Informing aerial total counts with demographic models: population growth of Serengeti elephants not explained purely by demography

Conservation management is strongly shaped by the interpretation of population trends. In the Serengeti ecosystem, Tanzania, aerial total counts indicate a striking increase in elephant abundance compared to all previous censuses. We developed a simple age-structured population model to guide interpretation of this reported increase, focusing on three possible causes: (1) in situ population growth, (2) immigration from Kenya, and (3) differences in counting methodologies over time. No single cause, nor the combination of two causes, adequately explained the observed population growth. Under the assumptions of maximum in situ growth and detection bias of 12.7% in previous censuses, conservative estimates of immigration from Kenya were between 250 and 1,450 individuals. Our results highlight the value of considering demography when drawing conclusions about the causes of population trends. The issues we illustrate apply to other species that have undergone dramatic changes in abundance, as well as many elephant populations

Assessing rotation-invariant feature classification for automated wildebeest population counts

Accurate and on-demand animal population counts are the holy grail for wildlife conservation organizations throughout the world because they enable fast and responsive adaptive management policies. While the collection of image data from camera traps, satellites, and manned or unmanned aircraft has advanced significantly, the detection and identification of animals within images remains a major bottleneck since counting is primarily conducted by dedicated enumerators or citizen scientists. Recent developments in the field of computer vision suggest a potential resolution to this issue through the use of rotation-invariant object descriptors combined with machine learning algorithms. Here we implement an algorithm to detect and count wildebeest from aerial images collected in the Serengeti National Park in 2009 as part of the biennial wildebeest count. We find that the per image error rates are greater than, but comparable to, two separate human counts. For the total count, the algorithm is more accurate than both manual counts, suggesting that human counters have a tendency to systematically over or under count images. While the accuracy of the algorithm is not yet at an acceptable level for fully automatic counts, our results show this method is a promising avenue for further research and we highlight specific areas where future research should focus in order to develop fast and accurate enumeration of aerial count data. If combined with a bespoke image collection protocol, this approach may yield a fully automated wildebeest count in the near future

A comparison of deep learning and citizen science techniques for counting wildlife in aerial survey images

Files contain wildebeest counts for images taken during the 2015 wildebeest survey - expert_counts: counting performed by DJLJ - raw_zooniverse_counts: all counts from zooniverse volunteers (usernames redacted) - yolo_counts: counting performed by deep learning object detection algorith

The basis functions, <i>U</i>_{<i>j</i>, <i>k</i>}, for performing convolutions are constructed from Fourier modes on concentric circles.

<p>The parameter <i>j</i> determines the radial distance from the centre of the object, while <i>k</i> is the wavenumber. The images show the real and imaginary part of the basis function.</p

Example images.

<p>From top: Correctly detected wildebeest; Pattern and structure in the landscape frequently lead to false positives; The method is able to distinguish between different species; Species such as zebra, that have distinct body shapes are frequently not identified as wildebeest; The ability to distinguish between species is dependent on sufficient training examples, here the algorithm has misidentified a flock of juvenile ostrich as wildebeest.</p

Comparing the performance of automated and manual counters.

<p>(A) Root mean square error of counts. The correct count for each image is assumed to be the third and final count. Average per image error is shown for the algorithm (blue line) as a function of the number of training samples from the 2012 survey that were used. For comparison, per image error is shown for each of the first pass human counts (red, green lines). (B) Total wildebeest counted within the image set. The final count is shown by the dashed line. The algorithm (blue line) outperforms both human counters in attaining a closer estimate to the true value. This is because the algorithm exhibits no systematic tendency to over or under count. It should be noted that 3000 was the maximum number of training samples available, and it is plausible that the automated total count will drop below the true count before it asymptotes. (C) Individual image errors. The black line is the <i>y</i> = <i>x</i> line for reference. While average per image errors are comparable between automated and human counters, the algorithm makes large errors in a small subset of images. Images that contain many false negatives tend to be darker than the training samples, while false positives occur when there is a lot of structure in the landscape. (D) A comparison of image light levels and under counting. A linear regression shows a significant negative relationship between image light level (average of value component of HSV image) and the amount of under counting (<i>β</i>₁ = −1.37, <i>R</i>² = 0.12). The under count fraction is calculated as and images for which algorithm count > true count are excluded. Point sizes are proportional to the absolute value of the under count of wildebeest in the image.</p

Confusion matrix.

<p>As the accuracy based on the total count does not indicate precision or recall, performance metrics were recorded for a random subset of 100 images. Negative totals are based on the number of non-overlapping regions within each image that are approximately equal in area to a single wildebeest. From these results: precision , recall .</p

Comparison of counts between manual and automated methods.

<p>Comparison of counts between manual and automated methods.</p