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We present the first high-resolution trace element (Mg/Ca, Sr/Ca, Ba/Ca) record from a stalagmite in southwestern Romania covering the last 3.6 ka, which provides the potential for quantitative climate reconstruction. Precise age control is based on three independent dating methods, in particular for the last 250 yr, where chemical lamina counting is combined with the identification of the 20th century radiocarbon bomb peak and Th-230/U dating. Long-term cave monitoring and model simulations of drip water and speleothem elemental variability indicate that precipitation-related processes are the main drivers of speleothem Mg/Ca ratios. Calibration against instrumental climate data shows a significant anti-correlation of speleothem Mg/Ca ratios with autumn/winter (October to March) precipitation (r = -0.61, p < 0.01), which is statistically robust when considering age uncertainties and auto-correlation. This relationship is used to develop a quantitative reconstruction of autumn/winter precipitation. During the late Holocene, our data suggest a heterogeneous pattern of past regional winter hydroclimate in the Carpathian/Balkan realm, along with intermittent weakening of the dominant influence of North Atlantic forcing. In agreement with other regional paleo-hydrological reconstructions, the observed variability reveals periodically occurring strong NW-SE hydro-climate gradients. We hypothesize, that this pattern is caused by shifts of the eastern edge of the area of influence of the NAO across central eastern Europe due to the confluence of North Atlantic forcing, and other climatic features such as the East Atlantic/Western Russia (EAWR) pattern. (C) 2018 Elsevier B.V. All rights reserved.
AimAdvancement in ecological methods predicting species distributions is a crucial precondition for deriving sound management actions. Maximum entropy (MaxEnt) models are a popular tool to predict species distributions, as they are considered able to cope well with sparse, irregularly sampled data and minor location errors. Although a fundamental assumption of MaxEnt is that the entire area of interest has been systematically sampled, in practice, MaxEnt models are usually built from occurrence records that are spatially biased towards better-surveyed areas. Two common, yet not compared, strategies to cope with uneven sampling effort are spatial filtering of occurrence data and background manipulation using environmental data with the same spatial bias as occurrence data. We tested these strategies using simulated data and a recently collated dataset on Malay civet Viverra tangalunga in Borneo.
LocationBorneo, Southeast Asia.
MethodsWe collated 504 occurrence records of Malay civets from Borneo of which 291 records were from 2001 to 2011 and used them in the MaxEnt analysis (baseline scenario) together with 25 environmental input variables. We simulated datasets for two virtual species (similar to a range-restricted highland and a lowland species) using the same number of records for model building. As occurrence records were biased towards north-eastern Borneo, we investigated the efficacy of spatial filtering versus background manipulation to reduce overprediction or underprediction in specific areas.
ResultsSpatial filtering minimized omission errors (false negatives) and commission errors (false positives). We recommend that when sample size is insufficient to allow spatial filtering, manipulation of the background dataset is preferable to not correcting for sampling bias, although predictions were comparatively weak and commission errors increased.
Main ConclusionsWe conclude that a substantial improvement in the quality of model predictions can be achieved if uneven sampling effort is taken into account, thereby improving the efficacy of species conservation planning.
Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlativeprocess spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the processcorrelation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.