Spatial scale effects of sampling on the interpolation of species distribution models in the southwestern Amazon.

Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

Estimation of climatological water deficit in an experimental watershed in the brazilian cerrado.

ABSTRACT: This study aimed to estimate the probability of climatological water deficit in an experimental watershed in the Cerrado biome, located in the central plateau of Brazil. For that, it was used a time series of 31 years (1982?2012). The probable climatological water deficit was calculated by the difference between rainfall and probable reference evapotranspiration, on a decennial scale. The reference evapotranspiration (ET0) was estimated by the standard FAO-56 Penman-Monteith method. To estimate water deficit, it was used gamma distribution, time series of rainfall and reference evapotranspiration. The adherence of the estimated probabilities to the observed data was verified by the Kolmogorov-Smirnov nonparametric test, with significance level (a-0.05), which presented a good adjustment to the distribution models. It was observed a climatological water deficit, in greater or lesser intensity, between the annual decennials 2 and 32.

Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models.

For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields). Summarised as CDFs, such forecasts allow an easy quantitative assessment of possible, alternative actions. Although the degree of uncertainty associated with CDF estimation could influence decisions, such information is rarely provided. Hence, we propose Cox-type regression models (CRMs) as a statistical framework for making inferences on CDFs in climate science. CRMs were designed for modelling probability distributions rather than just mean or median values. This makes the approach appealing for risk assessments where probabilities of extremes are often more informative than central tendency measures. CRMs are semi-parametric approaches originally designed for modelling risks arising from time-to-event data. Here we extend this original concept beyond time-dependent measures to other variables of interest. We also provide tools for estimating CDFs and surrounding uncertainty envelopes from empirical data. These statistical techniques intrinsically account for non-stationarities in time series that might be the result of climate change. This feature makes CRMs attractive candidates to investigate the feasibility of developing rigorous global circulation model (GCM)-CRM interfaces for provision of user-relevant forecasts. To demonstrate the applicability of CRMs, we present two examples for El Ni ? no/Southern Oscillation (ENSO)-based forecasts: the onset date of the wet season (Cairns, Australia) and total wet season rainfall (Quixeramobim, Brazil). This study emphasises the methodological aspects of CRMs rather than discussing merits or limitations of the ENSO-based predictors.

Impacts of climate change and species distribution modelling: future scenarios to endangered species in South Brazil.

Earth climate has changed significantly in the last century and the different models indicate that it will continue to change over the next decades, even if the emission of greenhouse gases stop immediately. These changes have impact on different plant populations, as well as in the actual distribution of several species. As plants, in general, have a smaller capacity of dispersion compared with the animals it is likely that they will suffer the impacts of the climate change more intensively.