Mapping the planet’s critical natural assets

Sustaining the organisms, ecosystems and processes that underpin human wellbeing is necessary to achieve sustainable development. Here we define critical natural assets as the natural and semi-natural ecosystems that provide 90% of the total current magnitude of 14 types of nature’s contributions to people (NCP), and we map the global locations of these critical natural assets at 2 km resolution. Critical natural assets for maintaining local-scale NCP (12 of the 14 NCP) account for 30% of total global land area and 24% of national territorial waters, while 44% of land area is required to also maintain two global-scale NCP (carbon storage and moisture recycling). These areas overlap substantially with cultural diversity (areas containing 96% of global languages) and biodiversity (covering area requirements for 73% of birds and 66% of mammals). At least 87% of the world’s population live in the areas benefitting from critical natural assets for local-scale NCP, while only 16% live on the lands containing these assets. Many of the NCP mapped here are left out of international agreements focused on conserving species or mitigating climate change, yet this analysis shows that explicitly prioritizing critical natural assets and the NCP they provide could simultaneously advance development, climate and conservation goals.We thank all the participants of two working groups hosted by Conservation International and the Natural Capital Project for their insights and intellectual contributions. For further advice or assistance, we thank A. Adams, K. Brandon, K. Brauman, A. Cramer, G. Daily, J. Fisher, R. Gould, L. Mandle, J. Montgomery, A. Rodewald, D. Rossiter, E. Selig, A. Vogl and T. M. Wright. The two working groups that provided the foundation for this analysis were funded by support from the Marcus and Marianne Wallenberg Foundation to the Natural Capital Project (R.C.-K. and R.P.S.) and the Betty and Gordon Moore to Conservation International (R.A.N. and P.M.C.)

ADHD symptomatology in eating disorders : a secondary psychopathological measure of severity?

Background: Attention-deficit/hyperactivity disorder (ADHD) has commonly been described in psychiatric disorders. Although several studies have found positive associations between abnormal eating patterns during childhood and ADHD, there is a lack of studies on ADHD and Eating Disorders (ED). The aims of this exploratory study were 1) to assess the ADHD symptoms level in ED and to ascertain whether there are differences among ED subtypes; 2) to analyze whether the presence of ADHD symptoms is associated with more severe eating disorder symptoms and greater general psychopathology; and 3) to assess whether the ADHD symptoms level is associated with specific temperament and character traits. Methods: 191 female ED patients were included. Assessment was carried out with the EDI-2, ASRS-v1.1, the SCL-90-R and the TCI-R. Results: The ADHD symptoms level was similar in bulimia, eating disorder not otherwise specified and binge eating subtypes, and lower in anorexic patients. Obsessiveness and Hostility were significantly positively associated with ADHD symptoms. A path model showed that ADHD was associated with high Novelty Seeking and low Self-Directedness, whereas ED severity was influenced by ADHD severity and low Self-Directedness. Conclusions: Bingeing/purging ED subtypes have a high ADHD symptoms level, also related with more severe eating, general and personality psychopathology

Exact integer linear programming solvers outperform simulated annealing for solving conservation planning problems

The resources available for conserving biodiversity are limited, and so protected areas need to be established in places that will achieve objectives for minimal cost. Two of the main algorithms for solving systematic conservation planning problems are Simulated Annealing (SA) and exact integer linear programing (EILP) solvers. Using a case study in BC, Canada, we compare the cost-effectiveness and processing times of SA used in Marxan versus EILP using both commercial and open-source algorithms. Plans for expanding protected area systems based on EILP algorithms were 12-30% cheaper than plans using SA, due to EILP's ability to find optimal solutions as opposed to approximations. The best EILP solver we examined was on average 1,071 times faster than the SA algorithm tested. The performance advantages of EILP solvers were also observed when we aimed for spatially compact solutions by including a boundary penalty. One practical advantage of using EILP over SA is that the analysis does not require calibration, saving even more time. Given the performance of EILP solvers, they can be used to generate conservation plans in real-time during stakeholder meetings and can facilitate rapid sensitivity analysis, and contribute to a more transparent, inclusive, and defensible decision-making process

Optimality in prioritizing conservation projects

The resources available to safeguard biodiversity are limited, and so funding must be allocated cost-effectively. To achieve this, conservation projects—such as threatened species recovery projects, or pest management projects—are often prioritized using algorithms. Conventionally, prioritizations have been generated by ranking projects according to their cost-effectiveness and selecting the top projects within a budget, or using backwards heuristic algorithms which iteratively remove projects until a budget is met. Yet such algorithms may not deliver optimal solutions. We investigated the performance of exact algorithms, a class of algorithms that guarantee optimality, compared with conventional algorithms for project prioritization. Specifically, we conducted a simulation study, involving 40 conservation projects, 50 species, and 80 management actions, and a case study involving recovery projects for 62 of New Zealand’s threatened bird species. In each of these studies, we generated prioritizations by (i) exact algorithms, (ii) ranking projects using cost-effectiveness, (iii) a backwards heuristic algorithm, and (iv) randomly funding projects. After generating the prioritizations, we evaluated their performance. We found that exact algorithms outperform conventional algorithms for project prioritization. In the simulation study, both the ranking and backwards heuristic algorithms returned solutions that were highly suboptimal when compared with solutions by exact algorithms. In the case study, both conventional algorithms returned solutions that would be expected to result in the needless loss of millions of years of avian evolutionary history due to poor planning. Furthermore, conventional algorithms returned solutions with large amounts of unallocated funding—providing little guidance for decision makers. Despite concerns that exact algorithm solvers require an inordinate amount of time, the longest run in either study took less than three minutes. Our results suggest that conservation agencies could benefit enormously from exact algorithms. To help make exact algorithms more accessible, we developed the oppr R package (https://CRAN.R-project.org/package=oppr) which can use open-source and commercial exact algorithm solvers to identify optimal solutions for a range of objectives and constraints. Our findings suggest that conservation plans could be substantially improved using exact algorithms, which could potentially save millions of dollars and lead to more species being saved from extinction

Tradeoffs in the value of biodiversity feature and cost data in conservation prioritization

Decision-support tools are commonly used to maximize return on investments (ROI) in conservation. We evaluated how the relative value of information on biodiversity features and land cost varied with data structure and variability, attributes of focal species and conservation targets, and habitat suitability thresholds for contrasting bird communities in the Pacific Northwest of North America. Specifically, we used spatial distribution maps for 20 bird species, land values, and an integer linear programming model to prioritize land units (1 km2) that met conservation targets at the lowest estimated cost (hereafter ‘efficiency’). Across scenarios, the relative value of biodiversity data increased with conservation targets, as higher thresholds for suitable habitat were applied, and when focal species occurred disproportionately on land of high assessed value. Incorporating land cost generally improved planning efficiency, but at diminishing rates as spatial variance in biodiversity features relative to land cost increased. Our results offer a precise, empirical demonstration of how spatially-optimized planning solutions are influenced by spatial variation in underlying feature layers. We also provide guidance to planners seeking to maximize efficiency in data acquisition and resolve potential trade-offs when setting targets and thresholds in financially-constrained, spatial planning efforts aimed at maximizing ROI in biodiversity conservation

Comparing abundance distributions and range maps in spatial conservation planning for migratory species

Most spatial conservation planning for wide-ranging or migratory species is constrained by poor knowledge of species' spatiotemporal dynamics and is only based on static species' ranges. However, species have substantial variation in abundance across their range and migratory species have important spatiotemporal population dynamics. With growing ecological data and advancing analytics, both of these can be estimated and incorporated into spatial conservation planning. However, there is limited information on the degree to which including this information affects conservation planning. We compared the performance of systematic conservation prioritizations for different scenarios based on varying the input species' distributions by ecological metric (abundance distributions versus range maps) and temporal sampling resolution (weekly, monthly, or quarterly). We used the example of a community of 41 species of migratory shorebirds that breed in North America, and we used eBird data to produce weekly estimates of species' abundances and ranges. Abundance distributions at a monthly or weekly resolution led to prioritizations that most efficiently protected species throughout the full annual cycle. Conversely, spatial prioritizations based on species' ranges required more sites and left most species insufficiently protected for at least part of their annual cycle. Prioritizations with only quarterly species ranges were very inefficient as they needed to target 40% of species' ranges to include 10% of populations. We highlight the high value of abundance information for spatial conservation planning, which leads to more efficient and effective spatial prioritization for conservation. Overall, we provide evidence that spatial conservation planning for wide-ranging migratory species is most robust and efficient when informed by species' abundance information from the full annual cycle.</p

Unveiling geographical gradients of species richness from scant occurrence data

Aim: Despite longstanding investigation, the gradients of species richness remain unknown for most taxa because of shortfalls in knowledge regarding the quantity and distribution of species. Here, we explore the ability of a geostatistical interpolation model, regression-kriging, to recover geographical gradients of species richness. We examined the technique with an in silico gradient of species richness and evaluated the effect of different configurations of knowledge shortfalls. We also took the same approach for empirical data with large knowledge gaps, the infraorder Furnariides of suboscine birds. Innovation: Regression-kriging builds upon two cornerstones of geographical gradients of biodiversity, the spatial autocorrelation of species richness and the conspicuous association of species with environmental factors. With this technique, we recovered a simulated gradient of richness using < 0.01% of sampling sites across the region. The accuracy of the regression-kriging is higher when input samples are more evenly distributed throughout the geographical space rather than the environmental space of the target region. Moreover, the accuracy of this method is more sensitive to the sufficiency of sampling effort within cells than to the quantity of sampled localities. For Furnariides birds, regression-kriging provided a geographical gradient of species richness that resembles purported patterns of other groups and illustrated ubiquitous shortfalls of knowledge about bird diversity. Main conclusions: Geostatistical interpolation, such as regression-kriging, might be a useful tool to overcome shortfalls in knowledge that plague our understanding of geographical gradients of biodiversity, with many applications in ecology, palaeoecology and conservation. © 2020 John Wiley & Sons Lt