Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect

1. Globally, protected areas are being established to protect biodiversity and to promote ecosystem resilience. The typical spatial conservation planning process leading to the creation of these protected areas focuses on representation and replication of ecological features, often using decision support tools such as Marxan. Yet, despite the important role ecological connectivity has in metapopulation persistence and resilience, Marxan currently requires manual input or specialized scripts to explicitly consider connectivity. 2. ‘Marxan Connect’ is a new open source, open access Graphical User Interface (GUI) tool designed to assist conservation planners with the appropriate use of data on ecological connectivity in protected area network planning. 3. Marxan Connect can facilitate the use of estimates of demographic connectivity (e.g. derived from animal tracking data, dispersal models, or genetic tools) or structural landscape connectivity (e.g. isolation by resistance). This is accomplished by calculating metapopulation‐relevant connectivity metrics (e.g. eigenvector centrality) and treating those as conservation features or by including the connectivity data as a spatial dependency amongst sites in the prioritization process. 4. Marxan Connect allows a wide group of users to incorporate directional ecological connectivity into conservation planning with Marxan. The solutions provided by Marxan Connect, combined with ecologically relevant post‐hoc testing, are more likely to support persistent and resilient metapopulations (e.g. fish stocks) and provide better protection for biodiversity

Integral transform solution of random coupled parabolic partial differential models

[EN] Random coupled parabolic partial differential models are solved numerically using random cosine Fourier transform together with non-Gaussian random numerical integration that captures the highly oscillatory behaviour of the involved integrands. Sufficient condition of spectral type imposed on the random matrices of the system is given so that the approximated stochastic process solution and its statistical moments are numerically convergent. Numerical experiments illustrate the results.Spanish Ministerio de Economia, Industria y Competitividad (MINECO); Agencia Estatal de Investigacion (AEI); Fondo Europeo de Desarrollo Regional (FEDER UE), Grant/Award Number: MTM2017-89664-PCasabán Bartual, MC.; Company Rossi, R.; Egorova, VN.; Jódar Sánchez, LA. (2020). Integral transform solution of random coupled parabolic partial differential models. Mathematical Methods in the Applied Sciences. 43(14):8223-8236. https://doi.org/10.1002/mma.6492S822382364314Bäck, J., Nobile, F., Tamellini, L., & Tempone, R. (2010). Stochastic Spectral Galerkin and Collocation Methods for PDEs with Random Coefficients: A Numerical Comparison. Spectral and High Order Methods for Partial Differential Equations, 43-62. doi:10.1007/978-3-642-15337-2_3Bachmayr, M., Cohen, A., & Migliorati, G. (2016). Sparse polynomial approximation of parametric elliptic PDEs. Part I: affine coefficients. ESAIM: Mathematical Modelling and Numerical Analysis, 51(1), 321-339. doi:10.1051/m2an/2016045Ernst, O. G., Sprungk, B., & Tamellini, L. (2018). Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables (with Application to Elliptic PDEs). SIAM Journal on Numerical Analysis, 56(2), 877-905. doi:10.1137/17m1123079Sheng, D., & Axelsson, K. (1995). Uncoupling of coupled flows in soil—a finite element method. International Journal for Numerical and Analytical Methods in Geomechanics, 19(8), 537-553. doi:10.1002/nag.1610190804Mitchell, J. K. (1991). Conduction phenomena: from theory to geotechnical practice. Géotechnique, 41(3), 299-340. doi:10.1680/geot.1991.41.3.299Das, P. K. (1991). Optical Signal Processing. doi:10.1007/978-3-642-74962-9Ashkenazy, Y. (2017). Energy transfer of surface wind-induced currents to the deep ocean via resonance with the Coriolis force. Journal of Marine Systems, 167, 93-104. doi:10.1016/j.jmarsys.2016.11.019Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 117(4), 500-544. doi:10.1113/jphysiol.1952.sp004764Galiano, G. (2012). On a cross-diffusion population model deduced from mutation and splitting of a single species. Computers & Mathematics with Applications, 64(6), 1927-1936. doi:10.1016/j.camwa.2012.03.045Casabán, M. C., Company, R., & Jódar, L. (2019). Numerical solutions of random mean square Fisher‐KPP models with advection. Mathematical Methods in the Applied Sciences, 43(14), 8015-8031. doi:10.1002/mma.5942Casabán, M. C., Company, R., & Jódar, L. (2019). Numerical Integral Transform Methods for Random Hyperbolic Models with a Finite Degree of Randomness. Mathematics, 7(9), 853. doi:10.3390/math7090853Shampine, L. F. (2008). Vectorized adaptive quadrature in MATLAB. Journal of Computational and Applied Mathematics, 211(2), 131-140. doi:10.1016/j.cam.2006.11.021Iserles, A. (2004). On the numerical quadrature of highly-oscillating integrals I: Fourier transforms. IMA Journal of Numerical Analysis, 24(3), 365-391. doi:10.1093/imanum/24.3.365Ma, J., & Liu, H. (2018). On the Convolution Quadrature Rule for Integral Transforms with Oscillatory Bessel Kernels. Symmetry, 10(7), 239. doi:10.3390/sym10070239Jódar, L., & Goberna, D. (1996). Exact and analytic numerical solution of coupled diffusion problems in a semi-infinite medium. Computers & Mathematics with Applications, 31(9), 17-24. doi:10.1016/0898-1221(96)00038-7Jódar, L., & Goberna, D. (1998). A matrix D’Alembert formula for coupled wave initial value problems. Computers & Mathematics with Applications, 35(9), 1-15. doi:10.1016/s0898-1221(98)00052-2Ostrowski, A. M. (1959). A QUANTITATIVE FORMULATION OF SYLVESTER’S LAW OF INERTIA. Proceedings of the National Academy of Sciences, 45(5), 740-744. doi:10.1073/pnas.45.5.740Ashkenazy, Y., Gildor, H., & Bel, G. (2015). The effect of stochastic wind on the infinite depth Ekman layer model. EPL (Europhysics Letters), 111(3), 39001. doi:10.1209/0295-5075/111/3900

Demystifying ecological connectivity for actionable spatial conservation planning

There is a disconnect between global high-level conservation goals and on-the-ground actions such as maintaining ecosystem services or persistence and local planning of protected areas. Dynamic processes such as ecological connectivity underpin species persistence and ecosystem resilience but are difficult to represent in mathematical spatial planning problems for protected areas. Quantitative and SMART (specific – measurable – action-oriented – realistic – time-bound) conservation objectives can provide a link between high-level conservation goals and local or regional design and implementation of functionally connected protected area networks. With current implementation gaps of protected area commitments and increasing climate change threats, there is tremendous opportunity to use quantifiable objectives for ecological connectivity as a vehicle to future-proof protected area networks to help achieve global conservation goals. Connectivity underpins the persistence of life; it needs to inform biodiversity conservation decisions. Yet, when prioritising conservation areas and developing actions, connectivity is not being operationalised in spatial planning. The challenge is the translation of flows associated with connectivity into conservation objectives that lead to actions. Connectivity is nebulous, it can be abstract and mean different things to different people, making it difficult to include in conservation problems. Here, we show how connectivity can be included in mathematically defining conservation planning objectives. We provide a path forward for linking connectivity to high-level conservation goals, such as increasing species’ persistence. We propose ways to design spatial management areas that gain biodiversity benefit from connectivity

Improving the design of industrial microwave processing systems through prediction of the dielectric properties of complex multi-layered materials

Rigorous design of industrial microwave processing systems requires in-depth knowledge of the dielectric properties of the materials to be processed. These values are not easy to measure, particularly when a material is multi-layered containing multiple phases, when one phase has a much higher loss than the other and the application is based on selective heating. This paper demonstrates the ability of the Clausius-Mossotti (CM) model to predict the dielectric constant of multi-layered materials. Furthermore, mixing rules and graphical extrapolation techniques were used to further evidence our conclusions and to estimate the loss factor. The material used for this study was vermiculite, a layered alumina-silicate mineral containing up to 10 % of an interlayer hydrated phase. It was measured at different bulk densities at two distinct microwave frequencies, namely 934 and 2143 MHz. The CM model, based on the ionic polarisability of the bulk material, gives only a prediction of the dielectric constant for experimental data with a deviation of less than 5 % at microwave frequencies. The complex refractive index model (CRIM), Landau, Lifshitz and Loyenga (LLL), Goldschmidt, Böttcher and Bruggeman-Hanai model equations are then shown to give a strong estimation of both dielectric constant and loss factor of the solid material compared to that of the measured powder with a deviation of less than 1 %. Results obtained from this work provide a basis for the design of further electromagnetic processing systems for multi-layered materials consisting of both high loss and low loss components

In Vivo Detection of Amyloid-β Deposits Using Heavy Chain Antibody Fragments in a Transgenic Mouse Model for Alzheimer's Disease

This study investigated the in vivo properties of two heavy chain antibody fragments (VHH), ni3A and pa2H, to differentially detect vascular or parenchymal amyloid-β deposits characteristic for Alzheimer's disease and cerebral amyloid angiopathy. Blood clearance and biodistribution including brain uptake were assessed by bolus injection of radiolabeled VHH in APP/PS1 mice or wildtype littermates. In addition, in vivo specificity for Aβ was examined in more detail with fluorescently labeled VHH by circumventing the blood-brain barrier via direct application or intracarotid co-injection with mannitol. All VHH showed rapid renal clearance (10–20 min). Twenty-four hours post-injection 99mTc-pa2H resulted in a small yet significant higher cerebral uptake in the APP/PS1 animals. No difference in brain uptake were observed for 99mTc-ni3A or DTPA(111In)-pa2H, which lacked additional peptide tags to investigate further clinical applicability. In vivo specificity for Aβ was confirmed for both fluorescently labeled VHH, where pa2H remained readily detectable for 24 hours or more after injection. Furthermore, both VHH showed affinity for parenchymal and vascular deposits, this in contrast to human tissue, where ni3A specifically targeted only vascular Aβ. Despite a brain uptake that is as yet too low for in vivo imaging, this study provides evidence that VHH detect Aβ deposits in vivo, with high selectivity and favorable in vivo characteristics, making them promising tools for further development as diagnostic agents for the distinctive detection of different Aβ deposits

Recruitment Constraints in Singapore's Fluted Giant Clam (Tridacna squamosa) Populations - A Dispersal Model Approach

10.1371/journal.pone.0058819PLoS ONE83