Changes in Patch Features May Exacerbate or Compensate for the Effect of Habitat Loss on Forest Bird Populations

One and a half centuries after Darwin visited Chiloe Island, what he described as “…an island covered by one great forest…” has lost two-thirds of its forested areas. At this biodiversity hotspot, forest surface is becoming increasingly fragmented due to unregulated logging, clearing for pastures and replacement by exotic tree plantations. Decrease in patch size, increased isolation and “edge effects” can influence the persistence of forest species in remnant fragments. We assessed how these variables affect local density for six forest birds, chosen to include the most important seed dispersers (four species) and bird pollinators (two species, one of which acts also as seed disperser), plus the most common insectivore (Aphrastura spinicauda). Based on cue-count point surveys (8 points per fragment), we estimated bird densities for each species in 22 forest fragments of varying size, shape, isolation and internal-habitat structure (e.g. tree size and epiphyte cover). Bird densities varied with fragment connectivity (three species) and shape (three species), but none of the species was significantly affected by patch size. Satellite image analyses revealed that, from 1985 to 2008, forested area decreased by 8.8% and the remaining forest fragments became 16% smaller, 58–73% more isolated and 11–50% more regular. During that period, bird density estimates for the northern part of Chiloé (covering an area of 1214.75 km2) decreased for one species (elaenia), increased for another two (chucao and hummingbird) and did not vary for three (rayadito, thrust and blackbird). For the first three species, changes in patch features respectively exacerbated, balanced and overcame the effects of forest loss on bird population size (landscape-level abundance). Hence, changes in patch features can modulate the effect of habitat fragmentation on forest birds, suggesting that spatial planning (guided by spatially-explicit models) can be an effective tool to facilitate their conservation

Uncertainties in the value and opportunity costs of pollination services

Pollination is an ecosystem service that directly contributes to agricultural production, and can therefore provide a strong incentive to conserve natural habitats that support pollinator populations. However, we have yet to provide consistent and convincing pollination service valuations to effectively slow the conversion of natural habitats. We use coffee in Kodagu, India, to illustrate the uncertainties involved in estimating costs and benefits of pollination services. First, we fully account for the benefits obtained by coffee agroforests that are attributable to pollination from wild bees nesting in forest habitats. Second, we compare these benefits to the opportunity cost of conserving forest habitats and forgoing conversion to coffee production. Throughout, we systematically quantify the uncertainties in our accounting exercise and identify the parameters that contribute most to uncertainty in pollination service valuation. We find the value of pollination services provided by one hectare of forest to be 25% lower than the profits obtained from converting that same surface to coffee production using average values for all parameters. However, our results show this value is not robust to moderate uncertainty in parameter values, particularly that driven by variability in pollinator density. Synthesis and applications. Our findings emphasize the need to develop robust estimates of both value and opportunity costs of pollination services that take into account landscape and management variables. Our analysis contributes to strengthening pollination service arguments used to help stakeholders make informed decisions on land use and conservation practices. © 2019 The Authors. Journal of Applied Ecology © 2019 British Ecological Societ

The global environmental agenda urgently needs a semantic web of knowledge

Progress in key social-ecological challenges of the global environmental agenda (e.g., climate change, biodiversity conservation, Sustainable Development Goals) is hampered by a lack of integration and synthesis of existing scientific evidence. Facing a fast-increasing volume of data, information remains compartmentalized to pre-defined scales and fields, rarely building its way up to collective knowledge. Today's distributed corpus of human intelligence, including the scientific publication system, cannot be exploited with the efficiency needed to meet current evidence synthesis challenges; computer-based intelligence could assist this task. Artificial Intelligence (AI)-based approaches underlain by semantics and machine reasoning offer a constructive way forward, but depend on greater understanding of these technologies by the science and policy communities and coordination of their use. By labelling web-based scientific information to become readable by both humans and computers, machines can search, organize, reuse, combine and synthesize information quickly and in novel ways. Modern open science infrastructure-i.e., public data and model repositories-is a useful starting point, but without shared semantics and common standards for machine actionable data and models, our collective ability to build, grow, and share a collective knowledge base will remain limited. The application of semantic and machine reasoning technologies by a broad community of scientists and decision makers will favour open synthesis to contribute and reuse knowledge and apply it toward decision making

Towards globally customizable ecosystem service models

Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five “Tier 1” ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed

Selective-logging and oil palm: multitaxon impacts, biodiversity indicators, and trade-offs for conservation planning

Strong global demand for tropical timber and agricultural products has driven large-scale logging and subsequent conversion of tropical forests. Given that the majority of tropical landscapes have been or will likely be logged, the protection of biodiversity within tropical forests thus depends on whether species can persist in these economically exploited lands, and if species cannot persist, whether we can protect enough primary forest from logging and conversion. However, our knowledge of the impact of logging and conversion on biodiversity is limited to a few taxa, often sampled in different locations with complex land-use histories, hampering attempts to plan cost-effective conservation strategies and to draw conclusions across taxa. Spanning a land-use gradient of primary forest, once- and twice-logged forests, and oil palm plantations, we used traditional sampling and DNA metabarcoding to compile an extensive data set in Sabah, Malaysian Borneo for nine vertebrate and invertebrate taxa to quantify the biological impacts of logging and oil palm, develop cost-effective methods of protecting biodiversity, and examine whether there is congruence in response among taxa. Logged forests retained high species richness, including, on average, 70% of species found in primary forest. In contrast, conversion to oil palm dramatically reduces species richness, with significantly fewer primary-forest species than found on logged forest transects for seven taxa. Using a systematic conservation planning analysis, we show that efficient protection of primary-forest species is achieved with land portfolios that include a large proportion of logged-forest plots. Protecting logged forests is thus a cost-effective method of protecting an ecologically and taxonomically diverse range of species, particularly when conservation budgets are limited. Six indicator groups (birds, leaf-litter ants, beetles, aerial hymenopterans, flies, and true bugs) proved to be consistently good predictors of the response of the other taxa to logging and oil palm. Our results confidently establish the high conservation value of logged forests and the low value of oil palm. Cross-taxon congruence in responses to disturbance also suggests that the practice of focusing on key indicator taxa yields important information of general biodiversity in studies of logging and oil palm

Plant-pollinator networks in semi-natural grasslands are resistant to the loss of pollinators during blooming of mass-flowering crops

Mass-flowering crops lead to spatial redistributions of pollinators and to transient shortages within nearby semi-natural grasslands, but the impacts on plant–pollinator interactions remain largely unexplored. Here, we characterised which pollinator species are attracted by oilseed rape and how this affected the structure of plant–pollinator networks in nearby grasslands. We surveyed 177 networks from three countries (Germany, Sweden and United Kingdom) in 24 landscapes with high crop cover, and compared them to 24 landscapes with low or no oilseed rape during and after crop blooming. On average 55% of grassland pollinator species were found on the crop, which attracted 8–35% of individuals away from grasslands. However, networks in the grasslands were resistant to these reductions, since mainly abundant and highly mobile species were attracted. Nonetheless, simulations indicated that network structural changes could be triggered if > 50% of individuals were attracted to the crop (a value well-above that found in our study system), which could affect community stability and resilience to further disturbance

Data Descriptor: Global terrestrial Human Footprint maps for 1993 and 2009

Remotely-sensed and bottom-up survey information were compiled on eight variables measuring the direct and indirect human pressures on the environment globally in 1993 and 2009. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. Pressures were then overlaid to create the standardized Human Footprint maps for all non-Antarctic land areas. A validation analysis using scored pressures from 3114 × 1 km2 random sample plots revealed strong agreement with the Human Footprint maps.We anticipate that the Human Footprint maps will find a range of uses as proxies for human disturbance of natural systems. The updated maps should provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application

CropPol: A dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open, and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e., berry mass, number of fruits, and fruit density [kg/ha], among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), North America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001–2005 (21 studies), 2006–2010 (40), 2011–2015 (88), and 2016–2020 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA).Fil: Allen Perkins, Alfonso. Universidad Politécnica de Madrid; España. Consejo Superior de Investigaciones Científicas. Estación Biológica de Doñana; EspañaFil: Magrach, Ainhoa. Universidad Politécnica de Madrid; EspañaFil: Dainese, Matteo. Eurac Research. Institute for Alpine Environment; ItaliaFil: Garibaldi, Lucas Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Río Negro; ArgentinaFil: Kleijn, David. Wageningen University & Research; Países BajosFil: Rader, Romina. University of New England; AustraliaFil: Reilly, James R.. Rutgers University; Estados UnidosFil: Winfree, Rachael. Rutgers University; Estados UnidosFil: Lundin, Ola. Swedish University of Agricultural Sciences; SueciaFil: McGrady, Carley M.. North Carolina State University; Estados UnidosFil: Brittain, Claire. University of California at Davis; Estados UnidosFil: Biddinger, David J.. University of California Davis; Estados UnidosFil: Artz, Derek R.. United States Department of Agriculture. Agriculture Research Service; Estados UnidosFil: Elle, Elizabeth. University Fraser Simon; CanadáFil: Hoffman, George. State University of Oregon; Estados UnidosFil: Ellis, James D.. University of Florida; Estados UnidosFil: Daniels, Jaret. University of Florida; Estados Unidos. University Of Florida. Florida Museum Of History; Estados UnidosFil: Gibbs, Jason. University of Manitoba; CanadáFil: Campbell, Joshua W.. University of Florida; Estados Unidos. Usda Ars Northern Plains Agricultural Research Laboratory; Estados UnidosFil: Brokaw, Julia. University of Minnesota; Estados UnidosFil: Wilson, Julianna K.. Michigan State University; Estados UnidosFil: Mason, Keith. Michigan State University; Estados UnidosFil: Ward, Kimiora L.. University of California at Davis; Estados UnidosFil: Gundersen, Knute B.. Michigan State University; Estados UnidosFil: Bobiwash, Kyle. University of Manitoba; Canadá. University Fraser Simon; CanadáFil: Gut, Larry. Michigan State University; Estados UnidosFil: Rowe, Logan M.. Michigan State University; Estados UnidosFil: Boyle, Natalie K.. United States Department of Agriculture. Agriculture Research Service; Estados UnidosFil: Williams, Neal M.. University of California at Davis; Estados UnidosFil: Chacoff, Natacha Paola. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentin

CropPol: a dynamic, open and global database on crop pollination

Seventy five percent of the world's food crops benefit from insect pollination. Hence, there has been increased interest in how global change drivers impact this critical ecosystem service. Because standardized data on crop pollination are rarely available, we are limited in our capacity to understand the variation in pollination benefits to crop yield, as well as to anticipate changes in this service, develop predictions, and inform management actions. Here, we present CropPol, a dynamic, open and global database on crop pollination. It contains measurements recorded from 202 crop studies, covering 3,394 field observations, 2,552 yield measurements (i.e. berry weight, number of fruits and kg per hectare, among others), and 47,752 insect records from 48 commercial crops distributed around the globe. CropPol comprises 32 of the 87 leading global crops and commodities that are pollinator dependent. Malus domestica is the most represented crop (32 studies), followed by Brassica napus (22 studies), Vaccinium corymbosum (13 studies), and Citrullus lanatus (12 studies). The most abundant pollinator guilds recorded are honey bees (34.22% counts), bumblebees (19.19%), flies other than Syrphidae and Bombyliidae (13.18%), other wild bees (13.13%), beetles (10.97%), Syrphidae (4.87%), and Bombyliidae (0.05%). Locations comprise 34 countries distributed among Europe (76 studies), Northern America (60), Latin America and the Caribbean (29), Asia (20), Oceania (10), and Africa (7). Sampling spans three decades and is concentrated on 2001-05 (21 studies), 2006-10 (40), 2011-15 (88), and 2016-20 (50). This is the most comprehensive open global data set on measurements of crop flower visitors, crop pollinators and pollination to date, and we encourage researchers to add more datasets to this database in the future. This data set is released for non-commercial use only. Credits should be given to this paper (i.e., proper citation), and the products generated with this database should be shared under the same license terms (CC BY-NC-SA). This article is protected by copyright. All rights reserved