Biology subjects

Gaul, W. B.

Publications and source records attributed to Gaul, W. B..

4 recordsLinked to original sources

bioRxiv2024

Daily vocalization patterns of the Saipan Reed Warbler (Acrocephalus hiwae)

We investigated how detectability and vocalization patterns of Saipan Reed Warblers (Acrocephalus hiwae) varied by time of day. We used long-duration sound recordings from eleven locations occupied by Saipan Reed Warblers to model the probability of detecting a vocalization in each hour of the day. We found that Saipan Reed Warblers sang during all daylight hours. We did not find evidence of a dawn or evening chorus in this species. These results are useful for determining what time of day surveys of Saipan Reed Warblers should be conducted, which is particularly relevant because Saipan Reed Warblers are protected by local and U.S. Federal laws.

zoology↗

bioRxiv2022

Modelling the distribution of rare invertebrates by correcting class imbalance and spatial bias

AimSoil arthropods are important decomposers and nutrient cyclers, but are poorly represented on national and international conservation Red Lists. Opportunistic biological records for soil invertebrates are often sparse, and contain few observations of rare species but a relatively large number of non-detection observations (a problem known as class imbalance). Robinson et al. (2018) proposed a method for sub-sampling non-detection data using a spatial grid to improve class balance and spatial bias in bird data. For taxa that are less intensively sampled, datasets are smaller, which poses a challenge because under-sampling data removes information. We tested whether spatial under-sampling improved prediction performance of species distribution models for millipedes, for which large datasets are not available. We also tested whether using environmental predictor variables provided additional information beyond what is captured by spatial position for predicting species distributions. LocationIsland of Ireland. MethodsWe tested the spatial under-sampling method of Robinson et al. (2018) by using biological records to train species distribution models of rare millipedes. ResultsUsing spatially under-sampled training data improved species distribution model sensitivity (true positive rate) but decreased model specificity (true negative rate). The decrease in specificity was minimal for rarer species and was accompanied by substantial increases in sensitivity. For common species, specificity decreased more, and sensitivity increased less, making spatial under-sampling most useful for rare species. Geographic coordinates were as good as or better than environmental variables for predicting distributions of two out of six species. Main ConclusionsSpatial under-sampling improved prediction performance of species distribution models for rare soil arthropod species. Spatial under-sampling was most effective for rarer species. The good prediction performance of models using geographic coordinates is promising for modeling distributions of poorly studied species for which little is known about ecological or physiological determinants of occurrence.

ecology↗

bioRxiv2020

Monitoring migratory birds in stopover habitat: assessing the value of extended duration audio recording

O_LIBecause birds are frequently detected by sound, autonomous audio recorders (called automated recording units or ARUs) are now an established tool in addition to in-person observations for monitoring the status and trends of bird populations. ARUs have been evaluated and applied during breeding seasons, and to monitor the nocturnal flight calls of migrating birds. However, birds behave differently during migration stopover than during the breeding season. Here we present a method for using ARUs to monitor land birds in migration stopover habitat. C_LIO_LIWe conducted in-person point counts next to continuously recording ARUs, and compared estimates of the number of species detected and focal species relative abundance from point counts and ARUs. We used a desk-based audio bird survey method for processing audio recordings, which does not require automated species identification algorithms. We tested two methods of using extended duration ARU recording: surveying consecutive minutes, and surveying randomly selected minutes. C_LIO_LIDesk-based surveys using randomly selected minutes from extended duration ARU recordings performed similarly to point counts, and better than desk-based surveys using consecutive minutes from ARU recordings. Surveying randomly selected minutes from ARUs provided estimates of relative abundance that were strongly correlated with estimates from point counts, and successfully showed the increase in abundance associated with migration timing. Randomly selected minutes also provided estimates of the number of species present that were comparable to estimates from point counts. C_LIO_LIARUs are an effective way to track migration timing and intensity in remote or seasonally inaccessible migration stopover habitats. We recommend that desk-based surveys use randomly sampled minutes from extended duration ARU recordings, rather than using consecutive minutes from recordings. Our methods can be immediately applied by researchers with the skills to conduct point counts, with no additional expertise necessary in automated species identification algorithms. C_LI

ecology↗

bioRxiv2020

Data quantity is more important than its spatial bias for predictive species distribution modelling

Biological records are often the data of choice for training predictive species distribution models (SDMs), but spatial sampling bias is pervasive in biological records data at multiple spatial scales and is thought to impair the performance of SDMs. We simulated presences and absences of virtual species as well as the process of recording these species to evaluate the effect on species distribution model prediction performance of 1) spatial bias in training data, 2) sample size (the average number of observations per species), and 3) the choice of species distribution modelling method. Our approach is novel in quantifying and applying real-world spatial sampling biases to simulated data. Spatial bias in training data decreased species distribution model prediction performance, but only when the bias was relatively strong. Sample size and the choice of modelling method were more important than spatial bias in determining the prediction performance of species distribution models.

ecology↗