How to make use of unlabeled observations in species distribution modeling using point process models.

Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity.In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best-performing methods to a dataset of three frog species (Mixophyes).These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.

Validation of presence-only models for conservation planning and the application to whales in a multiple-use marine park.

Identification of species' Biologically Important Areas (BIAs) is fundamental to conservation planning and species distribution models (SDMs) are a powerful tool commonly used to do this. Presence-only data are increasingly being used to develop SDMs to aid the conservation decision-making process. The application of presence-only SDMs for marine species' is particularly attractive due to often logistical and economic costs of obtaining systematic species' distribution data. However, robust model validation is important for conservation management applications that require accurate and reliable species' occurrence data (e.g., spatially explicit risk assessments). This is commonly done using a random subset of the data and less commonly with fully independent test data. Here, we apply a spatial block cross-validation (CV) approach to validate a MaxEnt presence-only model using independent presence/absence survey data for a highly mobile, marine species (humpback whale, Megaptera novaengliae) in the Great Barrier Reef (GBR). A MaxEnt model was developed using opportunistic whale sightings (2003-2007) and then used to identify areas differing in habitat suitability (low, medium, high) to conduct a systematic, line-transect, aerial survey (2012) and derive a density surface model. A spatial block CV buffering strategy was used to validate the MaxEnt model, using the opportunistic sightings as training data and independent aerial survey sightings data as test data. Moderate performance measures indicate MaxEnt was reliable in identifying the distribution patterns of a mobile whale species on their breeding ground, indicated by areas of high density aligned to areas of high habitat suitability. Furthermore, we demonstrate that MaxEnt models can be useful and cost-effective for designing a sampling scheme to undertake systematic surveys that significantly reduces sampling effort. In this study, higher quality information on whale reproductive class (calf vs. non-calf groups) was obtained that the presence-only data lacked, while sampling only 18% of the GBR World Heritage Area. The validation approach using fully independent data provides greater confidence in the MaxEnt model, which indicates significant overlap with the main breeding ground of humpback whales and the inner shipping route. This is important when evaluating presence-only models within certain conservation management applications, such as spatial risk assessments.

Targeting elevated left ventricular end-diastolic pressure following primary percutaneous coronary intervention for ST-segment elevation myocardial infarction - a phase one safety and feasibility study.

INTRODUCTION: Elevated left ventricular end diastolic pressure (LVEDP) is an independent predictor of mortality and heart failure in patients with ST-segment elevation myocardial infarction (STEMI). Whether lowering elevated LVEDP improves outcomes remains unknown. METHODS: This non-randomized, single blinded study with prospective enrolment and sequential group allocation recruited patients undergoing primary percutaneous coronary intervention for STEMI with LVEDP ⩾ 20 mmHg measured immediately after primary percutaneous coronary intervention. The intervention arm (n=10) received furosemide 40 mg intravenous bolus plus escalating doses of glyceryl trinitrate (100 µg per min to a maximum of 1000 µg) during simultaneous measurement of LVEDP. The control group (n=10) received corresponding normal saline boluses with simultaneous measurement of LVEDP (10 readings over 10 min). Efficacy endpoints were final LVEDP achieved, and the dose of glyceryl trinitrate needed to reduce LVEDP by ⩾ 20%. Safety endpoint was symptomatic hypotension (systolic blood pressure < 90 mmHg). RESULTS: From 1 April 2017 to 23 August 2017 we enrolled 20 patients (age: 64±9 years, males: 60%, n=12, anterior STEMI: 65%, n=13). The mean LVEDP for the whole cohort (n=20) was 29±4 mmHg (intervention group: 28±3 mmHg vs. control group: 31±5 mmHg; p=0.1). The LVEDP dropped from 28±3 to 16±2 mmHg in the glyceryl trinitrate + furosemide group (p <0.01) but remained unchanged in the control group. The median dose of glyceryl trinitrate required to produce ⩾ 20% reduction in LVEDP in the intervention group was 200 µg (range: 100-800). One patient experienced asymptomatic decline in systolic blood pressure to below 90 mmHg. There was no correlation between LVEDP and left ventricular ejection fraction. CONCLUSION: The administration of glyceryl trinitrate plus furosemide in patients with elevated LVEDP following primary percutaneous coronary intervention for STEMI safely reduces LVEDP.

Echocardiographic assessment of pulmonary artery systolic pressure following treadmill stress testing.

AIMS: The use of treadmill stress echocardiography (SE) for the diagnosis of nascent pulmonary hypertension (PH) has been hampered by a lack of well-defined, post-exercise pulmonary artery systolic pressure (PASP) values across representative age groups in a normal cohort. METHODS AND RESULTS: Five hundred and eleven subjects (mean age: 53 ±14, 68% female) with normal resting PASP were included in the study. All participants performed treadmill exercise using the Bruce protocol to a high level of perceived exertion. PASP was calculated before and immediately after exercise using Doppler assessment of tricuspid regurgitation. For the cohort, post-exercise PASP was 39 ± 7 mmHg (range: 23-64 mmHg) representing an increase of 11 ± 6 mmHg (44%) from resting values (P < 0.001). The 95th centile values for post-exercise PASP were calculated for the following age cohorts: <30 years; 46 mmHg, 31-50 years; 50 mmHg, 51-70 years; 52 mmHg, >70 years; 53 mmHg. There was a modest independent correlation between post-exercise PASP and (i) increasing age and (ii) resting PASP (r2 = 0.35 and 0.49, respectively, P = 0.01). CONCLUSION: An increase of post-exercise PASP was seen in all patients undergoing SE in this study. Age was directly correlated with post-exercise PASP. Using normative data from healthy controls, treadmill SE-derived post-exercise PASP may be a useful adjunct in the diagnosis of PH.

Incidence, time of occurrence and response to heart failure therapy in patients with anthracycline cardiotoxicity.

BACKGROUND: Anthracyclines are commonly used chemotherapeutic medications. AIM: In the current analysis, we evaluated all-cause mortality and incidence, timing and response to medical therapy of anthracycline cardiotoxicity. METHODS: Left ventricular ejection fraction (LVEF) was serially assessed using gated heart pool scan/echocardiography in patients receiving anthracycline-based chemotherapy from January 2009 to December 2014. RESULTS: A total of 1204 patients was administered anthracyclines during the study period. During a median follow up of 32 (interquartile range: 15-58) months, all-cause mortality was 38% (n = 463), with the incidence of cardiotoxicity 10.2% (n = 123). Only 15.4% (n = 19) patients required heart failure hospitalisation, with 48% (n = 59) of patients commenced on beta blockade therapy and/or angiotensin-converting enzyme inhibitors. The majority of patients (73.2%, n = 90) experienced cardiotoxicity within 1 year of anthracycline initiation. The proportion of patients with complete, partial and no LVEF recovery were 16.3% (n = 20), 29.3% (n = 36) and 54.4% (n = 67) respectively. Mortality was higher in the cardiotoxicity group (49% vs 37%, P < 0.01). History of coronary artery disease, leukaemia, idarubicin use and high cumulative anthracycline dose were predictors of cardiotoxicity. CONCLUSIONS: Cardiotoxicity after anthracycline use predictably occurs within the first year of therapy and is dose-related, with variable degrees of recovery. While the need for hospitalisation for heart failure was uncommon, medical therapy appears underutilised, suggesting there may be a role for improved surveillance and early initiation of treatment.

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.

Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology.

Modeling the spatial distribution of a species is a fundamental problem in ecology. A number of modeling methods have been developed, an extremely popular one being MAXENT, a maximum entropy modeling approach. In this article, we show that MAXENT is equivalent to a Poisson regression model and hence is related to a Poisson point process model, differing only in the intercept term, which is scale-dependent in MAXENT. We illustrate a number of improvements to MAXENT that follow from these relations. In particular, a point process model approach facilitates methods for choosing the appropriate spatial resolution, assessing model adequacy, and choosing the LASSO penalty parameter, all currently unavailable to MAXENT. The equivalence result represents a significant step in the unification of the species distribution modeling literature.