Resumo Neste trabalho objetivou-se avaliar quais espécies florestais apresentam sua distribuição espacial influenciada pelo efeito de borda. Inventários florestais anuais foram realizados em duas parcelas permanentes de 1 hectare cada, divididas em 10 subparcelas retangulares, delimitadas a cada 10 metros de distância da borda. Todos os indivíduos com DAP superior à 10 cm dentro das parcelas foram identificados ao nível de espécie, e para cada indivíduo foram coletadas suas variáveis alométricas e coordenadas geográficas, considerando as categorias vivos, recrutamento e mortalidade. Analisou-se a correlação entre a abundância de cada espécie em cada subparcela, e a distância da subparcela até a borda, pelo Coeficiente de Correlação de Spearman's e por um modelo aditivo generalizado (GAM) com base na distribuição de Poisson. A distribuição de algumas espécies e dos grupos ecológicos foram analisadas pelo modelo de densidade de Kernel. Foi observado um grande número de espécies pioneiras e secundárias iniciais com relação com a distância da borda, geralmente concentradas próximas à borda. As espécies secundárias tardias ou clímax foram observadas melhor distribuídas dentro das parcelas, apesar de que algumas espécies, como Eugenia uniflora foram negativamente afetadas pela presença da borda. A partir desses resultados observa-se que espécies de hábito heliófilo podem ser favorecidas por terem a habilidade de habitar áreas próximas à borda, apesar de que algumas espécies tolerantes à sombra possam também habitar essa região.

Abstract The objective of this research was to evaluate which species in the Brazilian Araucaria forest have its spatial distribution influenced by edge effects. We performed annual forest inventories inside two one-hectare plots, divided in 10 rectangular subplots, delimited by every 10 meters of edge distance. Each tree with at least 10 cm at DBH was identified to species level, and their allometric measurements and geographical coordinates were recorded considering the categories living, recruitment and mortality. We analyzed the correlation between the abundance of each species in each subplot and its distance by the Spearman's Correlation Coefficient and a Generalized Additive Model (GAM) with a Poisson distribution. We analyzed the distribution of some species and the ecological groups using a Kernel density model. We observed numerous pioneers and early secondary species with relationship with the edge distance, usually concentrated close to the edges. The late secondary/climax species are more evenly distributed in the plots, despite of some species, as Eugenia uniflora, are negatively affected by the edge. From these results, it is observed that some light demanding species can be favored to live close to the edges, even if some shadow tolerant species can inhabit this region as well.

ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.