Matrix Dimensions Bias Demographic Inferences: Implications for Comparative Plant Demography

Abstract

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. abstract: While the wealth of projection matrices in plant demography permits comparative studies, variation in matrix dimensions complicates interspecific comparisons. Collapsing matrices to a common dimension may facilitate such comparisons but may also bias the inferred demographic parameters. Here we examine how matrix dimension affects inferred demographic elasticities and how different collapsing criteria perform. We analyzed matrices 13 # 13 representing nine plant species, collapsing these matrices (i) into even , , , and matrices and (ii) into matrices 7 # 7 5 # 5 4 # 4 3# 3 5 # 5 using different criteria. Stasis and fecundity elasticities increased when matrix dimension was reduced, whereas those of progression and retrogression decreased. We suggest a collapsing criterion that minimizes dissimilarities between the original-and collapsed-matrix elasticities and apply it to 66 plant species to study how life span and growth form influence the relationship between matrix dimension and elasticities. Our analysis demonstrates that (i) projection matrix dimension has significant effects on inferred demographic parameters, (ii) there are better-performing methods than previously suggested for standardizing matrix dimension, and (iii) herbaceous perennial projection matrices are particularly sensitive to changes in matrix dimensionality. For comparative demographic studies, we recommend normalizing matrices to a common dimension by collapsing higher classes and leaving the first few classes unaltered.

Native insect herbivory limits population growth rate of a non-native thistle

“…1e, f); and (3) plant survival (12% less; ESM 8). These results are based on a simple, two-stage matrix model; population growth rate is not influenced by the number of stages included in a matrix model (Tenhumberg et al 2009;Salguero-Gómez and Plotkin 2010). These findings are consistent with experimental studies showing that insect herbivory often severely reduce the fecundity of thistles (Louda and Potvin 1995;Rose et al 2005Rose et al , 2011Suwa et al 2010).…”

Fast–slow continuum and reproductive strategies structure plant life-history variation worldwide

“…We used the Kaiser criterion (23) after optimization through varimax rotations to determine the number of axes necessary to explain a substantial amount of variation. To explore the role and possible interactions of growth form, matrix dimension (68), and habitat, we used a three-way ANOVA followed by post hoc Tukey's honestly significant difference tests on the phylogenetically informed PCA scores of the species. The major habitat classification (28) informs on the abiotic conditions to which populations are exposed, and the growth form information describes potential anatomical constraints.…”

Functional traits explain variation in plant life history strategies

“…If more than one matrix model was available for a given species, we chose the model with the greatest spatial and temporal replication. In the event that two models for the same species had equal spatial and temporal replication, we chose the one with the highest matrix dimension, because higher matrix dimension models offer a higher-resolution description of population dynamics (46). We ignored published studies where the matrix model did not include measures of fecundity, because the calculation of elasticities requires information on the whole lifecycle (27).…”

“…We did not include matrix dimension as a covariate, because although it is known to influence the summed elasticities of matrix elements (46,50), it has a minimal effect on the summed elasticities of vital rates (51), giving us confidence that our estimates are robust. In addition, differences among species in matrix dimension often reflect real differences in life history.…”