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...

We used Argos‐linked Fastloc‐Global Positioning System (Argos‐linked Fastloc‐GPS) satellite tags to investigate how loggerhead sea turtles use neritic foraging habitats at multiple scales. Out of 24 turtles, six individuals used more than one foraging site, with all sites being separated by >25 km. These six individuals used up to four sites, remaining at each site for a mean of 150 days and returning to the same site a minimum of 52 days later. The other 18 turtles remained in a single site. The area within sites was not used uniformly, with 15 out of 24 turtles exhibiting complex movement patterns within and amongst up to five focal patches, which were typically 0.1–5.0 km apart within a single site. Movements between sites and patches might sometimes have reflected overwintering behaviour; however, similar movement patterns occurred at multiple times of the year, suggesting other factors were also involved. Use of multiple sites and patches might be driven by differences in resource availability, such as food and/or night‐time refuges, competition, or exploratory movement to investigate or locate alternative patches. We confirmed competition via direct visual observations of aggressive interactions between individuals at one foraging patch. Our results illustrate the importance of standardizing data to the same number of locations per day and night to accurately delineate key areas used by turtles or for evidence‐based marine protected area planning.

No abstract

In this work, we introduce a function space setting for a wide class of structural/weighted total variation (TV) regularization methods motivated by their applications in inverse problems. In particular, we consider a regularizer that is the appropriate lower semi-continuous envelope (relaxation) of a suitable total variation type functional initially defined for sufficiently smooth functions.We study examples where this relaxation can be expressed explicitly, and we also provide refinements for weighted total variation for a wide range of weights. Since an integral characterization of the relaxation in function space is, in general, not always available, we show that, for a rather general linear inverse problems setting, instead of the classical Tikhonov regularization problem, one can equivalently solve a saddle-point problem where no a priori knowledge of an explicit formulation of the structural TV functional is needed. In particular, motivated by concrete applications, we deduce corresponding results for linear inverse problems with norm and Poisson log-likelihood data discrepancy terms. Finally, we provide proof-of-concept numerical examples where we solve the saddle-point problem for weighted TV denoising as well as for MR guided PET image reconstruction. Ω j v (x, ∇u(x)) dx,