Integrating biodiversity distribution knowledge: toward a global map of life.

By Walter Jetz, J. McPherson, Robert P. Guralnick. Trends in Ecology & Evolution, 2012

559 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/7d2c5abec3ac6ed984158c169a615fb5d291221e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Nature and Landscape Conservation

Citations in the corpus

Mapping ignorance: 300 years of collecting flowering plants in Africa (2016, background): “Much of this knowledge is now accessible through Biodiversity Information Systems (BIS), providing new opportunities for conservation planning (Saarenmaa & Nielsen, 2002; Jetz et al., 2012; Dubois et al., 2015).”
Monitoring biodiversity change through effective global coordination (2017, methodology): “The development of the species distribution EBV has benefitted from data infrastructures such as the Global Biodiversity Information Facility (GBIF), the Ocean Biogeographic Information System (OBIS), and Map of Life [37].”
Online solutions and the ‘Wallacean shortfall’: what does GBIF contribute to our knowledge of species' ranges? (2013, background): “(1) Range information based on ‘expert opinion’ is typically at (implicit) resolutions between 100 and 200 km (Jetz et al., 2012), and (2) many studies publish or analyse such data at scales between 50 km 9 50 km and 200 km 9 200 km (e.g. Danner et al., 1998; Settele et al., 2008; Jetz & Fine,…”
Data Integration for Large-Scale Models of Species Distributions. (2020, background): “The potential that can be unleashed by solving these issues is huge; the biodiversity crisis is global [78], but so is the collection of biodiversity data, through the work of many local actors [17,18].”
Predicting species distributions for conservation decisions (2013, background): “(d) Identification of habitat use by the Bighorn sheep (Ovis canadensis sierra, picture by Lynette Schimming) in the Sierra Nevada, California, based on historical records only (NPS Seki 2011); SDM were not used to plan current translocation efforts but to predict the future distribution of potentia”
The eBird enterprise: An integrated approach to development and application of citizen science (2014, background): “eBird data consisting of location, date, and species are provided in the EOD to ensure that they can be integrated with other observational data and natural history collections data for estimating patterns in biodiversity (Jetz et al., 2012).”
A standard protocol for reporting species distribution models (2020, methodology): “This boom in SDM studies is likely related to the increasing availability of digital data (Jetz et al. 2012, Franklin et al. 2017, Wüest et al. 2020) and easy-to-use software packages (Phillips et al. 2006, Thuiller et al. 2009, Brown 2014, Naimi and Araújo 2016, Golding et al. 2018, Kass et al.…”
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions (2014, background): “GBIF is not the only initiative of its kind, but it is by far the largest and is therefore seen as major step in closing the aforementioned data gap (Jetz et al., 2012; Beck et al., 2012).”