Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Simple identification tools in FishBase

Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further development. It explores the possibility of a holistic and integrated computeraided strategy

Population assessment and feeding ecology of brown hyenas (hyaena brunnea) in Mountain Zebra National Park, Eastern Cape, South Africa

The development of many small (<400 km2), enclosed game reserves in the Eastern Cape Province of South Africa over the past 25 years has contributed greatly to the conservation of large carnivores. However, the brown hyena (Hyaena brunnea) is one of the least studied large carnivores in South Africa. Nevertheless, the reintroduction of this species (n=3 in 2008) into Mountain Zebra National Park (MZNP) provided the perfect opportunity to broaden our understanding of the role that this carnivore plays in an enclosed system. Camera trap data was collected for just over a year from April 2014 to April 2015 and brown hyena density estimates were calculated using spatially explicit capture-recapture analysis. Left-side images of brown hyenas were used in the analysis and 12 individuals were positively identified. The best model to estimate brown hyena density included a road covariate and estimated brown hyena density to be 6-10 individuals/100 km2 (an absolute abundance of between 12 and 21 individuals), which is higher than densities calculated for brown hyenas in other arid, open systems. In, addition, brown hyena scat samples were collected over a five year period from April 2011 to June 2015 and standard techniques for scat analysis were used to identify prey items. Cheetah (Acinonyx jubatus) and lion (Panthera leo) kill site data were used to investigate the impacts of these species on the diet of brown hyenas. Before the release of lions brown hyenas predominantly scavenged on medium-sized mammals, which was what the cheetahs mainly killed. However, after the release of the lions, brown hyenas predominantly scavenged on large mammals, which was what the lions primarily killed. The results from my study indicate that brown hyenas are most likely reaching high densities in enclosed systems, due to increased scavenging opportunities provided by other large predators. The rapid increase of brown hyena densities from small founder populations in enclosed reserves could result in inbreeding. Therefore, in order to successfully conserve brown hyenas and other large carnivores in South Africa, continual post-release monitoring and possible implementation of meta-population management schemes is required

The effect of predator activity, weather and habitat variation on activity patterns of rock and bush hyrax (Procavia capensis and Heterohyrax brucei) in a mountainous environment

The relationship between a predator and its prey is multifaceted and this study aims to expand one of the keystone concepts within predator prey interactions. The spatial and temporal changes in a prey animal’s behaviour when there is a perceived risk of predation. Hyrax, a small African mammal, plays an integral role in rocky habitats as a key food source for many predators. The purpose of this study was to test the impact of predation on hyrax using giving-up density (GUD) experiments to establish a landscape of fear. Due to the absence of interaction from the hyrax with the GUD experiments a camera trapping protocol was implemented to collect behavioural data on hyrax in the Soutpansberg Mountains in northern South Africa. The study aimed to test a number of conclusions made in the literature about hyrax including their classification as a diurnal species, the potential for nocturnal activity and the impact of predation on the diel cycle. Behavioural plasticity with respect to seasonal and environmental changes is also explored including the extent to which they display behavioural thermoregulation. Hyrax showed nocturnal activity with 8.4% of activity occurring within the dark hours of the 24-hour period but the level of predation pressure may be limiting the level of nocturnality. Hyraxes were also significantly impacted by environmental variables. Activity increased with temperature in all four seasons and rainfall had an immediate effect of reducing activity. It was shown that hyrax demonstrate site specific activity that is consistent over time but can vary within a location. This study is novel in its methods for studying hyrax and has presented new information on this understudied species that may have far reaching implications for future studies on hyrax and their predators

iEcology: Harnessing Large Online Resources to Generate Ecological Insights

Digital data are accumulating at unprecedented rates. These contain a lot of information about the natural world, some of which can be used to answer key ecological questions. Here, we introduce iEcology (i.e., internet ecology), an emerging research approach that uses diverse online data sources and methods to generate insights about species distribution over space and time, interactions and dynamics of organisms and their environment, and anthropogenic impacts. We review iEcology data sources and methods, and provide examples of potential research applications. We also outline approaches to reduce potential biases and improve reliability and applicability. As technologies and expertise improve, and costs diminish, iEcology will become an increasingly important means to gain novel insights into the natural world.Peer reviewe

Conservation Status of the Plains Spotted Skunk in Texas

Research of rare or understudied species often benefits from the use of multiple methods and survey techniques. The potentially endangered plains spotted skunk (Spilogale putorius interrupta) is an uncommon mephitid historically distributed throughout much of Texas. To assess the status of the skunk, I collected presence data using both field surveys and crowd source methods. Field surveys were conducted throughout the state using live traps, trail cameras, and track plates. Additional presence data were also compiled from academic, wildlife, and citizen scientists’ groups. Skunk presence data were used to create a species distribution model. The model predicts that the skunk is still widely distributed in Texas. The results of the project indicate that the skunk has low localized abundance, but there are at least 2 areas with high local abundance: native prairies northwest of Houston and mixed oak/juniper forests in the Cross Timbers ecoregion