Twenty years and counting with SADIE: Spatial Analysis by Distance Indices software and review of its adoption and use

Abstract

SADIE (Spatial Analysis by Distance Indices) is designed specifically to quantify patterns in spatially-referenced count-based data. It was developed for dealing with data that can be considered ‘patchy’. Such distributions are commonly found, for example, in insect populations where discrete patches of individuals are often evident. The distributions of such populations have ‘hard edges’, with patches and gaps occurring spatially. In these cases variance of abundance does not vary smoothly, but discontinuously. In this paper we outline the use of SADIE and provide free access to the SADIE software suite, establishing Rethinking Ecology as its permanent home. Finally, we review the use of SADIE and demonstrate its use in a wide variety of sub-disciplines within the general field of ecology.

Spatial Distribution of Hunting Billbugs (Coleoptera: Curculionidae) in Sod Farms

“…Several geospatial methods, such as spatial analysis by distance indices (SADIE) and variograms, can be used to assess insect spatial distributions [14,20,[23][24][25]. Variograms are commonly used to analyze and model the spatial dependences among individuals in a population [14,25]. Spatial dependence (or spatial autocorrelation) can be used to define the sampling scales for independent samples and to quantify the spatial patterns of insect species [11,26].…”

“…Therefore, these models are not suitable for characterizing within-field population distributions or for developing sampling plans [ 11 , 12 ]. Due to the lack of two-dimensional information for individual sample locations, the information derived from these mean-variance methods lacks many ecological interactions [ 13 , 14 ]. Another benefit of spatial distribution sampling is to develop a visual representation of pest infestations in the field by creating prediction maps and kriging maps in variograms [ 6 , 15 , 16 ] and “red and blue” maps in SADIE [ 17 , 18 ].…”

Small-Scale Abiotic Factors Influencing the Spatial Distribution of Phytophthora cinnamomi under Declining Quercus ilex Trees

“…The spatial patterns of cfu were characterised using Spatial Analysis by Distance Indices (SADIE), implemented in the program "SADIEShell v2.0" and the aggregation index (I a ) and clustering index (ν) were calculated per plot [36,37]. The I a provides information on the overall spatial pattern of each environmental variable.…”

Spatio-Temporal Distribution and Fixed-Precision Sampling Plan of Scirtothrips dorsalis (Thysanoptera: Thripidae) in Florida Blueberry

“…Field counts were also fit to the Poisson distribution and Negative binomial distribution using the fit_two_distr function, and data were considered to belong to a frequency distribution when the observed and expected frequencies were not different (Chi-square test at p > 0.05). The function sadie was used to calculate the index of SADIE (Spatial Analysis of Distance Indices) index, which indicates significant clustering if the index is greater than 1.5 [ 26 , 27 ]. Lloyd’s patchiness index, P, was determined using the function agg index , and the degree of patchiness increases as the index becomes greater than the unity.…”