No abstract

GC (2020). Long-term photo-id and satellite tracking reveal sex-biased survival linked to movements in an endangered species. Ecology.

Establishing how wildlife viewing pressure is distributed across individual animals within a population can inform the management of this activity, and ensure targeted individuals or groups are sufficiently protected. Here, we used social media data to quantify whether tourism pressure varies in a loggerhead sea turtle Caretta caretta population and elucidate the potential implications. Laganas Bay (Zakynthos, Greece) supports both breeding (migratory, and hence transient) and foraging (resident) turtles, with turtle viewing representing a major component of the tourism industry. Social media entries spanning two seasons (April to November, 2018 and 2019) were evaluated, and turtles were identified via photo‐identification. For the 2 years, 1684 and 2105 entries of 139 and 122 unique turtles were obtained from viewings, respectively (boats and underwater combined). However, while residents represented less than one‐third of uniquely identified turtles, they represented 81.9 and 87.9% of all entries. Even when the seasonal breeding population was present (May to July), residents represented more than 60% of entries. Notably, the same small number of residents (<10), mostly males, were consistently viewed in both years; however, different individuals were targeted by boats versus underwater. Thus, turtles appear to remain in the area despite high viewing intensity, possibly indicating low disturbance. However, photo‐identification records revealed a high risk of propeller and boat strike to residents (30%) leading to trauma and mortality. To reduce this threat, we recommend the compulsory use of propeller guards for all boats, compliance with speed regulations and the creation of temporary ‘refuge’ zones for resident animals at viewing hotspots, with these suggestions likely being relevant for other wildlife with similar population dynamics. In conclusion, social media represents a useful tool for monitoring individuals at a population scale, evaluating the pressure under which they are placed, and providing sufficient data to refine wildlife viewing guidelines and/or zoning.

Establishing how wildlife viewing pressure is distributed across individual animals within a population can inform the management of this activity, and ensure targeted individuals or groups are sufficiently protected. Here, we used social media data to quantify whether tourism pressure varies in a loggerhead sea turtle (Caretta caretta) population and elucidate potential implications. Laganas Bay (Zakynthos, Greece) supports both breeding (migratory, and hence transient) and foraging (resident) turtles, with turtle viewing representing a major component of the tourism industry. Social media entries spanning two seasons (April to November, 2018 and 2019) were evaluated, and turtles were identified via photo-identification. For both years, 1684 and 2105 entries of 139 and 122 unique turtles were obtained from viewings, respectively (boats and underwater combined). However, while residents represented less than one-third of uniquely identified turtles, they represented 81.9% and 87.9% of all entries. Even when the seasonal breeding population was present (May to July), residents represented more than 60% entries. Of note, the same small number of resident turtles (<10), mostly males, were consistently viewed in both years; however, different individuals were targeted by boats versus underwater. Thus, turtles appear to use and remain in the area despite high viewing intensity, possibly indicating low disturbance. However, photo-identification records revealed a high risk of propeller and boat strike to residents (30%) leading to trauma and mortality. To reduce this threat and ease viewing pressure, we recommend the compulsory use of propeller guards for all boats and the creation of temporary "refuge" zones for resident animals at viewing hotspots, with these suggestions likely being relevant for other wildlife with similar population dynamics. In conclusion, social media represents a useful tool for monitoring individuals at a population scale, evaluating the pressure under which they are placed, and providing sufficient data to refine wildlife viewing guidelines and/or zoning.

Quantitative magnetic resonance imaging (qMRI) is concerned with estimating (in physical units) values of magnetic and tissue parameters e.g., relaxation times T1, T2, or proton density ρ. Recently in [Ma et al., Nature, 2013], Magnetic Resonance Fingerprinting (MRF) was introduced as a technique being capable of simultaneously recovering such quantitative parameters by using a two step procedure: (i) given a probe, a series of magnetization maps are computed and then (ii) matched to (quantitative) parameters with the help of a pre-computed dictionary which is related to the Bloch manifold. In this paper, we first put MRF and its variants into a perspective with optimization and inverse problems to gain mathematical insights concerning identifiability of parameters under noise and interpretation in terms of optimizers. Motivated by the fact that the Bloch manifold is non-convex and that the accuracy of the MRF-type algorithms is limited by the "discretization size" of the dictionary, a novel physics-based method for qMRI is proposed. In contrast to the conventional two step method, our model is dictionary-free and is rather governed by a single non-linear equation, which is studied analytically. This non-linear equation is efficiently solved via robustified Newton-type methods. The effectiveness of the new method for noisy and undersampled data is shown both analytically and via extensive numerical examples for which also improvement over MRF and its variants is documented.Here m, yielding m = ρm, is the macroscopic magnetization of (Hydrogen) proton of some unitary density in the tissue under an external magnetic field B, and the relaxation rates T 1 and T 2 are associated model parameters. Further, m 0 represents an initial state. System (1.1) is instrumental in our quantification process established below and will be further described in Section 2.1.Although qMRI techniques are still in their infancy, several interesting ideas and methods have already been conceived. Early approaches [25] are based on a set of spin echo or inversion recovery images that are reconstructed from k-space data with respect to various repetition times (T R) and echo times (T E). In that context, acquisitions are designed for each parameter individually. The overall technique is often referred to as parametric mapping method and consists of two steps: (i) reconstruct a sequence of images as in qualitative MRI, and (ii) for each pixel of those images fit its intensity to an ansatz curve characterized by the magnetic parameter associated to the tissue imaged at that pixel. Based on this idea, many improvements have been suggested in the literature; see for instance [17]. The associated approaches aim to simplify the physical model and handle tissue parameters separately, as these are considered to be time consuming for the patient.Another line of research, initiated by Ma et al. in [27] and named Magnetic Resonance Fingerprinting (MRF), has recently gained considerable attention. First, in an offline phase, it builds a database (dictio...