Ripley’s K-function (mean number of points within radius <i>r</i> from any point) for the observed point pattern (black), theoretical value under complete spatial randomness (blue), and value under an inhomogeneous Poisson process with no inter-point interaction (red).

<p>Confidence intervals generated through 1,000 simulations of point processes. Gentoo nests show over-dispersion (fewer points than expected) at short scales (inset) and under-dispersion (more points than expected) at larger scales.</p

Parameter estimates for the fitted hybrid Gibbs process model of gentoo nest locations at Port Lockroy, Antarctica.

<p>There is zero probability of points existing within 0.28m (h) of each other. From 0.28m to 0.5m (<i>r</i>₁) the probability of occurrence is reduced by , and from 0.5m - 1.86m (<i>r</i>₂) the probability of occurrence is increased by .</p

Raster and contour map derived from 3D model produced by Structure-from-Motion.

<p>Raster and contour map derived from 3D model produced by Structure-from-Motion.</p

Appendix B. Survivorship model comparison.

Survivorship model comparison

Colony abundance vs. guano stain area.

<p>(A) Colony abundance (and 95^th percentile confidence intervals; as reported by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113301#pone.0113301-Southwell1" target="_blank">[7]</a> as a function of the area identified as guano in the Landsat-7 survey (black circles  =  continental Antarctica, orange squares  =  Antarctic Peninsula), with best-fitting Poisson regression model (and associated 95^th percentile prediction envelop; gray-shaded envelop  =  continental Antarctica, orange-shaded envelop  =  Antarctic Peninsula). (B) Zoomed in portion shown as blue box in Panel A.</p

Mapping the Abundance and Distribution of Adélie Penguins Using Landsat-7: First Steps towards an Integrated Multi-Sensor Pipeline for Tracking Populations at the Continental Scale

<div><p>The last several years have seen an increased interest in the use of remote sensing to identify the location of penguin colonies in Antarctica, and the estimation of the abundance of breeding pairs contained therein. High-resolution (sub-meter) commercial satellite imagery (e.g., Worldview-1, Quickbird) is capable of colony detection and abundance estimation for both large and small colonies, and has already been used in a continental-scale survey of Adélie penguins. Medium-resolution Landsat imagery has been used successfully to detect the presence of breeding penguins, but has not been used previously for abundance estimation nor evaluated in terms of its minimum colony size detection threshold. We report on the first comprehensive analysis of the performance of these two methods for both detection and abundance estimation, identify the sensor-specific failure modes that can lead to both false positives and false negatives, and compare the abundance estimates of each method over multiple spatial scales. We find that errors of omission using Landsat imagery are low for colonies larger than ∼10,000 breeding pairs. Both high-resolution and Landsat imagery can be used to obtain unbiased estimates of abundance, and while Landsat-derived abundance estimates have high variance for individual breeding colonies relative to estimates derived from high-resolution imagery, this difference declines as the spatial domain of interest is increased. At the continental scale, abundance estimates using the two methods are roughly equivalent. Our comparison of these two methods represents a bridge between the more developed high-resolution imagery, which can be expensive to obtain, and the medium-resolution Landsat-7 record, which is freely available; this comparison of methodologies represents an essential step towards integration of these disparate sources of data for regional assessments of Adélie population abundance and distribution.</p></div

Appendix A. Survivorship data for the 58 mammal species considered in survivorship analysis.

Survivorship data for the 58 mammal species considered in survivorship analysis

Probability of detection using Landsat-7 retrieval algorithm.

<p>Colony sizes (breeding pairs) at various probabilities of detection using Landsat-7 retrieval algorithm.</p><p>Probability of detection using Landsat-7 retrieval algorithm.</p

Colony size distribution censored by Landsat-7 detection limit.

<p>We used a log-normal distribution model for colony size to calculate the total abundance of Adélie penguins not captured by our Landsat survey (see Eq. 2). The influence of size-dependent non-detection on the distribution of colony sizes detected by Landsat in continental Antarctica (A) and on the Antarctic Peninsula (B) can be seen in the difference between the modelled distribution of colony sizes (log-scale) for all Adélie penguin colonies (purple) and the distribution of colony sizes as detected by Landsat (green).</p

Probability of detection using Landsat-7 imagery.

<p>(A, B) The number of Adélie penguin colonies located (white) and missed (shaded) by the Landsat retrieval method for continental Antarctica (A) and the Antarctic Peninsula (B). The percent contribution of each bin to the total population is also illustrated. The horizontal axis is logarithmic, with boundaries equal to 10⁰, 10¹, 10^1.5, 10², 10^2.5, etc. (C, D) Probability of detection as a function of colony size (as reported by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113301#pone.0113301-Lynch1" target="_blank">[5]</a>) along the coast of continental Antarctica (C) and on the Antarctic Peninsula (D). Gray line represents best-fitting logistic model. Inset represents portion of the plot shaded in light gray.</p