Crowdsourcing Meets Ecology: Hemispherewide Spatiotemporal Species Distribution Models

By Fink, Daniel; Damoulas, Theodoros et al. | AI Magazine, Summer 2014 | Go to article overview

Crowdsourcing Meets Ecology: Hemispherewide Spatiotemporal Species Distribution Models


Fink, Daniel, Damoulas, Theodoros, Bruns, Nicholas E., La Sorte, Frank A., Hochachka, Wesley M., Gomes, Carla P., Kelling, Steve, AI Magazine


(ProQuest: ... denotes formulae omitted.)

... there is no single natural scale at which ecological phenomena should be studied - systems generally show characteristic variabil- ity on a range of spatial, temporal and organizational scales.

(Levin 1992)

Broad-scale environmental and ecological systems origi- nate as simultaneous processes operating across a range of spatiotemporal scales. To study and conserve these systems it is crucial to understand the multi-scale structure of underlying processes. For example, consider some of the processes affecting birds during migration. Climatic phe- nomena, like El Niño southern oscillation and the North Atlantic oscillation (Grosbois et al. 2008) can affect migration timing and direction at hemispheric spatial scales for years at a time. Regional migration pathways are affected by mesoscale spatial processes that define boundaries between major ecosystems like prairies and forests (Fortin and Dale 2005). At a local scale, individual foraging decisions may be based on the availability of specific plants or insects within small habitat patches (Bonter et al. 2009).

An important goal for many conservation applica- tions is spatial prioritization, the identification, delineation, and ranking of regions for management actions (Moilanen, Wilson, and Possingham 2009). For applications with large geographic extents, mul- tiscale spatial prioritization is essential for land man- agers to identify land parcels for acquisition (Schus- ter and Arcese 2013) or remediation. For example, with declining populations of long-distance migrat- ing birds, a key question is whether declines are caused by events on breeding grounds, nonbreeding grounds, or during migrations. Answering this ques- tion requires the comparison of regional population estimates across continents. Once important large- scale regions are identified, fine-scale information is needed to identify critical habitat patches and indi- vidual migration stopover sites.

Multiscale information is also vital to a broad range of related sustainability applications. Scientists need to prioritize regions for disease control man- agement (Ostfeld, Glass, and Keesing 2005). Policy makers need to select sites for human development while trying to minimize ecological costs, for exam- ple, when developing wind farms (Drewitt and Langston 2006). In these examples, multiscale infor- mation is valuable because it allows managers to inform policy and make objective decisions at the appropriate spatial and temporal scale (Gomes 2009).

One of the fundamental challenges of studying multiscale processes is the collection of data. Consis- tent sources of fine-resolution data are needed across broad extents. For many types of biodiversity data, the largest collection programs are national in scope. Unfortunately, the variation among national pro- grams hinders ecological study and conservation planning for broadly distributed species. Because of the difficulty and expense of collecting systematic biodiversity data across large extents, many researchers are beginning to use data collected by cit- izen science projects through crowdsourcing tech- niques (Dickinson, Zuckerberg, and Bonter 2010).

Crowdsourcing projects that engage the public to collect data have been very successful at collecting data across large areas. However, these data tend to be irregularly and sparsely distributed. When partici- pants opportunistically choose where to report their observations, the data tend to follow patterns of human activity (Hochachka et al. 2012), for example, figure 1. This structure presents a challenge for the analysis of multiscale processes because variation in data density translates into variation in scale at which valid inferences can be made. Intuitively, as data density increases at a particular location, the information available for estimating processes oper- ating there also increases, allowing study of smaller scale processes. …

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