Model-based control of observer bias for the analysis of presence-only data in ecology

Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record sightings at some locations than others (hereafter "observer bias"). In this paper, we describe and evaluate a model-based approach to accounting for observer bias directly - by modelling presence locations as a function of known observer bias variables (such as accessibility variables) in addition to environmental variables, then conditioning on a common level of bias to make predictions of species occurrence free of such observer bias. We implement this idea using point process models with a LASSO penalty, a new presence-only method related to maximum entropy modelling, that implicitly addresses the "pseudo-absence problem" of where to locate pseudo-absences (and how many). The proposed method of bias-correction is evaluated using systematically collected presence/absence data for 62 plant species endemic to the Blue Mountains near Sydney, Australia. It is shown that modelling and controlling for observer bias significantly improves the accuracy of predictions made using presence-only data, and usually improves predictions as compared to pseudo-absence or "inventory" methods of bias correction based on absences from non-target species. Future research will consider the potential for improving the proposed bias-correction approach by estimating the observer bias simultaneously across multiple species. © 2013 Warton et al

Using Ecological Modelling Tools to Inform Policy Makers of Potential Changes in Crop Distribution: An Example with Cacao Crops in Latin America

International audienceSpecies distribution models (SDM) is a powerful simulation tool that has become widely used in the ecological and agronomical sciences. The use of easily available presence data, global downscaled climate layers and software that can run on desktop computer has contributed to their popularity. The most used application is based on maximum entropy models that fit presence data to a series of environmental descriptors. SDM can be used to predict crop distribution under future conditions but the level of uncertainty of those models can be very high. The best use of these models is to be used as generators of hypothesis to be combined with other type of analysis

Contribution of spatially explicit models to climate change adaptation and mitigation plans for a priority forest habitat

Climate change will impact forest ecosystems, their biodiversity and the livelihoods they sustain. Several adaptation and mitigation strategies to counteract climate change impacts have been proposed for these ecosystems. However, effective implementation of such strategies requires a clear understanding of how climate change will influence the future distribution of forest ecosystems. This study uses maximum entropy modelling (MaxEnt) to predict environmentally suitable areas for cork oak (Quercus suber) woodlands, a socio-economically important forest ecosystem protected by the European Union Habitats Directive. Specifically, we use two climate change scenarios to predict changes in environmental suitability across the entire geographical range of the cork oak and in areas where stands were recently established. Up to 40 % of current environmentally suitable areas for cork oak may be lost by 2070, mainly in northern Africa and southern Iberian Peninsula. Almost 90 % of new cork oak stands are predicted to lose suitability by the end of the century, but future plantations can take advantage of increasing suitability in northern Iberian Peninsula and France. The predicted impacts cross-country borders, showing that a multinational strategy, will be required for cork oak woodland adaptation to climate change. Such a strategy must be regionally adjusted, featuring the protection of refugia sites in southern areas and stimulating sustainable forest management in areas that will keep long-term suitability. Afforestation efforts should also be promoted but must consider environmental suitability and land competition issues

Gelechiidae Moths Are Capable of Chemically Dissolving the Pollen of Their Host Plants: First Documented Sporopollenin Breakdown by an Animal

Background: Many insects feed on pollen surface lipids and contents accessible through the germination pores. Pollen walls, however, are not broken down because they consist of sporopollenin and are highly resistant to physical and enzymatic damage. Here we report that certain Microlepidoptera chemically dissolve pollen grains with exudates from their mouthparts. Methodology/Principal Findings: Field observations and experiments in tropical China revealed that two species of Deltophora (Gelechioidea) are the exclusive pollinators of two species of Phyllanthus (Phyllanthaceae) on which their larvae develop and from which the adults take pollen and nectar. DNA sequences placed the moths and plants phylogenetically and confirmed that larvae were those of the pollinating moths; molecular clock dating suggests that the moth clade is younger than the plant clade. Captive moths with pollen on their mouthparts after 2-3 days of starvation no longer carried intact grains, and SEM photographs showed exine fragments on their proboscises. GC-MS revealed cis-b-ocimene as the dominant volatile in leaves and flowers, but GC-MS analyses of proboscis extracts failed to reveal an obvious sporopollenindissolving compound. A candidate is ethanolamine, which occurs in insect hemolymphs and is used to dissolve sporopollenin by palynologists. Conclusions/Significance: This is the first report of any insect and indeed any animal chemically dissolving pollen

FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium.

BACKGROUND: Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium. METHODS: Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression. RESULTS: Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95% confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2. CONCLUSION: Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2

Environmental drivers of spatiotemporal foraging intensity in fruit bats and implications for Hendra virus ecology

In the Australian subtropics, flying-foxes (family Pteropididae) play a fundamental ecological role as forest pollinators. Flying-foxes are also reservoirs of the fatal zoonosis, Hendra virus. Understanding flying fox foraging ecology, particularly in agricultural areas during winter, is critical to determine their role in transmitting Hendra virus to horses and humans. We developed a spatiotemporal model of flying-fox foraging intensity based on foraging patterns of 37 grey-headed flying-foxes (Pteropus poliocephalus) using GPS tracking devices and boosted regression trees. We validated the model with independent population counts and summarized temporal patterns in terms of spatial resource concentration. We found that spatial resource concentration was highest in late-summer and lowest in winter, with lowest values in winter 2011, the same year an unprecedented cluster of spillover events occurred in Queensland and New South Wales. Spatial resource concentration was positively correlated with El Niño Southern Oscillation at 3–8 month time lags. Based on shared foraging traits with the primary reservoir of Hendra virus (Pteropus alecto), we used our results to develop hypotheses on how regional climatic history, eucalypt phenology, and foraging behaviour may contribute to the predominance of winter spillovers, and how these phenomena connote foraging habitat conservation as a public health intervention.Griffith Sciences, School of Environment and ScienceFull Tex

Climate Change Could Increase the Geographic Extent of Hendra Virus Spillover Risk

Disease risk mapping is important for predicting and mitigating impacts of bat-borne viruses, including Hendra virus (Paramyxoviridae:Henipavirus), that can spillover to domestic animals and thence to humans. We produced two models to estimate areas at potential risk of HeV spillover explained by the climatic suitability for its flying fox reservoir hosts, Pteropus alecto and P. conspicillatus. We included additional climatic variables that might affect spillover risk through other biological processes (such as bat or horse behaviour, plant phenology and bat foraging habitat). Models were fit with a Poisson point process model and a log-Gaussian Cox process. In response to climate change, risk expanded southwards due to an expansion of P. alecto suitable habitat, which increased the number of horses at risk by 175–260% (110,000–165,000). In the northern limits of the current distribution, spillover risk was highly uncertain because of model extrapolation to novel climatic conditions. The extent of areas at risk of spillover from P. conspicillatus was predicted shrink. Due to a likely expansion of P. alecto into these areas, it could replace P. conspicillatus as the main HeV reservoir. We recommend: (1) HeV monitoring in bats, (2) enhancing HeV prevention in horses in areas predicted to be at risk, (3) investigate and develop mitigation strategies for areas that could experience reservoir host replacements