Dataset Information

A World at Risk: Aggregating Development Trends to Forecast Global Habitat Conversion.

ABSTRACT: A growing and more affluent human population is expected to increase the demand for resources and to accelerate habitat modification, but by how much and where remains unknown. Here we project and aggregate global spatial patterns of expected urban and agricultural expansion, conventional and unconventional oil and gas, coal, solar, wind, biofuels and mining development. Cumulatively, these threats place at risk 20% of the remaining global natural lands (19.68 million km2) and could result in half of the world's biomes becoming >50% converted while doubling and tripling the extent of land converted in South America and Africa, respectively. Regionally, substantial shifts in land conversion could occur in Southern and Western South America, Central and Eastern Africa, and the Central Rocky Mountains of North America. With only 5% of the Earth's at-risk natural lands under strict legal protection, estimating and proactively mitigating multi-sector development risk is critical for curtailing the further substantial loss of nature.

SUBMITTER: Oakleaf JR

PROVIDER: S-EPMC4596827 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A World at Risk: Aggregating Development Trends to Forecast Global Habitat Conversion.

Oakleaf James R JR Kennedy Christina M CM Baruch-Mordo Sharon S West Paul C PC Gerber James S JS Jarvis Larissa L Kiesecker Joseph J

PloS one 20151007 10

A growing and more affluent human population is expected to increase the demand for resources and to accelerate habitat modification, but by how much and where remains unknown. Here we project and aggregate global spatial patterns of expected urban and agricultural expansion, conventional and unconventional oil and gas, coal, solar, wind, biofuels and mining development. Cumulatively, these threats place at risk 20% of the remaining global natural lands (19.68 million km2) and could result in ha ...[more]

PMID: 26445282

Similar Datasets

Project description:As climate change is expected to have a significant impact on species distributions, there is an urgent challenge to provide reliable information to guide conservation biodiversity policies. In addressing this challenge, we propose a remote sensing-based approach to forecast the future habitat quality for European badger, a species not abundant and at risk of local extinction in the arid environments of southeastern Spain, by incorporating environmental variables related with the ecosystem functioning and correlated with climate and land use. Using ensemble prediction methods, we designed global spatial distribution models for the distribution range of badger using presence-only data and climate variables. Then, we constructed regional models for an arid region in the southeast Spain using EVI (Enhanced Vegetation Index) derived variables and weighting the pseudo-absences with the global model projections applied to this region. Finally, we forecast the badger potential spatial distribution in the time period 2071-2099 based on IPCC scenarios incorporating the uncertainty derived from the predicted values of EVI-derived variables. By including remotely sensed descriptors of the temporal dynamics and spatial patterns of ecosystem functioning into spatial distribution models, results suggest that future forecast is less favorable for European badgers than not including them. In addition, change in spatial pattern of habitat suitability may become higher than when forecasts are based just on climate variables. Since the validity of future forecast only based on climate variables is currently questioned, conservation policies supported by such information could have a biased vision and overestimate or underestimate the potential changes in species distribution derived from climate change. The incorporation of ecosystem functional attributes derived from remote sensing in the modeling of future forecast may contribute to the improvement of the detection of ecological responses under climate change scenarios.

Dataset Information

A World at Risk: Aggregating Development Trends to Forecast Global Habitat Conversion.

Publications

A World at Risk: Aggregating Development Trends to Forecast Global Habitat Conversion.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets