High and Far: Biases in the Location of Protected Areas

BACKGROUND: About an eighth of the earth's land surface is in protected areas (hereafter "PAs"), most created during the 20(th) century. Natural landscapes are critical for species persistence and PAs can play a major role in conservation and in climate policy. Such contributions may be harder than expected to implement if new PAs are constrained to the same kinds of locations that PAs currently occupy. METHODOLOGY/PRINCIPAL FINDINGS: Quantitatively extending the perception that PAs occupy "rock and ice", we show that across 147 nations PA networks are biased towards places that are unlikely to face land conversion pressures even in the absence of protection. We test each country's PA network for bias in elevation, slope, distances to roads and cities, and suitability for agriculture. Further, within each country's set of PAs, we also ask if the level of protection is biased in these ways. We find that the significant majority of national PA networks are biased to higher elevations, steeper slopes and greater distances to roads and cities. Also, within a country, PAs with higher protection status are more biased than are the PAs with lower protection statuses. CONCLUSIONS/SIGNIFICANCE: In sum, PAs are biased towards where they can least prevent land conversion (even if they offer perfect protection). These globally comprehensive results extend findings from nation-level analyses. They imply that siting rules such as the Convention on Biological Diversity's 2010 Target [to protect 10% of all ecoregions] might raise PA impacts if applied at the country level. In light of the potential for global carbon-based payments for avoided deforestation or REDD, these results suggest that attention to threat could improve outcomes from the creation and management of PAs

How many species of flowering plants are there?

We estimate the probable number of flowering plants. First, we apply a model that explicitly incorporates taxonomic effort over time to estimate the number of as-yet-unknown species. Second, we ask taxonomic experts their opinions on how many species are likely to be missing, on a family-by-family basis. The results are broadly comparable. We show that the current number of species should grow by between 10 and 20 per cent. There are, however, interesting discrepancies between expert and model estimates for some families, suggesting that our model does not always completely capture patterns of taxonomic activity. The as-yet-unknown species are probably similar to those taxonomists have described recently—overwhelmingly rare and local, and disproportionately in biodiversity hotspots, where there are high levels of habitat destruction

Sentiment Analysis of Conservation Studies Captures Successes of Species Reintroductions

Learning from the rapidly growing body of scientific articles is constrained by human bandwidth. Existing methods in machine learning have been developed to extract knowledge from human language and may automate this process. Here, we apply sentiment analysis, a type of natural language processing, to facilitate a literature review in reintroduction biology. We analyzed 1,030,558 words from 4,313 scientific abstracts published over four decades using four previously trained lexicon-based models and one recursive neural tensor network model. We find frequently used terms share both a general and a domain-specific value, with either positive (success, protect, growth) or negative (threaten, loss, risk) sentiment. Sentiment trends suggest that reintroduction studies have become less variable and increasingly successful over time and seem to capture known successes and challenges for conservation biology. This approach offers promise for rapidly extracting explicit and latent information from a large corpus of scientific texts

Wildbook: Crowdsourcing, computer vision, and data science for conservation

Photographs, taken by field scientists, tourists, automated cameras, and incidental photographers, are the most abundant source of data on wildlife today. Wildbook is an autonomous computational system that starts from massive collections of images and, by detecting various species of animals and identifying individuals, combined with sophisticated data management, turns them into high resolution information database, enabling scientific inquiry, conservation, and citizen science. We have built Wildbooks for whales (flukebook.org), sharks (whaleshark.org), two species of zebras (Grevy's and plains), and several others. In January 2016, Wildbook enabled the first ever full species (the endangered Grevy's zebra) census using photographs taken by ordinary citizens in Kenya. The resulting numbers are now the official species census used by IUCN Red List: http://www.iucnredlist.org/details/7950/0. In 2016, Wildbook partnered up with WWF to build Wildbook for Sea Turtles, Internet of Turtles (IoT), as well as systems for seals and lynx. Most recently, we have demonstrated that we can now use publicly available social media images to count and track wild animals. In this paper we present and discuss both the impact and challenges that the use of crowdsourced images can have on wildlife conservation.Comment: Presented at the Data For Good Exchange 201

On Population Growth Near Protected Areas

Background: Protected areas are the first, and often only, line of defense in efforts to conserve biodiversity. They might be detrimental or beneficial to rural communities depending on how they alter economic opportunities and access to natural resources. As such, protected areas may attract or repel human settlement. Disproportionate increases in population growth near protected area boundaries may threaten their ability to conserve biodiversity. Methodology/Principal Findings: Using decadal population datasets, we analyze population growth across 45 countries and 304 protected areas. We find no evidence for population growth near protected areas to be greater than growth of rural areas in the same country. Furthermore, we argue that what growth does occur near protected areas likely results from a general expansion of nearby population centers. Conclusions/Significance: Our results contradict those from a recent study by Wittemyer et al., who claim overwhelming evidence for increased human population growth near protected areas. To understand the disagreement, we re-analyzed the protected areas in Wittemyer et al.’s paper. Their results are simply artifacts of mixing two incompatible datasets. Protected areas may experience unusual population pressures near their edges; indeed, individual case studies provid

Biophysical suitability, economic pressure and land-cover change: a global probabilistic approach and insights for REDD+

There has been a concerted effort by the international scientific community to understand the multiple causes and patterns of land-cover change to support sustainable land management. Here, we examined biophysical suitability, and a novel integrated index of “Economic Pressure on Land” (EPL) to explain land cover in the year 2000, and estimated the likelihood of future land-cover change through 2050, including protected area effectiveness. Biophysical suitability and EPL explained almost half of the global pattern of land cover (R 2 = 0.45), increasing to almost two-thirds in areas where a long-term equilibrium is likely to have been reached (e.g. R 2 = 0.64 in Europe). We identify a high likelihood of future land-cover change in vast areas with relatively lower current and past deforestation (e.g. the Congo Basin). Further, we simulated emissions arising from a “business as usual” and two reducing emissions from deforestation and forest degradation (REDD) scenarios by incorporating data on biomass carbon. As our model incorporates all biome types, it highlights a crucial aspect of the ongoing REDD + debate: if restricted to forests, “cross-biome leakage” would severely reduce REDD + effectiveness for climate change mitigation. If forests were protected from deforestation yet without measures to tackle the drivers of land-cover change, REDD + would only reduce 30 % of total emissions from land-cover change. Fifty-five percent of emissions reductions from forests would be compensated by increased emissions in other biomes. These results suggest that, although REDD + remains a very promising mitigation tool, implementation of complementary measures to reduce land demand is necessary to prevent this leakage

Projecting Global Biodiversity Indicators under Future Development Scenarios

To address the ongoing global biodiversity crisis, governments have set strategic objectives and have adopted indicators to monitor progress toward their achievement. Projecting the likely impacts on biodiversity of different policy decisions allows decision makers to understand if and how these targets can be met. We projected trends in two widely used indicators of population abundance Geometric Mean Abundance, equivalent to the Living Planet Index and extinction risk (the Red List Index) under different climate and land-use change scenarios. Testing these on terrestrial carnivore and ungulate species, we found that both indicators decline steadily, and by 2050, under a Business-as-usual (BAU) scenario, geometric mean population abundance declines by 18-35% while extinction risk increases for 8-23% of the species, depending on assumptions about species responses to climate change. BAU will therefore fail Convention on Biological Diversity target 12 of improving the conservation status of known threatened species. An alternative sustainable development scenario reduces both extinction risk and population losses compared with BAU and could lead to population increases. Our approach to model species responses to global changes brings the focus of scenarios directly to the species level, thus taking into account an additional dimension of biodiversity and paving the way for including stronger ecological foundations into future biodiversity scenario assessments.Peer reviewe

Conducting robust ecological analyses with climate data

Although the number of studies discerning the impact of climate change on ecological systems continues to increase, there has been relatively little sharing of the lessons learnt when accumulating this evidence. At a recent workshop entitled ‘Using climate data in ecological research’ held at the UK Met Office, ecologists and climate scientists came together to discuss the robust analysis of climate data in ecology. The discussions identified three common pitfalls encountered by ecologists: 1) selection of inappropriate spatial resolutions for analysis; 2) improper use of publically available data or code; and 3) insufficient representation of the uncertainties behind the adopted approach. Here, we discuss how these pitfalls can be avoided, before suggesting ways that both ecology and climate science can move forward. Our main recommendation is that ecologists and climate scientists collaborate more closely, on grant proposals and scientific publications, and informally through online media and workshops. More sharing of data and code (e.g. via online repositories), lessons and guidance would help to reconcile differing approaches to the robust handling of data. We call on ecologists to think critically about which aspects of the climate are relevant to their study system, and to acknowledge and actively explore uncertainty in all types of climate data. And we call on climate scientists to make simple estimates of uncertainty available to the wider research community. Through steps such as these, we will improve our ability to robustly attribute observed ecological changes to climate or other factors, while providing the sort of influential, comprehensive analyses that efforts to mitigate and adapt to climate change so urgently require

Framework for sustained climate assessment in the United States

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society, 100(5), (2019): 897-908, doi:10.1175/BAMS-D-19-0130.1.As states, cities, tribes, and private interests cope with climate damages and seek to increase preparedness and resilience, they will need to navigate myriad choices and options available to them. Making these choices in ways that identify pathways for climate action that support their development objectives will require constructive public dialogue, community participation, and flexible and ongoing access to science- and experience-based knowledge. In 2016, a Federal Advisory Committee (FAC) was convened to recommend how to conduct a sustained National Climate Assessment (NCA) to increase the relevance and usability of assessments for informing action. The FAC was disbanded in 2017, but members and additional experts reconvened to complete the report that is presented here. A key recommendation is establishing a new nonfederal “climate assessment consortium” to increase the role of state/local/tribal government and civil society in assessments. The expanded process would 1) focus on applied problems faced by practitioners, 2) organize sustained partnerships for collaborative learning across similar projects and case studies to identify effective tested practices, and 3) assess and improve knowledge-based methods for project implementation. Specific recommendations include evaluating climate models and data using user-defined metrics; improving benefit–cost assessment and supporting decision-making under uncertainty; and accelerating application of tools and methods such as citizen science, artificial intelligence, indicators, and geospatial analysis. The recommendations are the result of broad consultation and present an ambitious agenda for federal agencies, state/local/tribal jurisdictions, universities and the research sector, professional associations, nongovernmental and community-based organizations, and private-sector firms.This report would not have been possible without the support and participation of numerous organizations and individuals. We thank New York State Governor Andrew M. Cuomo for announcing in his 2018 State of the State agenda that the IAC would be reconvened. The New York State Energy Research and Development Authority (Contract ID 123416), Columbia University’s Earth Institute, and the American Meteorological Society provided essential financial support and much more, including sage advice and moral support from John O’Leary, Shara Mohtadi, Steve Cohen, Alex Halliday, Peter deMenocal, Keith Seitter, Paul Higgins, and Bill Hooke. We thank the attendees of a workshop, generously funded by the Kresge Foundation in November of 2017, that laid a foundation for the idea to establish a civil-society-based assessment consortium. During the course of preparing the report, IAC members consulted with individuals too numerous to list here—state, local, and tribal officials; researchers; experts in nongovernmental and community-based organizations; and professionals in engineering, architecture, public health, adaptation, and other areas. We are so grateful for their time and expertise. We thank the members and staff of the National Academy of Sciences, Engineering, and Medicine’s Committee to Advise the U.S. Global Change Research Program for providing individual comments on preliminary recommendations during several discussions in open sessions of their meetings. The following individuals provided detailed comments on an earlier version of this report, which greatly sharpened our thinking and recommendations: John Balbus, Tom Dietz, Phil Duffy, Baruch Fischhoff, Brenda Hoppe, Melissa Kenney, Linda Mearns, Claudia Nierenberg, Kathleen Segerson, Soroosh Sorooshian, Chris Weaver, and Brian Zuckerman. Mary Black provided insightful copy editing of several versions of the report. We also thank four anonymous reviewers for their effort and care in critiquing and improving the report. It is the dedication, thoughtful feedback, expertise, care, and commitment of all these people and more that not only made this report possible, but allow us all to continue to support smart and insightful actions in a changing climate. We are grateful as authors and as global citizens. Author contributions: RM, SA, KB, MB, AC, JD, PF, KJ, AJ, KK, JK, ML, JM, RP, TR, LS, JS, JW, and DZ were members of the IAC and shared in researching, discussing, drafting, and approving the report. BA, JF, AG, LJ, SJ, PK, RK, AM, RM, JN, WS, JS, PT, GY, and RZ contributed to specific sections of the report