The effect of sampling effort on spatial autocorrelation in macrobenthic intertidal invertebrates

Hamylton, Sarah; Barnes, R. S. K.

doi:10.1007/s10750-017-3491-x

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…The scale at which spatial autocorrelation is no longer present or changes from being clustered to over‐dispersed can indicate a new process acting on biological variables (Zhang and Zhang 2011). Spatial autocorrelation has been used to quantify forest fragmentation (Zhang et al 2009), optimise sampling protocols for marine macrobenthic invertebrate communities (Hamylton and Barnes 2018) and to relate spatial patterns of insect abundance to environmental gradients (Cocu et al 2005). On coral reefs, indices of spatial autocorrelation have been used to quantify the spatial patterning of coral bleaching across scales of cm to 100s of m (Levy et al 2018) and benthic communities up to kilometre‐scales around the circumference of a single tropical island (Aston et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Spatial scaling properties of coral reef benthic communities

et al. 2020

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The spatial structure of ecological communities on tropical coral reefs across seascapes and geographies have historically been poorly understood. Here we addressed this for the first time using spatially expansive and thematically resolved benthic community data collected around five uninhabited central Pacific oceanic islands, spanning 6° latitude and 17° longitude. Using towed‐diver digital image surveys over ~140 linear km of shallow (8–20 m depth) tropical reef, we highlight the autocorrelated nature of coral reef seascapes. Benthic functional groups and hard coral morphologies displayed significant spatial clustering (positive autocorrelation) up to kilometre‐scales around all islands, in some instances dominating entire sections of coastline. The scale and strength of these autocorrelation patterns showed differences across geographies, but patterns were more similar between islands in closer proximity and of a similar size. For example, crustose coralline algae (CCA) were clustered up to scales of 0.3 km at neighbouring Howland and Baker Islands and macroalgae were spatially clustered at scales up to ~3 km at both neighbouring Kingman Reef and Palmyra Atoll. Of all the functional groups, macroalgae had the highest levels of spatial clustering across geographies at the finest resolution of our data (100 m). There were several cases where the upper scale at which benthic community members showed evidence of spatial clustering correlated highly with the upper scales at which concurrent gradients in physical environmental drivers were spatially clustered. These correlations were stronger for surface wave energy than subsurface temperature (regardless of benthic group) and turf algae and CCA had the closest alignments in scale with wave energy across functional groups and geographies. Our findings suggest such physical drivers not only limit or promote the abundance of various benthic competitors on coral reefs, but also play a key role in governing their spatial scaling properties across seascapes.

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Section: Introductionmentioning

confidence: 99%

Spatial scaling properties of coral reef benthic communities

et al. 2020

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“…A global statistics looks at the entirety of a study area and provides a single output, such as if the data displays clustering but doesn't show clusters existence. Local statistics look within the data, in this case clustering or dispersion as compared to particular locations' neighbors and provides visualization of where that clustering is occurring [6].…”

Section: Methodology Of the Studymentioning

confidence: 99%

Determinant Factors and Spatial Distribution of Visceral Leishmaniasis in North Gondar in Selected Hospitals

Melesse

Abere

2019

Journal of Diseases

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Introduction: Visceral Leishmaniasis (VL) is a neglected tropical disease caused by Leishmaniasis protozoa, and transmitted by a sand fly vector. Leishmaniasis is distributed in worldwide and affects millions of peoples. Objective: The objective was to assess the prevalence and spatial distribution of VL in North Gondar. Methodology: 369 patients were selected from records or medical charts retrospectively using stratified and systematic sampling techniques. The study used spatial autocorrelation measures and auto logistic regression model. Result: From 369 patients, 313 (84.8%) were infected by VL. The proportion of VL incidence for those who traveled to endemic areas was (70.2%). Male patients accounted a larger proportion (93.2%) compared to female patients (6.8%). Moran's scatter plot test revealed there is regional clustering on the VL incidence. Local Moron's Index value of Gondar Zuria, Dembeya, Quara, Metemma, Tach Armachiho, Gondar Town, West Belesa and Adarakay was positive, this showed places that shared boundaries have similar VL incidence (i.e highhigh or low-low). From result of local Getis and Ord statistic, positive standardized Zvalues of Adarkay, Gondar Town, Metema, Quara, West Belesa and Dembiya showed the places were hot spot. Similarly negative standardized Z values of Gondar Zuria and Tach Armachiho revealed that places were cold spot. The spatial auto logistic regression result revealed that sex, travel history, BMI, rainfall and elevation were the significant factors of VL at 95% confidence level. Conclusion: The prevalence of VL is still very high (84.8%) in North Gondar Zone. VL clustered on endemic districts that shared common boundaries. Contribution/Originality: This study contributes in the existing literature, spatial distribution and determinant factors of visceral leishmaniasis in North Gondar. Spatial auto logistic regression model is the new approach which helps to determine the prevalence and correlates of visceral leishmaniasis on the study area. VL is clustered on shared boundaries.

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