Provides raw and curated data on the codes, classification
    and conservation status of freshwater fishes in British Columbia.
    Marine fishes will be added in a future release.

A collection of four datasets 
    based around the population dynamics of migratory fish. Datasets 
    contain both basic size information on a per fish basis, as well as
    otolith data that contains a per day record of fish growth history.
    All data in this package was collected by the author, from 
    2015-2016, in the Wellington region of New Zealand.

Quantitatively analyse depth time-series data from pop-up satellite archival tags (PSATs) through the application of continuous wavelet transformation (CWT) combined with Principal Component Analysis (PCA), and k-means clustering.
  Import, crop, and plot depth time-depth records (TDRs). Using CWT to detect important signals within the non-stationary data, we create daily wavelet statistics to summarise vertical movements on different wavelet periods and combine with daily and diel depth statistics.
  Classify depth time-series with unsupervised k-means clustering into 24-hour periods of vertical movement behaviour with distinct patterns of vertical movement. Plot example days from each behaviour cluster, and plot the TDR coloured by cluster.
  Based on principals of combining CWT with k-means first developed by Sakamoto (2009) <doi:10.1371/journal.pone.0005379> and redeveloped by Beale (2026) <doi:10.21203/rs.3.rs-6907076/v1>.

The FisherEM algorithm, proposed by Bouveyron & Brunet (2012) <doi:10.1007/s11222-011-9249-9>,
        is an efficient method for the clustering of high-dimensional data. FisherEM models and 
        clusters the data in a discriminative and low-dimensional latent subspace. It also provides
        a low-dimensional representation of the clustered data. A sparse version of Fisher-EM
        algorithm is also provided.

Base maps are transformed to focus on a specific location using an
    azimuthal logarithmic distance transformation.