Aligning biodiversity conservation and agricultural production in heterogeneous landscapes

Understanding the trade-offs between biodiversity conservation and agricultural production has become a fundamental question in sustainability science. Substantial research has focused on how species’ populations respond to agricultural intensification, with the goal to understand whether conservation policies that spatially separate agriculture and conservation or, alternatively, integrate the two are more beneficial. Spatial heterogeneity in both species abundance and agricultural productivity have been largely left out of this discussion, although these patterns are ubiquitous from local to global scales due to varying land capacity. Here, we address the question of how to align agricultural production and biodiversity conservation in heterogeneous landscapes. Using model simulations of species abundance and agricultural yields, we show that trade-offs between agricultural production and species’ abundance can be reduced by minimizing the cost (in terms of species abundance) of agricultural production. We find that when species’ abundance and agricultural yields vary across landscapes, the optimal strategy to minimize trade-offs is rarely pure land sparing or land sharing. Instead, landscapes that combine elements of both strategies are optimal. Additionally, we show how the reference population of a species is defined has important influences on optimization results. Our findings suggest that in the real world, understanding the impact of heterogeneous land capacity on biodiversity and agricultural production is crucial to designing multi-use landscapes that jointly maximize conservation and agricultural benefits.Fil: Butsic, Van. University of California at Berkeley; Estados Unidos. Berkeley University; Estados UnidosFil: Kuemmerle, Tobias. Universität zu Berlin; AlemaniaFil: Pallud, Leo. ENSTA ParisTech; FranciaFil: Helmstedt, Kate J.. Queensland University of Technology; AustraliaFil: Macchi, Leandro. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; Argentina. Universität zu Berlin; AlemaniaFil: Potts, Matthew D.. University of California at Berkeley; Estados Unido

Cost-efficient fenced reserves for conservation: single large or two small?

Fences that exclude alien invasive species are used to reduce predation pressure on reintroduced threatened wildlife. Planning these continuously managed systems of reserves raises an important extension of the Single Large or Several Small (SLOSS) reserve planning framework: the added complexity of ongoing management. We investigate the long-term cost-efficiency of a single large or two small predator exclusion fences in the arid Australian context of reintroducing bilbies Macrotis lagotis, and we highlight the broader significance of our results with sensitivity analysis. A single fence more frequently results in a much larger net cost than two smaller fences. We find that the cost-efficiency of two fences is robust to strong demographic and environmental uncertainty, which can help managers to mitigate the risk of incurring high costs over the entire life of the project

Scrutinizing the impact of policy instruments on adoption of agricultural conservation practices using Bayesian expert models

Policy instruments—such as regulation, financial incentives, and agricultural extension—are commonly applied by governments to promote sustainable agricultural practices and tackle ecosystem degradation. Despite substantial investment, little data are available to gauge the impact of evolving policy mixes. We constructed a Bayesian network model to explore relationships between pol-icy instruments, contextual factors, and adoption. Applying a series of scenarios, we present examples of how different instruments influence adoption and how their effectiveness is shaped by contextual factors. Scenarios highlight that the effect of policy instruments is often modest, and constrained by diverse practice and population characteristics. These findings allow us to reflect on the role of policy instruments, and the conditions necessary to support practice change. For example, our findings raise questions about the role of financial benefits versus financial capacity, and highlight the potential importance of concepts such as mental bandwidth in shaping both motivation and capacity to adopt

Operationalizing marketable blue carbon

The global carbon sequestration and avoided emissions potentially achieved via blue carbon is high (∼3% of annual global greenhouse gas emissions); however, it is limited by multidisciplinary and interacting uncertainties spanning the social, governance, financial, and technological dimensions. We compiled a transdisciplinary team of experts to elucidate these challenges and identify a way forward. Key actions to enhance blue carbon as a natural climate solution include improving policy and legal arrangements to ensure equitable sharing of benefits; improving stewardship by incorporating indigenous knowledge and values; clarifying property rights; improving financial approaches and accounting tools to incorporate co-benefits; developing technological solutions for measuring blue carbon sequestration at low cost; and resolving knowledge gaps regarding blue carbon cycles. Implementing these actions and operationalizing blue carbon will achieve measurable changes to atmospheric greenhouse gas concentrations, provide multiple co-benefits, and address national obligations associated with international agreements

Data from: Valuable habitat and low deforestation can reduce biodiversity gains from development rights markets

1. Illegal private land deforestation threatens global biodiversity, even in areas with native habitat requirements stipulated by law. Compliance can be improved by allowing landholders to meet legal reserve requirements by buying and selling the rights to have deforested land through a Tradeable Development Rights system (TDR). While this policy mechanism may prevent native habitat area loss, the spatial pattern of reserved areas will shift, creating novel landscape patterns. The resulting altered fragmentation and connectivity of habitat will impact biodiversity. TDR may also allow landholders to earn rent on land they never intended on converting, resulting in additional deforestation elsewhere and net habitat loss. 2. We construct a simulation model to explore the potential implications for biodiversity when development rights can be traded, compared with the landscape resulting from enforced individual compliance with deforestation laws. 3. We find that where future deforestation is very likely, a TDR market can provide better outcomes for both biodiversity and agriculture, resulting in more connected habitat networks with larger fragments and fewer edge effects. However, the TDR market can be harmful if future deforestation is unlikely, or if one habitat type is tightly spatially correlated with high economic returns from agriculture. 4. Policy implications. Allowing landholders to buy and sell the rights to keep more cleared land than legally stipulated will result in transformed multi-use landscapes. Losses of native habitat in some areas will be offset in others. We conclude that trading forest development rights has the potential to improve habitat configurations, but that careful consideration should be given to current species distributions and likely future deforestation scenarios

Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data

Forests are a global environmental priority that need to be monitored frequently and at large scales. Satellite images are a proven useful, free data source for regular global forest monitoring but these images often have missing data in tropical regions due to climate driven persistent cloud cover. Remote sensing and statistical approaches to filling these missing data gaps exist and these can be highly accurate, but any interpolation method results are uncertain and these methods do not provide measures of this uncertainty. We present a new two-step spatial stochastic random forest (SS-RF) method that uses random forest algorithms to construct Beta distributions for interpolating missing data. This method has comparable performance with the traditional remote sensing compositing method, and additionally provides a probability for each interpolated data point. Our results show that the SS-RF method can accurately interpolate missing data and quantify uncertainty and its applicability to the challenge of monitoring forest using free and incomplete satellite imagery data. We propose that there is scope for our SS-RF method to be applied to other big data problems where a measurement of uncertainty is needed in addition to estimates.</p

Interpolating missing land cover data using stochastic spatial random forests for improved change detection

Forest cover requires large scale and frequent monitoring as an indicator of biodiversity and progress towards United Nations and World Bank Sustainable Development Goal 15. Measuring change in forest cover over time is an essential task in order to track and preserve quality habitats for species around the world. Due to the prohibitive expense and impracticality of mass field data collection to monitor forest cover at regular intervals, satellite images are a key data source for monitoring forest cover globally. A challenge of working with satellite images is missing data due to clouds. Existing methods for interpolating the missing data based on past images, such as compositing, are effective for stable land cover but can be inaccurate for dynamic and substantially changing landscapes. Here we present an adaptation of our recent stochastic spatial random forest (SS-RF) method, which combines observed data from a prior image and modelled estimates of the current image to produce interpolated land cover values and associated probabilities of those values. Results show our SS-RF method accurately detected simulated land cover change under both clear felling (0.83 average overall accuracy) and tree thinning (0.85 average overall accuracy). Our method detected forest cover change substantially more accurately than compositing, offering 39% and 12% increases in average overall accuracy for clear felling and tree thinning simulations respectively. However, when natural fluctuation occurs and there is minimal change in land cover, compositing has equivalent or more accurate performance than our method. Overall we find that our SS-RF method produces accurate estimates under a range of simulated forest clearing scenarios and has a more accurate and robust performance than compositing when modelling noticeably changing landscapes

Prioritizing eradication actions on islands: It's not all or nothing

1. Many highly diverse island ecosystems across the globe are threatened by invasive species. Eradications of invasive mammals from islands are being attempted with increasing frequency, with success aided by geographical isolation and increasing knowledge of eradication techniques. There have been many attempts to prioritize islands for invasive species eradication; however, these coarse methods all assume managers are unrealistically limited to a single action on each island: either eradicate all invasive mammals, or do nothing