Anticipating anthropogenic threats in acquiring new protected areas.

Biodiversity continues to decline despite protected area expansion and global conservation commitments. Biodiversity losses occur in existing protected areas, yet common methods used to select protected areas ignore postimplementation threats that reduce effectiveness. We developed a conservation planning framework that considers the ongoing anthropogenic threats within protected areas when selecting sites and the value of planning for costly threat-mitigating activities (i.e., enforcement) at the time of siting decisions. We applied the framework to a set of landscapes that contained the range of possible correlations between species richness and threat. Accounting for threats and implementing enforcement activities increased benefits from protected areas without increasing budgets. Threat information was valuable in conserving more species per spending level even without enforcement, especially on landscapes with randomly distributed threats. Benefits from including threat information and enforcement were greatest when human threats peaked in areas of high species richness and were lowest where human threats were negatively associated with species richness. Because acquiring information on threats and using threat-mitigating activities are costly, our findings can guide decision-makers regarding the settings in which to pursue these planning steps.

Anticipación de las amenazas antropogénicas durante la adquisición de áreas protegidas nuevas Resumen La biodiversidad sigue declinando a pesar de la expansión de áreas protegidas y los compromisos mundiales con la conservación. La pérdida de la biodiversidad ocurre en las áreas protegidas existentes, y todavía los métodos comunes usados para seleccionar las áreas protegidas ignoran las amenazas posteriores a la implementación, las cuales reducen la efectividad. Desarrollamos un marco de planeación de la conservación que considera las amenazas antropogénicas actuales dentro de las áreas protegidas durante la selección de sitios y el valor de la planeación de actividades mitigantes costosas, como la aplicación, al momento de decidir. Aplicamos nuestro marco a un conjunto de paisajes que comprende el rango de correlaciones posibles entre las amenazas y la riqueza de especies. Si consideramos las amenazas y la implementación de actividades de aplicación, los beneficios de las áreas protegidas incrementan sin incrementar el presupuesto. La información sobre las amenazas fue importante para la conservación de especies por nivel de gasto incluso sin la aplicación, especialmente en paisajes con amenazas distribuidas de forma azarosa. Los beneficios de incluir la información sobre las amenazas y la aplicación fueron mayores cuando las amenazas humanas llegaron a su tope en áreas con gran riqueza de especies y alcanzaron su punto más bajo cuando las amenazas humanas estaban asociadas negativamente con la riqueza de especies. Ya que es costoso adquirir información sobre las amenazas y mitigar las amenazas con actividades, nuestros descubrimientos pueden informar a los tomadores de decisiones con respecto al entorno para seguir los pasos de la planeación.

A Bayesian Network Approach for Friedreich Ataxia Severity Classification using Probability Modelling.

Friedreich ataxia (FRDA) requires an objective measure of severity to overcome the shortcoming of clinical scales when applied to trials for treatments. This is hindered due to the rarity of the disease resulting in small datasets. Further, the published quantitative measures for ataxia do not incorporate or underutilise expert knowledge. Bayesian Networks (BNs) provide a structure to adopt both subjective and objective measures to give a severity value while addressing these issues. The BN presented in this paper uses a hybrid learning approach, which utilises both subjective clinical assessments as well as instrumented measurements of disordered upper body movement of individuals with FRDA. The final model's estimates gave a 0.93 Pearson correlation with low error, 9.42 root mean square error and 7.17 mean absolute error. Predicting the clinical scales gave 94% accuracy for Upright Stability and Lower Limb Coordination and 67% accuracy for Functional Staging, Upper Limb Coordination and Activities of Daily Living.Clinical relevance- Due to the nature of rare diseases conventional machine learning is difficult. Most clinical trials only generate small datasets. This approach allows the combination of expert knowledge with instrumented measures to develop a clinical decision support system for the prediction of severity.

Optimizing conservation planning for multiple cohabiting species.

Conservation planning often involves multiple species occupying large areas including habitat sites with varying characteristics. For a given amount of financial resources, designing a spatially coherent nature reserve system that provides the best possible protection to targeted species is an important ecological and economic problem. In this paper, we address this problem using optimization methods. Incorporating spatial criteria in an optimization framework considering spatial habitat needs of multiple species poses serious challenges because of modeling and computational complexities. We present a novel linear integer programming model to address this issue considering spatial contiguity and compactness of the reserved area. The model uses the concept of path in graph theory to ensure contiguity and minimizes the sum of distances between selected sites and a central site in individual reserves to promote compactness. We test the computational efficiency of the model using randomly generated data sets. The results show that the model can be solved quite efficiently in most cases. We also present an empirical application of the model to simultaneous protection of two cohabiting species, Gopher Tortoise and Gopher Frogs, in a military installation in Georgia, USA.

Impact of a local, coastal community based management regime when defining marine protected areas: Empirical results from a study in Okinawa, Japan.

There is a growing impetus to increase marine protected areas coverage globally from 6% to 30% in 2030. Successfully establishing and maintaining marine protected areas require incorporating public preferences into their establishment and management. We investigate the role of alternate management regimes (top-down and bottom-up) on preferences for marine protected areas and the marginal rate of substitution between natural and man-made capital using a case study in the Asia-Pacific region of Okinawa, Japan. We implemented a choice experiment survey to infer which attributes of marine protected areas are most important for the respondents. We use our survey results to calculate respondents' willingness to support marine protected areas in Okinawa. This study contributes to the policy debate on management of marine protected areas with empirical data that characterizes the perception of Okinawan residents with respect to the role of local coastal communities (bottom-up) compared to central government based agencies (top-down) management. We extend the analysis and estimate the trade-offs to residents in Okinawa between natural capital (i.e. coral coverage and marine biodiversity attribute) and man-made capital (i.e. restrictions on coastal development). We find that the underlying management regime affects the local residents' valuation of the marine protected area with residents showing a higher willingness to support bottom-up management regimes. There is also substantial differences in the willingness to support different characteristics of marine protected areas by management type. Finally, we find that the marginal rate of substitution between natural capital and man-made capital varies by management type such that residents would need to be compensated relatively less in terms of man-made capital in the presence of a policy scenario that proposes an increase in natural capital under a bottom-up management regime.

A dynamic simulation/optimization model for scheduling restoration of degraded military training lands.

Intensive use of military vehicles on Department of Defense training installations causes deterioration in ground surface quality. Degraded lands restrict the scheduled training activities and jeopardize personnel and equipment safety. We present a simulation-optimization approach and develop a discrete dynamic optimization model to determine an optimum land restoration for a given training schedule and availability of financial resources to minimize the adverse effects of training on military lands. The model considers weather forecasts, scheduled maneuver exercises, and unique qualities and importance of the maneuver areas. An application of this approach to Fort Riley, Kansas, shows that: i) starting with natural conditions, the total amount of training damages would increase almost linearly and exceed a quarter of the training area and 228 gullies would be formed (mostly in the intensive training areas) if no restoration is carried out over 10 years; ii) assuming an initial state that resembles the present conditions, sustaining the landscape requires an annual restoration budget of $957 thousand; iii) targeting a uniform distribution of maneuver damages would increase the total damages and adversely affect the overall landscape quality, therefore a selective restoration strategy may be preferred; and iv) a proactive restoration strategy would be optimal where land degradations are repaired before they turn into more severe damages that are more expensive to repair and may pose a higher training risk. The last finding can be used as a rule-of-thumb for land restoration efforts in other installations with similar characteristics.