Takuya Iwamura

Harnessing carbon payments to protect biodiversity.

A model shows that REDD (reducing emissions from deforestation and degradation) can be extended to biodiversity conservation. Initiatives to reduce carbon emissions from deforestation and degradation (REDD) are providing increasing incentives for forest protection. The collateral benefits for biodiversity depend on the extent to which emissions reductions and biodiversity conservation can be achieved in the same places. Globally, we demonstrate spatial trade-offs in allocating funds to protect forests for carbon and biodiversity and show that cost-effective spending for REDD would protect relatively few species of forest vertebrates. Because trade-offs are nonlinear, we discover that minor adjustments to the allocation of funds could double the biodiversity protected by REDD, while reducing carbon outcomes by only 4 to 8%.

Targeting global protected area expansion for imperiled biodiversity.

Meeting international targets for expanding protected areas could simultaneously contribute to species conservation, but only if the distribution of threatened species informs the future establishment of protected areas.

Cost-effective priorities for global mammal conservation

Global biodiversity priority setting underpins the strategic allocation of conservation funds. In identifying the first comprehensive set of global priority areas for mammals, Ceballos et al. [Ceballos G, Ehrlich PR, Soberón J, Salazar I, Fay JP (2005) Science 309:603–607] found much potential for conflict between conservation and agricultural human activity. This is not surprising because, like other global priority-setting approaches, they set priorities without socioeconomic objectives. Here we present a priority-setting framework that seeks to minimize the conflicts and opportunity costs of meeting conservation goals. We use it to derive a new set of priority areas for investment in mammal conservation based on (i) agricultural opportunity cost and biodiversity importance, (ii) current levels of international funding, and (iii) degree of threat. Our approach achieves the same biodiversity outcomes as Ceballos et al.s while reducing the opportunity costs and conflicts with agricultural human activity by up to 50%. We uncover shortfalls in the allocation of conservation funds in many threatened priority areas, highlighting a global conservation challenge.

Migratory connectivity magnifies the consequences of habitat loss from sea-level rise for shorebird populations.

Sea-level rise (SLR) will greatly alter littoral ecosystems, causing habitat change and loss for coastal species. Habitat loss is widely used as a measurement of the risk of extinction, but because many coastal species are migratory, the impact of habitat loss will depend not only on its extent, but also on where it occurs. Here, we develop a novel graph-theoretic approach to measure the vulnerability of a migratory network to the impact of habitat loss from SLR based on population flow through the network. We show that reductions in population flow far exceed the proportion of habitat lost for 10 long-distance migrant shorebirds using the East Asian–Australasian Flyway. We estimate that SLR will inundate 23–40% of intertidal habitat area along their migration routes, but cause a reduction in population flow of up to 72 per cent across the taxa. This magnifying effect was particularly strong for taxa whose migration routes contain bottlenecks—sites through which a large fraction of the population travels. We develop the bottleneck index, a new network metric that positively correlates with the predicted impacts of habitat loss on overall population flow. Our results indicate that migratory species are at greater risk than previously realized.

A climatic stability approach to prioritizing global conservation investments

Climate change is impacting species and ecosystems globally. Many existing templates to identify the most important areas to conserve terrestrial biodiversity at the global scale neglect the future impacts of climate change. Unstable climatic conditions are predicted to undermine conservation investments in the future. This paper presents an approach to developing a resource allocation algorithm for conservation investment that incorporates the ecological stability of ecoregions under climate change. We discover that allocating funds in this way changes the optimal schedule of global investments both spatially and temporally. This allocation reduces the biodiversity loss of terrestrial endemic species from protected areas due to climate change by 22% for the period of 2002–2052, when compared to allocations that do not consider climate change. To maximize the resilience of global biodiversity to climate change we recommend that funding be increased in ecoregions located in the tropics and/or mid-elevation habitats, where climatic conditions are predicted to remain relatively stable. Accounting for the ecological stability of ecoregions provides a realistic approach to incorporating climate change into global conservation planning, with potential to save more species from extinction in the long term.

Optimal Management of a Multispecies Shorebird Flyway under Sea-Level Rise

Every year, millions of migratory shorebirds fly through the East Asian-Australasian Flyway between their arctic breeding grounds and Australasia. This flyway includes numerous coastal wetlands in Asia and the Pacific that are used as stopover sites where birds rest and feed. Loss of a few important stopover sites through sea-level rise (SLR) could cause sudden population declines. We formulated and solved mathematically the problem of how to identify the most important stopover sites to minimize losses of bird populations across flyways by conserving land that facilitates upshore shifts of tidal flats in response to SLR. To guide conservation investment that minimizes losses of migratory bird populations during migration, we developed a spatially explicit flyway model coupled with a maximum flow algorithm. Migratory routes of 10 shorebird taxa were modeled in a graph theoretic framework by representing clusters of important wetlands as nodes and the number of birds flying between 2 nodes as edges. We also evaluated several resource allocation algorithms that required only partial information on flyway connectivity (node strategy, based on the impacts of SLR at nodes; habitat strategy, based on habitat change at sites; population strategy, based on population change at sites; and random investment). The resource allocation algorithms based on flyway information performed on average 15% better than simpler allocations based on patterns of habitat loss or local bird counts. The Yellow Sea region stood out as the most important priority for effective conservation of migratory shorebirds, but investment in this area alone will not ensure the persistence of species across the flyway. The spatial distribution of conservation investments differed enormously according to the severity of SLR and whether information about flyway connectivity was used to guide the prioritizations. With the rapid ongoing loss of coastal wetlands globally, our method provides insight into efficient conservation planning for migratory species.

Agent-based modeling of hunting and subsistence agriculture on indigenous lands: Understanding interactions between social and ecological systems

Indigenous people of the Rupununi region of Amazonian Guyana interact with their natural environment through hunting and subsistence agriculture. To date the sustainability of indigenous livelihoods has been analyzed by modeling either hunting or forest clearing. Here we develop a holistic model framework with agent-based modeling to examine interactions between demographic growth, hunting, subsistence agriculture, land cover change, and animal population in the Rupununi. We use an extensive field dataset from social surveys, animal observation records and hunting kill locations along with satellite images. The model exhibits feedback loops between a growing human population and depletion of local natural resources. Our model can reproduce the population size of two different villages along with landscape patterns without further calibration. Our model can be used for understanding the conditions of sustainability for indigenous communities relying on subsistence agriculture and hunting, and for scenario analyses to examine the implications of external interventions.

Adapting environmental management to uncertain but inevitable change.

Implementation of adaptation actions to protect biodiversity is limited by uncertainty about the future. One reason for this is the fear of making the wrong decisions caused by the myriad future scenarios presented to decision-makers. We propose an adaptive management (AM) method for optimally managing a population under uncertain and changing habitat conditions. Our approach incorporates multiple future scenarios and continually learns the best management strategy from observations, even as conditions change. We demonstrate the performance of our AM approach by applying it to the spatial management of migratory shorebird habitats on the East Asian–Australasian flyway, predicted to be severely impacted by future sea-level rise. By accounting for non-stationary dynamics, our solution protects 25 000 more birds per year than the current best stationary approach. Our approach can be applied to many ecological systems that require efficient adaptation strategies for an uncertain future.

The Cost of Conservation

C. Kremen et al. (“Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools,” Reports, 11 April, p. [222][1]) proposed a systematic plan for acquiring new protected areas in Madagascar, using extensive new species richness data, but their analysis did not

The large-scale drivers of population declines in a long-distance migratory shorebird

Migratory species can travel tens of thousands of kilometers each year, spending different parts of their annual cycle in geographically distinct locations. Understanding the drivers of population change is vital for conserving migratory species, yet the challenge of collecting data over entire geographic ranges has hindered attempts to identify the processes leading to observed population changes. Here, we use remotely sensed environmental data and bird count data to investigate the factors driving variability in abundance in two subspecies of a long-distance migratory shorebird, the bar-tailed godwit Limosa lapponica. We compiled a spatially and temporally explicit dataset of three environmental variables to identify the conditions experienced by each subspecies in each stage of their annual cycle (breeding, non-breeding and staging). We used a Bayesian N-mixture model to analyze 18 years of monthly count data from 21 sites across Australia and New Zealand in relation to the remote sensing data. We found that the abundance of one subspecies L. l. menzbieri in their non-breeding range was related to climate conditions in breeding grounds, and detected sustained population declines between 1995 and 2012 in both subspecies (L. l. menzbieri, –6.7% and L. l. baueri, –2.1% year–1). To investigate the possible causes of the declines, we quantified changes in habitat extent at 22 migratory staging sites in the Yellow Sea, East Asia, over a 25-year period and found –1.7% and –1.2% year–1 loss of habitat at staging sites used by L. l. menzbieri and L. l baueri, respectively. Our results highlight the need to identify environmental and anthropogenic drivers of population change across all stages of migration to allow the formulation of effective conservation strategies across entire migratory ranges.