Marked decline in forest-dependent small mammals following habitat loss and fragmentation in an Amazonian deforestation frontier

Agricultural frontier expansion into the Amazon over the last four decades has created million hectares of fragmented forests. While many species undergo local extinctions within remaining forest patches, this may be compensated by native species from neighbouring open-habitat areas potentially invading these patches, particularly as forest habitats become increasingly degraded. Here, we examine the effects of habitat loss, fragmentation and degradation on small mammal assemblages in a southern Amazonian deforestation frontier, while accounting for species-specific degree of forest-dependency. We surveyed small mammals at three continuous forest sites and 19 forest patches of different sizes and degrees of isolation. We further sampled matrix habitats adjacent to forest patches, which allowed us to classify each species according to forest-dependency and generate a community-averaged forest-dependency index for each site. Based on 21,568 trap-nights, we recorded 970 small mammals representing 20 species: 12 forest-dependents, 5 matrix-tolerants and 3 open-habitat specialists. Across the gradient of forest patch size, small mammal assemblages failed to show the typical species-area relationship, but this relationship held true when either species abundance or composition was considered. Species composition was further mediated by community-averaged forest-dependency, so that smaller forest patches were occupied by a lower proportion of forest-dependent rodents and marsupials. Both species richness and abundance increased in less isolated fragments surrounded by structurally simplified matrix habitats (e.g. active or abandoned cattle pastures). While shorter distances between forest patches may favour small mammal abundances, forest area and matrix complexity dictated which species could persist within forest fragments according to their degree of forest-dependency. Small mammal local extinctions in small forest patches within Amazonian deforestation frontiers are therefore likely offset by the incursion of open-habitat species. To preclude the dominance of those species, and consequent losses of native species and associated ecosystem functions, management actions should limit or reduce areas dedicated to pasture, additionally maintaining more structurally complex matrix habitats across fragmented landscapes

Wealth Accumulation and Activity Choice Evolution Among Amazonian Forest Peasant Households

This paper examines investment and livelihood decisions among forest peasant households in the Amazonian floodplain. A dynamic household model of multiple asset accumulation and activity choice under risk and credit constraints is developed by incorporating natural resource use and human capital evolution. Asset portfolios and sectoral incomes are estimated and then simulated to investigate the endowment and lifecycle dependency as well as the convergence/divergence of asset accumulation and corresponding activity choices. Physical asset endowment (especially land) and different human capital evolutions across activities help to explain forest peasants' livelihood choices, distinctive asset portfolios, and divergent income outcomes over the lifecycle.

A Framework to Adjust Dependency Measure Estimates for Chance

Estimating the strength of dependency between two variables is fundamental for exploratory analysis and many other applications in data mining. For example: non-linear dependencies between two continuous variables can be explored with the Maximal Information Coefficient (MIC); and categorical variables that are dependent to the target class are selected using Gini gain in random forests. Nonetheless, because dependency measures are estimated on finite samples, the interpretability of their quantification and the accuracy when ranking dependencies become challenging. Dependency estimates are not equal to 0 when variables are independent, cannot be compared if computed on different sample size, and they are inflated by chance on variables with more categories. In this paper, we propose a framework to adjust dependency measure estimates on finite samples. Our adjustments, which are simple and applicable to any dependency measure, are helpful in improving interpretability when quantifying dependency and in improving accuracy on the task of ranking dependencies. In particular, we demonstrate that our approach enhances the interpretability of MIC when used as a proxy for the amount of noise between variables, and to gain accuracy when ranking variables during the splitting procedure in random forests.Comment: In Proceedings of the 2016 SIAM International Conference on Data Minin

Development and application of a new Forestation Index: global forestation patterns and drivers

Deforestation has long been heavily studied; several proximate and underlying causes behind the global decrease of forest extent have been discussed. However, systematic analyses of positive examples are sparse, even if forestation is happening in almost 70 countries (on approximately 40% of the world forested area). This study focuses on countries where forest cover increased between 1990 and 2010. As "forests" is a heterogeneous group, a biodiversity-corrected Forestation Index is also introduced to distinguish between different forms of "environmentally valuable" new forests (that are expected to have positive impact on biodiversity) and monocultures (that are debatable with that respect). OLS regression is used to reveal factors that may influence the observed patterns. Our results present some evidence to support the existence of an environmental Kuznets curve (EKC). Direct conservation investments appear to have negative effect on forestation which implies substitution of measures. Several traditional factors, which are important in deforestation (such as corruption, economic freedom, etc.) seems to have no impact from forestation perspective. Results show that refinement is needed during the modelling of forestation and different types should be acknowledged - treatment of forests as a homogenous category is an oversimplification

Triangle-Intersecting Families of Graphs

A family of graphs F is said to be triangle-intersecting if for any two graphs G,H in F, the intersection of G and H contains a triangle. A conjecture of Simonovits and Sos from 1976 states that the largest triangle-intersecting families of graphs on a fixed set of n vertices are those obtained by fixing a specific triangle and taking all graphs containing it, resulting in a family of size (1/8) 2^{n choose 2}. We prove this conjecture and some generalizations (for example, we prove that the same is true of odd-cycle-intersecting families, and we obtain best possible bounds on the size of the family under different, not necessarily uniform, measures). We also obtain stability results, showing that almost-largest triangle-intersecting families have approximately the same structure.Comment: 43 page

Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks

Sustainability of the global environment is dependent on the accurate land cover information over large areas. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets were derived from a pixel-based single-date multi-spectral remotely sensed image with low accuracy. To improve the accuracy, the bottleneck is how to develop an accurate and effective image classification technique. By incorporating and utilizing the complete multi-spectral, multi-temporal and spatial information in remote sensing images and considering their inherit spatial and sequential interdependence, we propose a new patch-based RNN (PB-RNN) system tailored for multi-temporal remote sensing data. The system is designed by incorporating distinctive characteristics in multi-temporal remote sensing data. In particular, it uses multi-temporal-spectral-spatial samples and deals with pixels contaminated by clouds/shadow present in the multi-temporal data series. Using a Florida Everglades ecosystem study site covering an area of 771 square kilo-meters, the proposed PB-RNN system has achieved a significant improvement in the classification accuracy over pixel-based RNN system, pixel-based single-imagery NN system, pixel-based multi-images NN system, patch-based single-imagery NN system and patch-based multi-images NN system. For example, the proposed system achieves 97.21% classification accuracy while a pixel-based single-imagery NN system achieves 64.74%. By utilizing methods like the proposed PB-RNN one, we believe that much more accurate land cover datasets can be produced over large areas efficiently