Survivorship dynamics of the flora of Devonian Angarida.

Devonian plants in Siberia present protracted pioneer succession. Research into the survivorship dynamics of early plant communities upon the palaeocontinent Angarida have demonstrated that transgression and volcanogenic nutrient influx were key to the survival of colonizing plants. Taxic proportions show that migrating taxa entered Angarida from the southwest, Kuznetsk and Minusinsk basins, dispersing across the continent in waves through central areas northwards. The patterns of dispersal are consistent throughout the Devonian. Increased nutrient load from the active pulses of the Viluy-Yakutsk Large Igneous Province, biogeomorphic ecosystem engineering and the increased biomass of Angaridan plants are assisted by Late Devonian transgression. These cumulative factors can be linked to the Late Devonian marine extinctions observed in Siberia.

Temporal area approach for distributional data in biogeography.

A structural approach to temporality in distributional data for use in palaeobiogeography is described herein. Pre-established areas in the distributional data matrix are split temporally, allowing a single geographical space to have multiple iterations [e.g. Area A (Lower Devonian), Area A (Middle Devonian)]. The resulting temporal matrix will allow the representation and capture of any differing relationships through time. Designed primarily for Parsimony Analysis of Endemicity (PAE) and biotic similarity analyses, this approach simply structures distributional data within a temporal partition, meaning that numerical methods can be used to assess relationships between areas to find a branching diagram. Created through the application of the temporal matrix to a given analysis, Temporal Area Approach (TAAp) is a structural approach that facilitates exploration of the data rather than being a hypothesis-driven model following analysis. Understanding the behaviour of non-phylogenetic palaeobiogeographical data and reducing the prevalence of temporal artefacts will lead to more robust area classifications.