Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania

Conservation biologists increasingly employ drones to reduce poaching of animals. However, there are no published studies on the probability of detecting poachers and the factors influencing detection. In an experimental setting with voluntary subjects, we evaluated the influence of various factors on poacher detection probability: camera (visual spectrum: RGB and thermal infrared: TIR), density of canopy cover, subject distance from the image centreline, subject contrast against the background, altitude of the drone and image analyst. We manually analysed the footage and marked all recorded subject detections. A multilevel model was used to analyse the TIR image data and a general linear model approach was used for the RGB image data. We found that the TIR camera had a higher detection probability than the RGB camera. Detection probability in TIR images was significantly influenced by canopy density, subject distance from the centreline and the analyst. Detection probability in RGB images was significantly influenced by canopy density, subject contrast against the background, altitude and the analyst. Overall, our findings indicate that TIR cameras improve human detection, particularly at cooler times of the day, but this is significantly hampered by thick vegetation cover. The effects of diminished detection with increased distance from the image centreline can be improved by increasing the overlap between images although this requires more flights over a specific area. Analyst experience also contributed to increased detection probability, but this might cease being a problem following the development of automated detection using machine learning

Urbanization drives community shifts towards thermophilic and dispersive species at local and landscape scales

The increasing conversion of agricultural and natural areas to human-dominated urban landscapes is predicted to lead to a major decline in biodiversity worldwide. Two conditions that typically differ between urban environments and the surrounding landscape are increased temperature, and high patch isolation and habitat turnover rates. However, the extent and spatial scale at which these altered conditions shape biotic communities through selection and/or filtering on species traits are currently poorly understood. We sampled carabid beetles at 81 sites in Belgium using a hierarchically nested sampling design wherein three local-scale (200 x 200 m) urbanization levels were repeatedly sampled across three landscape-scale (3 x 3 km) urbanization levels. First, we showed that communities sampled in the most urbanized locations and landscapes displayed a distinct species composition at both local and landscape scale. Second, we related community means of species-specific thermal preferences and dispersal capacity (based on European distribution and wing morphology, respectively) to the urbanization gradients. We showed that urban communities consisted on average of species with a preference for higher temperatures and with better dispersal capacities compared to rural communities. These shifts were caused by an increased number of species tolerating higher temperatures, a decreased richness of species with low thermal preference, and an almost complete depletion of species with very low-dispersal capacity in the most urbanized localities. Effects of urbanization were most clearly detected at the local scale, although more subtle effects could also be found at the scale of entire landscapes. Our results demonstrate that urbanization may fundamentally and consistently alter species composition by exerting a strong filtering effect on species dispersal characteristics and favouring replacement by warm-dwelling species

Making space for shellfish farming along the Adriatic coast

This work focuses on the selection of new areas for shellfish farming along the coast of the Northern Adriatic Sea (Italy). Shellfish site suitability was assessed by means of a methodology based on Spatial Multi-Criteria Evaluation (SMCE), which provided the framework to combine mathematical models and operational oceanography products. Intermediate level criteria considered in the analysis included optimal growth conditions, environmental interactions, and socio-economic evaluation (e.g. organic carbon deposition; distance to harbour). Results showed that the whole coastal area comprised within 0 and 3 nm is highly suitable for farming of mussel, while the area comprised between 3 and 12 nm is divided between a highly suitable northern part, and a less suitable southern one. Seven different scenarios of development of shellfish aquaculture industry were explored. The introduction of a new species, and the assessment of the exposure to storm events are specific aspects taken into account in development scenarios. Results show that the degree of suitability for shellfish aquaculture in this area would not change dramatically with the introduction of oyster farming. Furthermore, results highlight that: (i) the growth potential in this area is high; (ii) the space with suitability index >0.5 increases when prioritizing the optimal growth condition criteria, and (iii) the socio-economic is the most restrictive Intermediate Level Criteria. Results were discussed by deriving general lessons concerning the use of SMCE in aquaculture space allocation, from the specific application in the Northern Adriatic Sea. Challenges and opportunities related to the proposed methodological framework, with particular reference to the use of resources provided by remote sensing and operational oceanography by means of mathematical models, were also discussed. Results can support a science-based identification of allocated zones for aquaculture in order to avoid conflicts, and promote sustainable aquaculture in the Mediterranean Sea, where the space for these activities is becoming increasingly limited

Recent autopolyploidization in a naturalized population of Mimulus guttatus (Phrymaceae)

Polyploidization can trigger rapid changes in morphology, ecology and genomics even in the absence of associated hybridization. However, disentangling the immediate biological consequences of genome duplication from the evolutionary change that subsequently accumulates in polyploid lineages requires the identification and analysis of recently formed polyploids. We investigated the incidence of polyploidization in introduced populations of Mimulus guttatus in the UK and report the discovery of a new mixed diploid–autopolyploid population in the Shetland Isles. We conducted a genetic analysis of six Shetland populations to investigate whether tetraploid individuals may have originated from local diploid plants and compared the morphology of tetraploids and local diploids to assess the phenotypic consequences of genome duplication. Autotetraploids are genetically close to sympatric diploids, suggesting that they have originated locally. Phenotypically, whole genome duplication has resulted in clear differences between ploidies, with tetraploids showing delayed phenology and larger flowers, leaves and stems than diploids. Our results support the hypothesis that novel evolutionary lineages can rapidly originate via polyploidization. The newly discovered autopolyploidization event in a non-native Mimulus population provides an opportunity to investigate the early causes and consequences of polyploidization in the wild

Benthic Photo Survey: Software for Geotagging, Depth-tagging, and Classifying Photos from Survey Data and Producing Shapefiles for Habitat Mapping in GIS

Photo survey techniques are common for resource management, ecological research, and ground truthing for remote sensing but current data processing methods are cumbersome and inefficient. The Benthic Photo Survey (BPS) software described here was created to simplify the data processing and management tasks associated with photo surveys of underwater habitats. BPS is free and open source software written in Python with a QT graphical user interface. BPS takes a GPS log and jpeg images acquired by a diver or drop camera and assigns the GPS position to each photo based on time-stamps (i.e. geotagging). Depth and temperature can be assigned in a similar fashion (i.e. depth-tagging) using log files from an inexpensive consumer grade depth / temperature logger that can be attached to the camera. BPS provides the user with a simple interface to assign quantitative habitat and substrate classifications to each photo. Location, depth, temperature, habitat, and substrate data are all stored with the jpeg metadata in Exchangeable image file format (Exif). BPS can then export all of these data in a spatially explicit point shapefile format for use in GIS. BPS greatly reduces the time and skill required to turn photos into usable data thereby making photo survey methods more efficient and cost effective. BPS can also be used, as is, for other photo sampling techniques in terrestrial and aquatic environments and the open source code base offers numerous opportunities for expansion and customization