Programita, a software for spatial point pattern analysis in Ecology
Programita is a comprehensive software package for conducting spatial point pattern analysis in ecology. I tailored Programita to accommodate the needs of “real world” applications in ecology and developed the different modules in response to my own research questions and to requests of colleagues and students who have approached me with their specific research problems in mind. Originally, I developed Programita for my graduate courses “Patrones espaciales en ecología: modelos y análisis”, at the Escuela para Graduados, Facultad de Agronomia, University Buenos Aires, Argentina.
A detailed description of the methods implemented in Programita can be found in my Handbook of Spatial Point Pattern Analysis in Ecology .
The updated Programita software has been downloaded since its launch in January 2014 (up to July 2021) by more than 1600 scientists from all over the world. Google scholar lists more than 600 articles that use the Programita software.
Recent publications using Programita include:
From seed dispersal service to reproductive collapse: density-dependent outcome of a palm–mammal interaction
Oikos 2023 (10), e10002 10.1111/oik.10002
Sex-driven neighborhood effects on herbivory in the dioecious Mediterranean palm Chamaerops humilis L.
Oecologia 203 (1-2), 151 - 165 10.1007/s00442-023-05457-z
Spatial facilitation and competition regulate tree species assembly in a tropical dry forest
Front. For. Glob. Change 6 , art. 1028515 10.3389/ffgc.2023.1028515
Spatial phylogenetic and phenotypic patterns reveal ontogenetic shifts in ecological processes of plant community assembly
Oikos 2022 (12), e09260 10.1111/oik.09260
Legacy effects of seed dispersal mechanisms shape the spatial interaction network of plant species in Mediterranean forests
J. Ecol. 109 (10), 3670 - 3684 10.1111/1365-2745.13744
Habitat filtering drives the local distribution of congeneric species in a Brazilian white-sand flooded tropical forest
Ecol. Evol. 11 (4), 1797 - 1813 10.1002/ece3.7169
Competition for light and persistence of rare light-demanding species within tree-fall gaps in a moist tropical forest
Ecology 101 (7), e03034 10.1002/ecy.3034
Spatial patterns of local species richness reveal importance of frugivores for tropical forest diversity
J. Ecol. 106 (3), 925 - 935 10.1111/1365-2745.12886
Spatio-temporal arrangement of Chamaerops humilis inflorescences and occupancy patterns by its nursery pollinator, Derelomus chamaeropsis
Ann. Bot. 121 (3), 471 - 482 10.1093/aob/mcx177
Phylogeny contributes more than site characteristics and traits to the spatial distribution pattern of tropical tree populations
Oikos 127 (9), 1368 - 1379 10.1111/oik.05142
Colonization in Mediterranean old-fields: the role of dispersal and plant–plant interactions
J. Veg. Sci. 28 (3), 627 - 638 10.1111/jvs.12500
Distance-dependent seedling mortality and long-term spacing dynamics in a neotropical forest community
Ecol. Lett. 20 (11), 1469 - 1478 10.1111/ele.12856
Spatially explicit metrics of species diversity, functional diversity, and phylogenetic diversity: Insights into plant community assembly processes
Annu. Rev. Ecol. Evol. Syst. 48 , 329 - 351 10.1146/annurev-ecolsys-110316-022936
Spatial patterns of an endemic Mediterranean palm recolonizing old fields
Ecol. Evol. 6 (23), 8556 - 8568 10.1002/ece3.2504
Spatial patterns of sapling mortality in a moist tropical forest: consistency with total density-dependent effects
Oikos 125 (6), 872 - 882 10.1111/oik.02520
An evaluation of the state of spatial point pattern analysis in ecology
Ecography 39 (11), 1042 - 1055 10.1111/ecog.01579
Stochastic dilution effects weaken deterministic effects of niche-based processes in species rich forests
Ecology 97 (2), 347 - 360 10.1890/14-2357.1
Nonrandom seedling establishment corresponds with distance-dependent decline in mycorrhizal abundance in two terrestrial orchids
New Phytol. 211 (1), 255 - 264 10.1111/nph.13894
Envelope tests for spatial point patterns with and without simulation
Ecosphere 7 (6), e01365 10.1002/ecs2.1365
Requests for Programita and a user manual (with extensive examples):
www.programita.org
Data types included in Programita
Seventeen years after its launch in the 2004 Oikos Mini review (Wiegand and Moloney 2004), Programita has considerably grown and now contains a variety of statistical methods for most point pattern data types that are relevant in ecological applications, including:
- univariate patterns
one type of points, the most commonly analyzed data type. Null models and point process models include homogeneous and heterogeneous Poisson processes, Thomas cluster processes with one or two critical scales of clustering, and simple soft and hard-core processes.
Wiegand et al. (2009) , Wiegand and Moloney (2014: section 4.1) - bivariate patterns
two types of points such as two species of trees. Programita provides several options to test the independence null hypothesis by using the toroidal shift null model, Thomas cluster point processes, or pattern reconstruction. Additionally, you can use bivariate homogeneous and heterogeneous Poisson processes, bivariate Thomas cluster processes with one or two critical scales of clustering, and simple bivariate soft and hard-core processes.
Getzin and Wiegand (2014), Wiegand and Moloney (2014: section 4.2) - qualitatively marked patterns
one type of points that carries a qualitative mark such as surviving vs. dead. Null models include random labeling, trivariate random labeling, random labeling with a covariate, and random labeling for communities.
Jacquemyn et al. (2009), Wiegand and Moloney (2014: section 3.16) - quantitatively marked patterns
in the simplest case (i) one type of point that carries a quantitative marks such as size, but I implemented additional data types relevant for my work including patterns with (ii) one qualitative and one quantitative mark, and (iii) bivariate patterns with one quantitative mark. The random marking null models are adapted to each data structure.
Wiegand and Moloney (2014: section 4.4) - multivariate patterns
several types of points such as trees of different species in a forest community. You can conduct with Programita multivariate analyses at the community level using (previously generated) null communities, or analyses on the species level using for the focal species the toroidal shift null model, homogeneous and heterogeneous Poisson processes, or previously generated null model data.
Wiegand et al. (2007), Wiegand and Moloney (2014: section 4.3) - multivariate patterns and pairwise dissimilarities
uses additionally a matrix of pairwise dissimilarities such as functional or functional dissimilarities, but also pairwise differences in a single trait. It allows e.g., to analyze phylogenetic or functional beta diversity. Additionally to the null models for multivariate patterns you can randomize the dissimilarity matrix in different ways following Hardy (2009).
Wang et al. (2015, 2016), Wiegand and Moloney (2014: sections 3.1.5.3 and 3.1.7.6), Wiegand et al. (2017) - objects with finite size and real shape
designed for cases where the point approximation does not hold.
Wiegand et al. (2006), Wiegand and Moloney (2014: section 3.1.8)
Additional features of Programita
Programita allows you to
- conduct Monte Carlos simulations of null models or point process models that proved to be important for real world ecological applications
- fit cluster point processes to the data and generate stochastic realizations of the fitted point processes
- determine (global) simulation envelopes of the null model and point process models and conduct goodness-of-fit (GoF) tests ( Wiegand et al. 2016)
- use irregularly shaped observation windows
- combine the results from several replicate plots into a mean, weighted summary statistic
Programita offers for each of these data types the most appropriate summary statistics:
- uni- and bivariate patterns
pair correlation function, L-function, the K2 function, the distribution functions of the distances to the kth nearest neighbor, the spherical contact distribution (only univariate), the mean distance to the kth neighbor, inhomogeneous g- and L-functions (only univariate implemented yet) - qualitatively marked patterns
mark connection functions and various test functions based on pair correlation or K-functions ( Jacquemyn et al. 2009) - quantitatively marked patterns
various normalized and non-normalized mark correlation functions, including the mark and the r-mark correlation functions, the mark variogram, Morans I and Schlathers correlation functions, and the density correlation function ( Fedriani et al. 2015) - multivariate patterns
spatially explicit Simpson index, individual species-area relationship - multivariate patterns and pairwise dissimilarities
phylogenetic Simpson index, phylogenetic mark correlation function, rISAR function ( Shen et al. 2013, Wang et al. 2015, 2016, Wiegand et al. 2017) - objects with finite size and real shape
uni- and bivariate O-ring statistic and L-function ( Wiegand et al. 2006)
see also Software for pattern reconstruction