Modelling settlement futures: Techniques and challenges

Limitations of secondary data collected by external agencies for examining demographic change in sparsely populated areas (SPAs) are well documented in this volume (especially Chapter 7) and elsewhere (for example, Taylor, 2011). Even robust data collections specifically designed to provide settlement level analysis, such as population censuses, present with a diversity of issues. Broadly, these pertain to enumeration issues, conceptual issues, collection issues, changes to collection methods over time, or simply unexplained events at individual settlements (Koch and Carson, 2012; Taylor et al., 2011). Without local knowledge of specific issues under these themes (should they exist), downstream analysis and the dissection of demographic change for settlements is obstructed by a lack of distinction between 'real' demographic shifts and those which simply represent the outcome of one or more of these influences. Alternatively, 'black swan' events (where the event - like a major shift in the sex ratio for a settlement over a short period of time) may be neither predicted nor traceable to known factors. Most often it is a combination of these, and often the precedent cause is relatively unclear, making the task of modelling time series and projecting future settlement level demographics a hefty challenge

monthly summary of frog calling intensity

This file gives the total calling intensity of frogs per month (i.e. the sum of all records per month, on a four point logarithmic scale). Variable names are as explained in the pape

Supplementary File 6 (Table host-fungus associations)

List of host-parasite associations tested for co-phylogenetic analyses

Supplementary Table 1

List of Sclerotiniaceae and Rutstroemiaceae species used for phylogenetic analysis and their corresponding host range. 1 refers to position in the tree shown in Figure 1; 2 refers to the code used in RASP analysis (Sup. Figure 5). NA, not applicable; Rutst., Rutstroemiaceae; Sclero. Sclerotiniaceae

MOESM5 of Post-tagging behaviour and habitat use in shortnose sturgeon measured with high-frequency accelerometer and PSATs

Additional file 5: Figure S5. Behavioural clustering for the five sturgeon (a to e; smallest to largest respectively) illustrating (i) the four elements of the acceleration ethogram based on the behaviour spectra in Fig. 7 where the vertical axis represents the amplitude of acceleration, the horizontal axis represents the cycle length of the acceleration, (ii) the percent of time spent in each cluster, and (iii) the time series of % times assigned to each cluster over deployment time. Colour coding corresponds to behavioural clusters in (i)

Supplementary File 5 (Chronotree Sclerotiniaceae)

Time calibrated phylogenetic tree of the 105 Sclerotiniaceae species used in Figure 2

Supplementary File 2 (ITS multiple sequence alignment)

Curated multiple ITS sequence alignment for 200 Leotiomycete species, including 105 Sclerotiniaceae and 56 Rutstroemiaceae species. This alignment includes 797 informative sites and was used to generate the phylogenetic tree shown in Figure 1 and downstream analyses (fasta format)

Supplementary File 1 (Host families tree)

Phylogenetic tree of host plant families used for co-phylogenetic analyses in this work (newick format)

Supplementary File 3 (ML tree with SH-aLRT support)

Phylogenetic tree of 200 Leotiomycete species including 161 Sclerotiniaceae and Rutstroemiaceae species used in Figure 1, obtained by maximum likelihood approach and featuring SH-aLRT branch support (newick format)

Population trajectories of baseline and management models in Vortex.

<p>Population trajectories of baseline and management models in Vortex.</p