, the first (Ceno valley) having a substantial human footprint and mapped at 1:25,000 scale, the second (Foses valley) being almost natural and mapped at 1:5000 scale.We were able to: (a) detect the entire network of spatial adjacencies among landcover types and (b) quantify the frequency and strength of detected adjacencies. Based on our spatial analysis, we also advanced hypotheses on both natural and man-driven vegetation dynamics. In addition, ARA allowed us to propose an index of naturality based on the discovered contiguities.Results show the skill of the proposed approach to characterize landcover spatial patterns for both mid-resolution and high-resolution maps. Furthermore the proposed approach bears a general interest, since it can be applied to the analysis of any landcover map.