Achieving conservation through cattle ranching: The case of the Brazilian Pantanal

Cattle ranching in the ~140,000 km2 Brazilian Pantanal is considered one of the most important cases of sustainable use of natural resources in the global south. The region has had a successful history of balancing environmental protection with the production of >3.8 million cattle. However, global change, infrastructure projects, and deforestation, threaten the sustainable use of the Pantanal. Here, using Ostrom's design principles as a framework, we interviewed 49 local stakeholders and conducted a review of secondary information aiming to evaluate the sustainability of cattle ranching practices across the region and the threats to it. We show that well-defined property boundaries, congruence between appropriation and provision rules through low-intensity cattle ranching, and co-management of resources, are all key components for achieving sustainability in the Pantanal. However, we documented shortcomings in satisfying critical aspects of Orstrom's design principles. Specifically, we argue that the Pantanal needs better biodiversity and behavior monitoring, the creation of platforms or mechanisms to solve local conflicts around resource access and use, recognition by governments and international bodies of the local efforts to promote local sustainability, and the creation of networks effectively connecting existing sustainability initiatives

Curriculum and curriculum access issues for students with special educational needs in post-primary settings: an international review

This report presents the findings of an evidence review designed to summarise what is known about good practice concerning the content of, and access to, the school curriculum for students with special educational needs. The review focused on postprimary settings where the issue is particularly pertinent; the focus of education shifts from being student-centred at primary level to being much more subject-focused in postprimary settings

Empirical GPS tracking data

Anonymised, empirical tracking data used to estimate home range areas based on various home range estimators

Data from: A comprehensive analysis of autocorrelation and bias in home range estimation

Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive dataset of GPS locations from 369 individuals representing 27 species distributed across 5 continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation (KDE) with four bandwidth optimizers (Gaussian reference function, autocorrelated-Gaussian reference function (AKDE), Silverman's rule of thumb, and least squares cross-validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed (IID) data. We then employ half-sample cross-validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation ($\hat{N}_\mathrm{area}$) to quantify the information content of each dataset. We found that AKDE 95\% area estimates were larger than conventional IID-based estimates by a mean factor of 2. The median number of cross-validated locations included in the holdout sets by AKDE 95\% (or 50\%) estimates was 95.3\% (or 50.1\%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing $\hat{N}_\mathrm{area}$. To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small $\hat{N}_\mathrm{area}$. While 72\% of the 369 empirical datasets had \textgreater1000 total observations, only 4\% had an $\hat{N}_\mathrm{area}$ \textgreater1000, where 30\% had an $\hat{N}_\mathrm{area}$ \textless30. In this frequently encountered scenario of small $\hat{N}_\mathrm{area}$, AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data