Sentence examples for presence threshold from inspiring English sources

We thresholded raw model output to binary using the least training presence threshold approach that emphasizes full prediction of the ecological niche of the species in question [50].

In all cases, these thresholds identified smaller areas than a lowest presence threshold that yielded zero omission error, thus resulting in more restricted pictures of potential LGM distributions.

By considering that the current distribution of the Corsican Nuthatch was clearly not fulfilling the fundamental niche of the species, we opted for applying a more conservative threshold when producing binary distributions, namely the lowest presence threshold.

We estimated the suitability for P. clarkii under both the current and future climate conditions for each pixel based on the 10th percentile training presence threshold of the MaxEnt output maps.

The realized niche of the Corsican pine was very likely different from its fundamental niche, because of past and ongoing anthropogenic pressures on lowland habitats associated with the development of human societies, and a Lowest Presence Threshold seemed more appropriate too.

We converted the continuous Maxent outputs of relative climatic suitability into binary grid files using two threshold criteria: (1) the generous minimum training presence (MTP) threshold, sometimes termed 'lowest presence threshold;' and (2) the more stringent maximum training sensitivity plus specificity (MTSS) threshold.

The use of a Corsican pine range that was supposed to be closer to the geographic projection of its fundamental niche (using Lower Presence Thresholds) obviously led to current nuthatch distributions being estimated as larger than the actual range, reflecting the potential bioclimatic distribution of the bird in absence of any human extirpation of the pine.

Presence thresholds were determined using the sensitivity-specificity sum maximisation approach [ 33] and the performance of the models were tested using 25% of the occurrence data points to determine the area under the receiver operating characteristic (ROC) curve (AUC).

Based on AUC scores from 10 replicates, models performed well with high discriminatory ability (LGM minimum training presence logistic threshold = 0.079, AUC = 0.920 ± 0.016; LIG minimum training presence logistic threshold = 0.022, AUC = 0.915 ± 0.018).

Having found some clues about the presence of threshold effect, we now focus on a subgroup of 21 studies that used a singular threshold of >5 mm to define test positivity.

Secondly, this study evaluates the presence of threshold nonlinearities.