Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns

Holloway, Paul A.; Miller, Jennifer A.

doi:10.3390/ijgi4020783

Cited by 13 publications

(13 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tools such as geographically weighted regression (GWR; Fotheringham, Brunsdon, & Charlton, 2002) have been used to explore locally varying processes and their spatial scale across boundaries (Cheng & Fotheringham, 2013) and have also been used to investigate locally varying patterns in species richness (Bickford & Laffan, 2006;Foody, 2004;Holloway & Miller, 2015) and species-environment relationships (Kupfer & Farris, 2006;Miller, 2012;Miller, Franklin, & Aspinall, 2007;Osborne et al, 2007). In GWR, the spatial scale of species-environment relationships is represented with bandwidth parameters that determine the degree to which nearby observations are given higher weights than more distant ones (Fotheringham et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Local niche differences predict genotype associations in sister taxa of desert tortoise

Inman

Fotheringham

Franklin

et al. 2019

Diversity and Distributions

View full text Add to dashboard Cite

Aims To investigate spatial congruence between ecological niches and genotype in two allopatric species of desert tortoise that are species of conservation concern. Location Mojave and Sonoran Desert ecoregions; California, Nevada, Arizona, Utah, USA. Methods We compare ecological niches of Gopherus agassizii and Gopherus morafkai using species distribution modelling (SDM) and then calibrate a pooled‐taxa distribution model to explore local differences in species–environment relationships based on the spatial residuals of the pooled‐taxa model. We use multiscale geographically weighted regression (MGWR) applied to those residuals to estimate local species–environment relationships that can vary across the landscape. We identify multivariate clusters in these local species–environment relationships and compare them against models of (a) a geographically based taxonomic designation for two sister species and (b) an environmental ecoregion designation, with respect to their ability to predict a genotype association index for these two species. Results We find non‐identical niches for these species, with differences that span physiographic and vegetation niche dimensions. We find evidence for two distinct clusters of local species–environment relationships that when mapped, predict an index of genotype association for the two sister taxa better than did either the geographically based taxonomic designation or an environmental ecoregion designation. Main conclusions Exploring local species–environment relationships by coupling SDM and MGWR can benefit studies of biogeography and conservation. We find that niche separation in habitat selection conforms to genotypic differences between sister taxa of tortoise in a recent secondary contact zone. This result may inform decision making by agencies with regulatory or land management authority for the two sister taxa addressed here.

show abstract

Section: Introductionmentioning

confidence: 99%

Local niche differences predict genotype associations in sister taxa of desert tortoise

Inman

Fotheringham

Franklin

et al. 2019

Diversity and Distributions

View full text Add to dashboard Cite

show abstract

“…These techniques are typically inductive, as opposed to deductive, in that they are not used to prove or disprove pre-existing hypotheses, but rather are used to identify patterns embedded in data, and thereby support hypothesis generation [15]. Association rule mining techniques have been widely used to obtain the interrelationships among geographical parameters, including the interrelationship between bird species richness and geographical parameters [31], the spatial distribution of aerosol optical depth and its affecting factors [32], the spatial co-location patterns between the fish distribution and marine parameters [33], and the teleconnection among regional or global marine parameters [27,30,34].…”

Section: Introductionmentioning

confidence: 99%

Using Remote Sensing Products to Identify Marine Association Patterns in Factors Relating to ENSO in the Pacific Ocean

Xue

Fan

Qian

et al. 2017

IJGI

View full text Add to dashboard Cite

El Niño-Southern Oscillation (ENSO) and its relationships with marine environmental parameters comprise a very complicated and interrelated system. Traditional spatiotemporal techniques face great challenges in dealing with which, how, and where the marine environmental parameters in different zones help to drive, and respond to, ENSO events. Remote sensing products covering a 15-year period from 1998 to 2012 were used to quantitatively explore these patterns in the Pacific Ocean (PO) by a prevail quantitative association rule mining algorithm, that is, a priori, within a mining framework. The marine environmental parameters considered were monthly anomaly of sea surface chlorophyll-a (CHLA), monthly anomaly of sea surface temperature (SSTA), monthly anomaly of sea level anomaly (SLAA), monthly anomaly of sea surface precipitation (SSPA), and monthly anomaly of sea surface wind speed (WSA). Four significant discoveries are found, namely: (1) Association patterns among marine environmental parameters and ENSO events were found primarily in five sub-regions of the PO: the western PO, the central and eastern tropical PO, the middle of the northern subtropical PO, offshore of the California coast, and the southern PO; (2) In the western and the middle and east of the equatorial PO, the association patterns are more complicated than other regions; (3) The following factors were found to be predicators of and responses to La Niña events: abnormal decrease of SLAA and WSA in the east of the equatorial PO, abnormal decrease of SSPA and WSA in the middle of the equatorial PO, abnormal decrease of SSTA in the eastern and central tropical PO, and abnormal increase of SLAA in the western PO; (4) Only abnormal decrease of CHLA in the middle of the equatorial PO was found to be a predicator of and response to El Niño events. These findings will help to improve our abilities to identify the marine association patterns in factors relating to ENSO events.

show abstract

“…Technically, spatial autocorrelation examines the relationship between similarities and distance [17][18][19]. The phenomenon that near things are more related than distant things is universal.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of the Distributions of KFC and McDonald’s Outlets in China

Rui

Huang

et al. 2016

IJGI

View full text Add to dashboard Cite

Abstract:Mainland China has become one of the most important markets for international fast-food chains over the past decade. To study the regional spread of KFC and McDonald's outlets in Chinese cities, the correlation of their distributions and degree of market expansion were explored and compared to analyze both the local and the global spatial autocorrelations. A geographically weighted Poisson regression model was also used to examine the influence of demographic, economic, and geographic factors on their spatial distributions. The findings of this comparative study reveal the site selection criteria at the city level by studying the differences and similarities in outlet distributions for KFC and McDonald's. The presented results can guide other chains to enhance business location planning and formulate regional development policy.

show abstract

Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns

Cited by 13 publications

References 46 publications

Local niche differences predict genotype associations in sister taxa of desert tortoise

Local niche differences predict genotype associations in sister taxa of desert tortoise

Using Remote Sensing Products to Identify Marine Association Patterns in Factors Relating to ENSO in the Pacific Ocean

A Comparative Analysis of the Distributions of KFC and McDonald’s Outlets in China

Contact Info

Product

Resources

About