Journal articles on the topic 'Occupancy Models Detection-nondetection data'
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Rossman, Sam, Charles B. Yackulic, Sarah P. Saunders, Janice Reid, Ray Davis, and Elise F. Zipkin. "DynamicN-occupancy models: estimating demographic rates and local abundance from detection-nondetection data." Ecology 97, no. 12 (December 2016): 3300–3307. http://dx.doi.org/10.1002/ecy.1598.
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Ngo, Chung D., Hai P. Dang, Nghiep T. Hoang, and Binh Van Ngo. "Factors Associated with Detection Probability and Site Occupancy of the Long-Tailed Skink (Eutropis longicaudata) in the Aluoi Area, Central Vietnam." Russian Journal of Herpetology 28, no. 2 (May 3, 2021): 67–72. http://dx.doi.org/10.30906/1026-2296-2021-28-2-67-72.
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Lizard species are rarely detected with perfect accuracy, regardless of the method employed. Nondetection of a species at a site does not necessarily mean the species was absent unless the detection probability was 100%. We assessed the influence of site covariates (less disturbed habitat and disturbed habitat) and sample covariates (temperature, humidity, rainfall) on the occupancy of Eutropis longicaudata in the Aluoi area, central Vietnam. Based on detection/nondetection data over nine visits at 40 less disturbed sites and 39 sites with disturbed habitats, the distribution of E. longicaudata was estimated using site occupancy models. From the best model, we estimated a site occupancy probability of 0.595, a 12.05% increase over the naive occupancy of 0.531 at which E. longicaudata skinks were actually observed. The site covariate of the less disturbed habitat was an important determinant of site occupancy, which was not associated with the variable of disturbance habitats. In the combined AIC model weight, p(precipitation), p(temperature), and p(humidity) have 92%, 36%, and 21% of the total, respectively; providing evidence that environmental conditions (especially precipitation) were important sample covariates in modelling detection probabilities of E. longicaudata. In terms of occupancy probability, the combined weight for the ψ(less disturbed habitat) model and the ψ(disturbed habitat) model were 60% and 32%, respectively. Our results substantiate the importance of incorporating detection and occupancy probabilities into studies of habitat relationships and suggest that the less disturbed habitat associated with weather conditions influence the occupancy of E. longicaudata in central Vietnam.
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Lee, Jooyoung, Jihye Byun, Jaedeok Lim, and Jaeyun Lee. "A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method." Journal of Advanced Transportation 2020 (December 2, 2020): 1–8. http://dx.doi.org/10.1155/2020/8870211.
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High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.
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Holcomb, Jordan M., Colin P. Shea, and Nathan A. Johnson. "Cumulative Spring Discharge and Survey Effort Influence Occupancy and Detection of a Threatened Freshwater Mussel, the Suwannee Moccasinshell." Journal of Fish and Wildlife Management 9, no. 1 (February 2, 2018): 95–105. http://dx.doi.org/10.3996/052017-jfwm-042.
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AbstractFreshwater mussels (Unionidae) are among the most imperiled groups of organisms in the world, and the lack of information regarding species distributions, life-history characteristics, and ecological and biological requirements may limit the protection of remaining mussel populations. We examined the influence of hydrologic factors on the occurrence of the Suwannee Moccasinshell Medionidus walkeri, a federally threatened freshwater mussel species, endemic to the Suwannee River Basin in Georgia and Florida. We also evaluated the influence of survey effort on detection of Suwannee Moccasinshell during field surveys. We compiled all recent (2013–2016) mussel survey records in the Suwannee River Basin. We calculated cumulative discharge contributed by upstream springs for each of 220 survey locations. We combined the spring discharge predictor variable with Suwannee Moccasinshell detection and nondetection data from each survey location to develop a suite of occupancy models. Modeling results indicated that detection of Suwannee Moccasinshell during surveys was strongly and positively related to survey effort. Modeling results also indicated that sites with cumulative spring discharge inputs exceeding ∼28 cubic meters per second were most likely (i.e., predicted occupancy probabilities >0.5) to support Suwannee Moccasinshell populations. However, occupancy declined in the lowermost reaches of the Suwannee mainstem despite high spring discharge inputs, presumably due to greater tidal influences and differences in physicochemical habitat conditions. Historical localities where Suwannee Moccasinshell has presumably been extirpated are all devoid of springs in their upstream watersheds. We hypothesize that springs may buffer extremely tannic, and at times polluted, surface waters, in addition to maintaining adequate flows during periods of drought, thereby promoting the persistence of Suwannee Moccasinshell populations. Our study suggests that springs are a critical resource for Suwannee Moccasinshell and may be more important for conservation planning than was previously recognized.
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Santos, Rodrigo A. L., Mário Mota-Ferreira, Ludmilla M. S. Aguiar, and Fernando Ascensão. "Predicting wildlife road-crossing probability from roadkill data using occupancy-detection models." Science of The Total Environment 642 (November 2018): 629–37. http://dx.doi.org/10.1016/j.scitotenv.2018.06.107.
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Lepard, Clara C., Remington J. Moll, Jonathon D. Cepek, Patrick D. Lorch, Patricia M. Dennis, Terry Robison, and Robert A. Montgomery. "The influence of the delay-period setting on camera-trap data storage, wildlife detections and occupancy models." Wildlife Research 46, no. 1 (2019): 37. http://dx.doi.org/10.1071/wr17181.
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Context The use of camera traps in ecological research has grown exponentially over the past decade, but questions remain about the effect of camera-trap settings on ecological inference. The delay-period setting controls the amount of time that a camera trap is idle between motion-activated triggers. Longer delay periods may potentially extend battery life, reduce data-storage requirements, and shorten data-analysis time. However, they might result in lost data (i.e. missed wildlife detections), which could bias ecological inference and compromise research objectives. Aims We aimed to examine the effect of the delay period on (1) the number of camera-trap triggers, (2) detection and site-occupancy probabilities for eight mammalian species that varied in size, movement rate and commonness and (3) parameter estimates of habitat-based covariates from the occupancy models for these species. Methods We deployed 104 camera traps for 4 months throughout an extensive urban park system in Cleveland, Ohio, USA, using a spatially random design. Using the resultant data, we simulated delay periods ranging from 10s to 60min. For each of these delay periods and for each of our eight focal species, we calculated the number of camera-trap triggers and the parameter estimates of hierarchical Bayesian occupancy models. Key results A simulated increase in the delay period from 10s to 10min decreased the number of triggers by 79.6%, and decreased detection probability and occupancy probability across all species by 1.6% and 4.4% respectively. Further increases in the delay period (i.e. from 10 to 60min) resulted in modest additional reductions in the number of triggers and detection and occupancy probabilities. Variation in the delay period had negligible effects on the qualitative interpretations of habitat-based occupancy models for all eight species. Conclusions Our results suggest that delay-period settings ranging from 5 to 10min can drastically reduce data-storage needs and analysis time without compromising inference resulting from occupancy modelling for a diversity of mammalian species. Implications Broadly, we provide guidance on designing camera-trap studies that optimally trade-off research effort and potential bias, thereby increasing the utility of camera traps as ecological research tools.
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Koshkina, Vira, Yan Wang, Ascelin Gordon, Robert M. Dorazio, Matt White, and Lewi Stone. "Integrated species distribution models: combining presence‐background data and site‐occupancy data with imperfect detection." Methods in Ecology and Evolution 8, no. 4 (April 2017): 420–30. http://dx.doi.org/10.1111/2041-210x.12738.
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Ngo, Binh Van, Ya-Fu Lee, and Chung D. Ngo. "Detection Probability and Site Occupancy of the Granular Spiny Frog (Quasipaa verrucospinosa) in the Tropical Rain Forests of Bach Ma National Park, Central Vietnam." Russian Journal of Herpetology 27, no. 1 (March 21, 2020): 26–32. http://dx.doi.org/10.30906/1026-2296-2020-27-1-26-32.
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Amphibian species are rarely detected with perfect accuracy, regardless of the method employed. A large-scale assessment for Quasipaa verrucospinosa occupancy was conducted at 35 sites in the primary forest and 42 sites in the secondary forest of Bach Ma National Park, central Vietnam. Based on the detection data for each site, the distribution of Q. verrucospinosa was estimated in different habitat types using occupancy models. From the best model among all performed models, we estimated a site occupancy probability of 0.576 that was higher than the naive occupancy estimate of 0.403 and a 43.1% increase over the site proportion at which Q. verrucospinosa was actually observed. The site covariate of the primary forest was an important determinant of site occupancy, which was not associated with the variable of secondary forest. In a combined AIC model weight: the p(temperature), p(humidity), and p(precipitation) models have 47.3, 67.1, and 90.9% of the total, respectively; providing evidence that aforementioned environmental conditions were important sample covariates in modelling detection probabilities of Q. verrucospinosa. Our results substantiate the importance of incorporating detection and occupancy probabilities into studies of habitat relationships and suggest that the primary forests associated with weather conditions influence the site occupancy of Q. verrucospinosa in Bach Ma National Park, central Vietnam.
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Murn, Campbell, and Graham J. Holloway. "Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys." Royal Society Open Science 3, no. 10 (October 2016): 160368. http://dx.doi.org/10.1098/rsos.160368.
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Species occurring at low density can be difficult to detect and if not properly accounted for, imperfect detection will lead to inaccurate estimates of occupancy. Understanding sources of variation in detection probability and how they can be managed is a key part of monitoring. We used sightings data of a low-density and elusive raptor (white-headed vulture Trigonoceps occipitalis ) in areas of known occupancy (breeding territories) in a likelihood-based modelling approach to calculate detection probability and the factors affecting it. Because occupancy was known a priori to be 100%, we fixed the model occupancy parameter to 1.0 and focused on identifying sources of variation in detection probability. Using detection histories from 359 territory visits, we assessed nine covariates in 29 candidate models. The model with the highest support indicated that observer speed during a survey, combined with temporal covariates such as time of year and length of time within a territory, had the highest influence on the detection probability. Averaged detection probability was 0.207 (s.e. 0.033) and based on this the mean number of visits required to determine within 95% confidence that white-headed vultures are absent from a breeding area is 13 (95% CI: 9–20). Topographical and habitat covariates contributed little to the best models and had little effect on detection probability. We highlight that low detection probabilities of some species means that emphasizing habitat covariates could lead to spurious results in occupancy models that do not also incorporate temporal components. While variation in detection probability is complex and influenced by effects at both temporal and spatial scales, temporal covariates can and should be controlled as part of robust survey methods. Our results emphasize the importance of accounting for detection probability in occupancy studies, particularly during presence/absence studies for species such as raptors that are widespread and occur at low densities.
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van Strien, A. J., J. J. A. Dekker, M. Straver, T. van der Meij, L. L. Soldaat, A. Ehrenburg, and E. van Loon. "Occupancy dynamics of wild rabbits (Oryctolagus cuniculus) in the coastal dunes of the Netherlands with imperfect detection." Wildlife Research 38, no. 8 (2011): 717. http://dx.doi.org/10.1071/wr11050.
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Context Wild rabbits are considered a key species in the coastal dunes of the Netherlands, but populations have collapsed as a result of viral diseases. Aim We studied to what extent population collapse led to local extinction and whether recolonisation of empty patches in the dunes happened. Methods We investigated occupancy dynamics using data of 245 transects where rabbits were surveyed in 1984–2009. Dynamic site-occupancy models were used to analyse the data. These models adjust for imperfect detection to avoid bias in occupancy-trend estimation. Key results The decline of the rabbit population has resulted in many local extinctions, especially in woodland and in the northern part of the coastal dunes. Most transects along grassland and mixed vegetation have recently been reoccupied. The recovery of woodland occupancy is slow, probably not because of limited dispersal capacity of rabbits, but because the quality of woodland habitats is poor. Detection probability of rabbits varied considerably over the years and among habitat types, indicating the necessity of taking detection into account. Rabbits were slightly better detected when it was cloudy, windy and rainy and when lunar phase approached new moon. Conclusion Extinction and recolonisation of habitat patches varied considerably among habitat types. Implications The current slow recolonisation hampers the recovery of rabbit populations in woodland habitats in the Dutch coastal dunes. Furthermore, monitoring rabbit occupancy should take imperfect detection into account to avoid biased results.
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Liow, Lee Hsiang. "Simultaneous estimation of occupancy and detection probabilities: an illustration using Cincinnatian brachiopods." Paleobiology 39, no. 2 (2013): 193–213. http://dx.doi.org/10.1666/12009.
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Preservation in the fossil record is never perfect in the sense that we cannot sample all individuals of a given population in time and space. Incomplete detection (i.e., preservation and modern-day sampling of fossils) often affects estimates of other paleobiological parameters of interest, such as occupancy and turnover. Here, I simultaneously model the occupancy and detection probability of taxa, teasing apart the zeros in data that reflect true absences and those that imply non-detection of taxa that were actually present in the space and time of interest. Occupancy modeling, an approach first developed in population ecology, can easily incorporate covariates of interest, such as sampling effort and habitat variables. I use a data set of brachiopod taxa from the Paleozoic to illustrate the utility of this approach for paleontological questions. I demonstrate a range of models, including those that allow colonization between time intervals and those that incorporate facies as site covariates. I also suggest how future data collection can be improved so that process- and sampling-oriented approaches such as occupancy modeling can be applied with ease to paleobiological settings to answer important paleoecological and evolutionary questions.
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Peach, Michelle A., Jonathan B. Cohen, and Jacqueline L. Frair. "Single-visit dynamic occupancy models: an approach to account for imperfect detection with Atlas data." Journal of Applied Ecology 54, no. 6 (May 23, 2017): 2033–42. http://dx.doi.org/10.1111/1365-2664.12925.
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Baker, Leanne F., Kyle J. Artym, and Heidi K. Swanson. "Optimal sampling methods for modelling the occupancy of Arctic grayling (Thymallus arcticus) in the Canadian Barrenlands." Canadian Journal of Fisheries and Aquatic Sciences 74, no. 10 (October 2017): 1564–74. http://dx.doi.org/10.1139/cjfas-2016-0429.
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In occupancy models, imperfect detectability of animals is usually corrected for by using temporally repeated surveys to estimate probability of detection. Substituting spatial replicates for temporal replicates could be an advantageous sampling strategy in remote Arctic regions, but may lead to serious violations of model assumptions. Using a case study of site occupancy of adfluvial young-of-year Arctic grayling (Thymallus arcticus) in Barrenland tundra streams, we assessed the reliability and efficiency of alternative sampling strategies: (i) randomly distributed versus sequential adjacent spatial replicates; (ii) visual versus electrofishing surveys; and (iii) spatial versus temporal replicates. Sequential, adjacent spatial replicates produced spatially autocorrelated data. Autocorrelation was relieved using randomly distributed spatial replicates, but using these randomly distributed spatial replicates introduced significant error into estimates of the probability of occupancy in streams. Models designed for spatially autocorrelated data could minimize this bias. Visual and electrofishing surveys produced comparable probabilities of detection. Spatially replicated surveys performed better than temporal replicates. The easiest and relatively most cost-effective sampling methods performed as well as, or better than, the more established, expensive, and logistically difficult alternatives for occupancy estimation.
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White, Shannon, Evan Faulk, Caleb Tzilkowski, Andrew Weber, Matthew Marshall, and Tyler Wagner. "Predicting fish species richness and habitat relationships using Bayesian hierarchical multispecies occupancy models." Canadian Journal of Fisheries and Aquatic Sciences 77, no. 3 (March 2020): 602–10. http://dx.doi.org/10.1139/cjfas-2019-0125.
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Understanding how stream fishes respond to changes in habitat availability is complicated by low occurrence rates of many species, which in turn reduces the ability to quantify species–habitat relationships and account for imperfect detection in estimates of species richness. Multispecies occupancy models have been used sparingly in the analysis of fisheries data, but address the aforementioned deficiencies by allowing information to be shared among ecologically similar species, thereby enabling species–habitat relationships to be estimated for entire fish communities, including rare species. Here, we highlight the utility of hierarchical multispecies occupancy models for the analysis of fish community data and demonstrate the modeling framework on a stream fish community dataset collected in the Delaware Water Gap National Recreation Area, USA. In particular, we demonstrate the ability of the modeling framework to make inferences at the species-, guild-, and community-levels, thereby making it a powerful tool for understanding and predicting how environmental variables influence species occupancy probabilities and structure fish assemblages.
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Elshaboury, Nehal. "Investigating the occupant existence to reduce energy consumption by using a hybrid artificial neural network with metaheuristic algorithms." Decision Science Letters 11, no. 1 (2022): 91–104. http://dx.doi.org/10.5267/j.dsl.2021.8.001.
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There is an acute need to evaluate the energy consumption of buildings in response to climate change. The “occupant” factor has been largely overlooked in building energy analysis. This research aims at investigating occupancy existence in the office environment using a hybrid artificial neural network with metaheuristic algorithms for improved energy management. It proposes and compares three classification models, namely particle swarm optimization (PSO), gravitational search algorithm (GSA), and hybrid PSO-GSA in combination with the feedforward neural network (FFNN). The inputs to these models are data related to temperature, humidity, light, and carbon dioxide emissions. Two data sets are used for testing the models while the office door is open and closed. The capabilities of the optimized models are evaluated using best, average, median, and standard deviation of the mean squared error. Most of the performance metrics indicate that the FFNN-PSO-GSA model exhibits better performance compared to the other models using the two datasets. The proposed model yields a classification accuracy ranging between 98.47-98.73% using one predictor (i.e., temperature). Besides, it yields an accuracy ranging between 85.45-94.03% using temperature and CO2 predictors. It can be concluded that the FFNN combined with PSO and GSA algorithms can be a useful tool for occupancy detection modeling.
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Thapa, Kanchan, Gokarna Jung Thapa, Damber Bista, Shant Raj Jnawali, Krishna Prasad Acharya, Kapil Khanal, Ram Chandra Kandel, et al. "Landscape variables affecting the Himalayan red panda Ailurus fulgens occupancy in wet season along the mountains in Nepal." PLOS ONE 15, no. 12 (December 11, 2020): e0243450. http://dx.doi.org/10.1371/journal.pone.0243450.
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The Himalayan red panda is an endangered mammal endemic to Eastern Himalayan and South Western China. Data deficiency often hinders understanding of their spatial distribution and habitat use, which is critical for species conservation planning. We used sign surveys covering the entire potential red panda habitat over 22,453 km2 along the mid-hills and high mountains encompassing six conservation complexes in Nepal. To estimate red panda distribution using an occupancy framework, we walked 1,451 km along 446 sampled grid cells out of 4,631 grid cells in the wet season of 2016. We used single-species, single-season models to make inferences regarding covariates influencing detection and occupancy. We estimated the probability of detection and occupancy based on model-averaging techniques and drew predictive maps showing site-specific occupancy estimates. We observed red panda in 213 grid cells and found covariates such as elevation, distance to water sources, and bamboo cover influencing the occupancy. Red panda detection probability p^(SE) estimated at 0.70 (0.02). We estimated red panda site occupancy (sampled grid cells) and landscape occupancy (across the potential habitat) Ψ^(SE) at 0.48 (0.01) and 0.40 (0.02) respectively. The predictive map shows a site-specific variation in the spatial distribution of this arboreal species along the priority red panda conservation complexes. Data on their spatial distribution may serve as a baseline for future studies and are expected to aid in species conservation planning in priority conservation complexes.
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Piédallu, Blaise, Pierre-Yves Quenette, Nicolas Bombillon, Adrienne Gastineau, Christian Miquel, and Olivier Gimenez. "Determinants and patterns of habitat use by the brown bear Ursus arctos in the French Pyrenees revealed by occupancy modelling." Oryx 53, no. 2 (July 10, 2017): 334–43. http://dx.doi.org/10.1017/s0030605317000321.
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AbstractThe Pyrenean brown bear Ursus arctos population in the mountains between France and Spain is one of the smallest and most threatened populations of large carnivores in Europe. We assessed trends in brown bear habitat use in the Pyrenees and investigated the underlying environmental and anthropogenic drivers. Using detection/non-detection data collected during 2008–2014 through non-invasive methods, we developed dynamic occupancy models, accounting for local colonization and extinction processes. We found two non-connected core areas of occupancy, one in the west and the other in the centre of the Pyrenees, with a significant decrease in habitat use overall during 2008–2014. We also found a negative correlation between human density and bear occupancy, in agreement with previous studies on brown bear habitat suitability. Our results confirm the Critically Endangered status of the Pyrenean population of brown bears.
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Cashins, Scott D., Annie Philips, and Lee F. Skerratt. "Using site-occupancy models to prepare for the spread of chytridiomyosis and identify factors affecting detectability of a cryptic susceptible species, the Tasmanian tree frog." Wildlife Research 42, no. 5 (2015): 405. http://dx.doi.org/10.1071/wr14183.
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Context The global reduction of amphibian biodiversity as a result of the disease chytridiomycosis (caused by the fungus Batrachochytrium dendrobatidis; Bd) has highlighted the need to accurately detect local population declines in association with Bd presence. Although Bd has spread globally, some remote regions such as the Tasmanian Wilderness World Heritage Area (1.40 million ha; TWWHA) in Australia, remain largely, but not entirely, disease free. The Tasmanian tree frog (Litoria burrowsae) resides primarily within TWWHA boundaries, and is believed to be susceptible to chytridiomycosis. Aims In the absence of historical abundance data, we used a single-season multi-state site-occupancy model to investigate the impact of Bd on L. burrowsae populations, on factors affecting species detection and to inform ongoing surveillance and conservation. Methods We recorded frog detection and ranked call intensity (estimation of population size) from repeated independent surveys within a season to estimate the role of covariates, such as presence of Bd and environmental variables, on species occupancy and detection probability. Key results Modelling revealed large frog populations are more likely to be present at naturally formed than human-formed ponds, strong winds negatively affect detection of populations, and time after sunset affects detection of large populations. Large frog populations were more likely to be Bd-negative; however, models including Bd presence were not well supported, in part a result of the small number of Bd-positive sites recorded. Conclusions and Implications The utility of site-occupancy modelling in understanding the impact of disease on populations is little known, but has the potential to improve the accuracy and efficiency of many conservation programs.
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Jones, Chris, Susan Bettany, Henrik Moller, David Fletcher, and Justine de Cruz. "Burrow occupancy and productivity at coastal sooty shearwater (Puffinus griseus) breeding colonies, South Island, New Zealand: can mark - recapture be used to estimate burrowscope accuracy?" Wildlife Research 30, no. 4 (2003): 377. http://dx.doi.org/10.1071/wr01050.
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Breeding colonies of sooty shearwaters ('muttonbird', tïtï, Puffinus griseus) on mainland New Zealand have declined in recent years. New data on burrow occupancy and colony productivity for seven sooty shearwater breeding colonies on the coast of Otago, New Zealand for the 1996–97 and 1997–98 breeding seasons are presented and analysed as part of a five-year data set. Detection of a burrow's occupants using a fibre-optic burrowscope may underestimate absolute occupancy rates, but is still of value in the analysis of trends. Detection probabilities estimated by the novel use of mark–recapture models corresponded with those of previous studies of the technique's accuracy. Mainland declines are associated with a lack of control of introduced mammalian predators at most mainland colonies superimposed on a global pattern of decline in the species' abundance. Large numbers of recovered carcasses and an absence of burrow activity at two small mainland colonies show the decline to extinction of these colonies over the five years of collecting data. At one mainland colony with intensive predator control, survival rates and parameter variances are comparable with those found on a predator-free offshore island. All other mainland colonies showed negligible breeding success. There was a significant positive relationship between egg survival and an index of relative adult survival, with an apparent threshold below which few eggs hatch. Adult survival during the breeding season is likely to be the most important parameter in maintaining a colony's viability.
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GMUR, STEPHAN J., DANIEL J. VOGT, KRISTIINA A. VOGT, and ASEP S. SUNTANA. "Effects of different sampling scales and selection criteria on modelling net primary productivity of Indonesian tropical forests." Environmental Conservation 41, no. 2 (October 17, 2013): 187–97. http://dx.doi.org/10.1017/s0376892913000428.
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SUMMARYThe availability of spatial data sourced from either field-derived or satellite-based systems has created new opportunities to estimate and/or monitor changes in carbon sequestration rates, climate change impacts or the potential habitat alterations occurring across large landscapes. However, an effort to create models is not standardized, in part, due to different needs and data sources available for the models. For example, data may have different spatial resolutions with varying degrees of complexity in regards to inputs and statistical methods. This study determines effects of 20, 15, 10, five and one km sampling resolutions on detection of changes in net primary productivity (NPP), occupancy selection criteria for areas to be included in the sample and identification of significant variables impacting NPP in Indonesia forests. Production forest designated for selective harvest was used to define the sampling areas. Variances explained by predictive models were similar across cell sizes although relative importance of variables was different. Partial dependence plots were used to search for potential thresholds or tipping points of NPP change as affected by an independent variable such as minimum daytime temperature. Applying different cell occupancy selection rules significantly changed the overall distribution of NPP values. The magnitude of those changes within a cell size varied with changes in cell size. The mean estimated NPP for production forests across Indonesia differed significantly at every sampling resolution and occupancy selection criteria. Lows ranged from 1.107 to 1.121 kg C m−2yr−1for the 1-km cell size for the three occupancy selection criteria with highs ranging from 1.245 to 1.189 kg C m−2yr−1for the 20-km cell size. The difference in NPP values between these two cell sizes for the three occupancy selection criteria extrapolates to a range in annual biomass of 132 × 106to 66 × 106t for the total area of production forests in Indonesia.
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Zielinski, William J., James A. Baldwin, Richard L. Truex, Jody M. Tucker, and Patricia A. Flebbe. "Estimating Trend in Occupancy for the Southern Sierra Fisher Martes pennanti Population." Journal of Fish and Wildlife Management 4, no. 1 (June 1, 2013): 3–19. http://dx.doi.org/10.3996/012012-jfwm-002.
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Abstract Carnivores are important elements of biodiversity, not only because of their role in transferring energy and nutrients, but also because they influence the structure of the communities where they occur. The fisher Martes pennanti is a mammalian carnivore that is associated with late-successional mixed forests in the Sierra Nevada in California, and is vulnerable to the effects of forest management. As a candidate for endangered species status, it is important to monitor its population to determine whether actions to conserve it are successful. We implemented a monitoring program to estimate change in occupancy of fishers across a 12,240-km2 area in the southern Sierra Nevada. Sample units were about 4 km apart, consisting of six enclosed, baited track-plate stations, and aligned with the national Forest Inventory and Analysis grid. We report here the results of 8 y (2002–2009) of sampling of a core set of 223 sample units. We model the combined effects of probability of detection and occupancy to estimate occupancy, persistence rates, and trend in occupancy. In combined models, we evaluated four forms of detection probability (1-group and 2-group both constant and varying by year) and nine forms of probability of occupancy (differing primarily by how occupancy and persistence vary among years). The best-fitting model assumed constant probability of occupancy, constant persistence, and two detection groups (AIC weight = 0.707). This fit the data best for the entire study area as well as two of the three distinct geographic zones therein. The one zone with a trend parameter found no significant difference from zero for that parameter. This suggests that, over the 8-y period, that there was no trend or statistically significant variations in occupancy. The overall probability of occupancy, adjusted to account for uncertain detection, was 0.367 (SE = 0.033) and estimates were lowest in the southeastern zone (0.261) and highest in the southwestern zone (0.583). Constant and positive persistence values suggested that sample units rarely changed status from occupied to unoccupied or vice versa. The small population of fishers in the southern Sierra (probably <250 individuals) does not appear to be decreasing. However, given the habitat degradation that has occurred in forests of the region, we favor continued monitoring to determine whether fisher occupancy increases as land managers implement measures to restore conditions favorable to fishers.
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Chakraborty, Pranamesh, Yaw Okyere Adu-Gyamfi, Subhadipto Poddar, Vesal Ahsani, Anuj Sharma, and Soumik Sarkar. "Traffic Congestion Detection from Camera Images using Deep Convolution Neural Networks." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 11, 2018): 222–31. http://dx.doi.org/10.1177/0361198118777631.
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Recent improvements in machine vision algorithms have led to closed-circuit television (CCTV) cameras emerging as an important data source for determining of the state of traffic congestion. In this study we used two different deep learning techniques, you only look once (YOLO) and deep convolution neural network (DCNN), to detect traffic congestion from camera images. The support vector machine (SVM), a shallow algorithm, was also used as a comparison to determine the improvements obtained using deep learning algorithms. Occupancy data from nearby radar sensors were used to label congested images in the dataset and for training the models. YOLO and DCCN achieved 91.5% and 90.2% accuracy, respectively, whereas SVM’s accuracy was 85.2%. Receiver operating characteristic curves were used to determine the sensitivity of the models with regard to different camera configurations, light conditions, and so forth. Although poor camera conditions at night affected the accuracy of the models, the areas under the curve from the deep models were found to be greater than 0.9 for all conditions. This shows that the models can perform well in challenging conditions as well.
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Parise, Alec, Miguel A. Manso-Callejo, Hung Cao, and Monica Wachowicz. "Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors." AGILE: GIScience Series 2 (June 4, 2021): 1–13. http://dx.doi.org/10.5194/agile-giss-2-9-2021.
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Abstract. The Internet of Things is a multi-sensor technology with the unique advantage of supporting non-intrusive and non-device occupancy detection, while also allowing us to explore new forecasting occupancy models. However, forecasting occupancy presence is not a trivial task, since it is still unknown the main criteria in selecting a forecasting modelling approach according to a non-intrusive sensing strategy. Towards this challenge, this paper proposes an analytical workflow developed to support the Prophet model for forecasting occupancy presence in indoor spaces throughout the tasks of sensing, processing, and analysing event triggered data generated from ten non-intrusive sensors, including motion, temperature, luminosity, CO2, TVOC, sound, pressure, accelerometer, gyroscope, and humidity sensors. The usefulness of this analytical workflow is demonstrated with the implementation of an IoT platform for an experiment operating non-intrusive sensing in a classroom. The assessment is made at different time intervals and the results confirm that there is a relationship between the event-count and occupancy presence in such a way that the larger the number of events triggered in an indoor space, the higher the probability of an indoor space being occupied.
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Ishak, Sherif S., and Haitham M. Al-Deek. "Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (January 1998): 56–63. http://dx.doi.org/10.3141/1634-07.
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Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.
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Stolen, Eric D., Donna M. Oddy, Mike L. Legare, David R. Breininger, Shanon L. Gann, Stephanie A. Legare, Stephanie K. Weiss, Karen G. Holloway-Adkins, and Ron Schaub. "Preventing Tracking-Tube False Detections in Occupancy Modeling of Southeastern Beach Mouse." Journal of Fish and Wildlife Management 5, no. 2 (August 1, 2014): 270–81. http://dx.doi.org/10.3996/032014-jfwm-025.
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Abstract Quantifying habitat occupancy of the southeastern beach mouse Peromyscus polionotus niveiventris is important for managing this threatened species throughout its limited range. Tracking tubes were used to detect the southeastern beach mouse in coastal areas on the federal lands of the Kennedy Space Center, Cape Canaveral Air Force Station, and Canaveral National Seashore. Because this method relied on observations of footprints, detections of beach mice were confounded by the co-occurrence of cotton mice Peromyscus gossypinus, which have wider but slightly overlapping footprint widths. Mice of both species were captured and footprinted using tracking tubes to collect a database of footprints of known identity. These data were used to develop a Bayesian hierarchical model of the cutoff width at which a print could be assigned as a beach mouse with a known probability of error. Specifically, within the model, observed footprint widths were used to estimate a mean and variance of footprint width for each species, while accounting for variation between individual mice. Then, a distribution of new footprint widths was generated for each species by drawing from their modeled distributions. Finally, the new footprints were compared with a range of potential cutoff widths to evaluate the proportion of times the correct decision to exclude or accept the footprint was made. We graphically evaluated the performance of the cutoff widths and chose one that traded off between reducing false positives and retaining more correct detections for use in occupancy models. We explored the use of the cutoff width using occupancy models that allow for false-positive detections, and found that the use of the cutoff performed as expected. Over 40% of primary dune habitat on the Kennedy Space Center was occupied by beach mice during the period sampled. The proportion of vegetated habitat at a site had a negative influence on detection probability. No ecological covariates had a measurable influence on beach mouse occupancy, probably due to the limited range of environmental variation in the sampled region. The use of a cutoff for footprint width resulted in a reliable method to deal with false-positive detections in tracking tubes with small mammals and allowed the use of occupancy models that rely on certain detection.
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Freeman, Mary C., Megan M. Hagler, Phillip M. Bumpers, Kit Wheeler, Seth J. Wenger, and Byron J. Freeman. "Long-Term Monitoring Data Provide Evidence of Declining Species Richness in a River Valued for Biodiversity Conservation." Journal of Fish and Wildlife Management 8, no. 2 (August 1, 2017): 418–34. http://dx.doi.org/10.3996/122016-jfwm-090.
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Abstract Free-flowing river segments provide refuges for many imperiled aquatic biota that have been extirpated elsewhere in their native ranges. These biodiversity refuges are also foci of conservation concerns because species persisting within isolated habitat fragments may be particularly vulnerable to local environmental change. We have analyzed long-term (14- and 20-y) survey data to assess evidence of fish species declines in two southeastern U.S. rivers where managers and stakeholders have identified potentially detrimental impacts of current and future land uses. The Conasauga River (Georgia and Tennessee) and the Etowah River (Georgia) form free-flowing headwaters of the extensively dammed Coosa River system. These rivers are valued in part because they harbor multiple species of conservation concern, including three federally endangered and two federally threatened fishes. We used data sets comprising annual surveys for fish species at multiple, fixed sites located at river shoals to analyze occupancy dynamics and temporal changes in species richness. Our analyses incorporated repeated site-specific surveys in some years to estimate and account for incomplete species detection, and test for species-specific (rarity, mainstem-restriction) and year-specific (elevated frequencies of low- or high-flow days) covariates on occupancy dynamics. In the Conasauga River, analysis of 26 species at 13 sites showed evidence of temporal declines in colonization rates for nearly all taxa, accompanied by declining species richness. Four taxa (including one federally endangered species) had reduced occupancy across the Conasauga study sites, with three of these taxa apparently absent for at least the last 5 y of the study. In contrast, a similar fauna of 28 taxa at 10 sites in the Etowah River showed no trends in species persistence, colonization, or occupancy. None of the tested covariates showed strong effects on persistence or colonization rates in either river. Previous studies and observations identified contaminants, nutrient loading, or changes in benthic habitat as possible causes for fish species declines in the Conasauga River. Our analysis provides baseline information that could be used to assess effectiveness of future management actions in the Conasauga or Etowah rivers, and illustrates the use of dynamic occupancy models to evaluate evidence of faunal decline from time-series data.
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Beasley, Emily M., and Sean P. Maher. "Small mammal community composition varies among Ozark glades." Journal of Mammalogy 100, no. 6 (December 19, 2019): 1774–82. http://dx.doi.org/10.1093/jmammal/gyz102.
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Abstract Island biogeography theory (IBT) explains and estimates large-scale ecological patterns among islands and isolated habitat patches. Specifically, IBT predicts that the number of species per habitat patch differs as a function of area and isolation as a result of local colonization and extinction. Accurate estimates of species richness are essential for testing predictions of IBT, but differences in detectability of species can lead to bias in empirical data. Hierarchical community models correct for imperfect detection by leveraging information from across the community to estimate species-specific occupancy and detection probabilities. Using the fragmented Ozark glades as our model system, we constructed a hierarchical community model to 1) estimate site-level and regional species richness of small mammals while correcting for detection error, and 2) determine environmental covariates driving occupancy. We sampled 16 glades in southwestern Missouri in summer 2016–2017 and quantified mammal community structure within the glade network. The detected species pool included eight species, and the model yielded a regional species estimate of 8.6 species, with a mean of 3.47 species per glade. Species richness increased with patch area but not isolation, and effects of patch shape varied between species in the community.
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Majidzadeh Gorjani, Ojan, Radek Byrtus, Jakub Dohnal, Petr Bilik, Jiri Koziorek, and Radek Martinek. "Human Activity Classification Using Multilayer Perceptron." Sensors 21, no. 18 (September 16, 2021): 6207. http://dx.doi.org/10.3390/s21186207.
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The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.
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Ribeiro, José W., Kristopher Harmon, Gabriel Augusto Leite, Tomaz Nascimento de Melo, Jack LeBien, and Marconi Campos-Cerqueira. "Passive Acoustic Monitoring as a Tool to Investigate the Spatial Distribution of Invasive Alien Species." Remote Sensing 14, no. 18 (September 13, 2022): 4565. http://dx.doi.org/10.3390/rs14184565.
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Invasive alien species (IAS) are a threat to biodiversity and ecosystem function worldwide. Unfortunately, researchers, agencies, and other management groups face the unresolved challenge of effectively detecting and monitoring IAS at large spatial and temporal scales. To improve the detection of soniferous IAS, we introduced a pipeline for large-scale passive acoustic monitoring (PAM). Our main goal was to illustrate how PAM can be used to rapidly provide baseline information on soniferous IAS. To that aim, we collected acoustic data across Puerto Rico from March to June 2021 and used single-species occupancy models to investigate species distribution of species in the archipelago and to assess the peak of vocal activity. Overall, we detected 16 IAS (10 birds, 3 mammals, and 3 frogs) and 79 native species in an extensive data set with 1,773,287 1-min recordings. Avian activity peaked early in the morning (between 5 a.m. and 7 a.m.), while amphibians peaked between 1 a.m. and 5 a.m. Occupancy probability for IAS in Puerto Rico ranged from 0.002 to 0.67. In general, elevation and forest cover older than 54 years were negatively associated with IAS occupancy, corroborating our expectation that IAS occurrence is related to high levels of human disturbance and present higher occupancy probabilities in places characterized by more intense human activities. The work presented here demonstrates that PAM is a workable solution for monitoring vocally active IAS over a large area and provides a reproducible workflow that can be extended to allow for continued monitoring over longer timeframes.
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Abdel-Razek, Shahira Assem, Hanaa Salem Marie, Ali Alshehri, and Omar M. Elzeki. "Energy Efficiency through the Implementation of an AI Model to Predict Room Occupancy Based on Thermal Comfort Parameters." Sustainability 14, no. 13 (June 24, 2022): 7734. http://dx.doi.org/10.3390/su14137734.
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Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.
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Guzy, Jacquelyn C., Steven J. Price, and Michael E. Dorcas. "Using multiple methods to assess detection probabilities of riparian-zone anurans: implications for monitoring." Wildlife Research 41, no. 3 (2014): 243. http://dx.doi.org/10.1071/wr14038.
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Context Both manual call surveys (MCS) and visual encounter surveys (VES) are popular methods used to monitor anuran populations. Recent statistical developments, specifically the development of occupancy models that permit the use of data from various survey methods to assess method-specific detection probabilities, provide a rigorous framework for evaluating the effectiveness of field methods. Aim To compare species-specific detection probabilities generated by MCS and VES and to evaluate the effectiveness of these methods throughout the activity season of several riparian-zone anuran species. Methods During 2010 and 2011, we sampled 21 sites along the Broad and Pacolet Rivers, in South Carolina, USA, using MCS and VES. Anuran species were surveyed across three seasons (fall, spring and summer) each year. Key results For six species, MCS resulted in a higher mean probability of detection, whereas VES resulted in a higher mean probability of detection for four species. In addition, survey date was an important influence on detection probability of most anurans when using MCS, but largely unimportant when employing VES. Conclusions Our findings indicated that VES are as effective as MCS for detecting some species of anurans, and for others, VES represent a more effective method. Furthermore, when using VES outside the breeding window, some anurans can be reliably detected, and in some cases, detected more easily than by using MCS. Implications We suggest that VES is a complimentary technique to MCS and a potentially important tool for population monitoring of anurans. VES can provide more flexibility for anuran researchers, as robust estimates of detection and occupancy can be obtained outside a narrow breeding window.
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Applegate, Roger D., Robert E. Kissell, E. Daniel Moss, Edward L. Warr, and Michael L. Kennedy. "Problems with Avian Point Counts for Estimating Density of Northern Bobwhite—A Case Study." Journal of Fish and Wildlife Management 2, no. 1 (June 1, 2011): 117–21. http://dx.doi.org/10.3996/092010-jfwm-033.
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Abstract Point count data are used increasingly to provide density estimates of bird species. A favored approach to analyze point count data uses distance sampling theory where model selection and model fit are important considerations. We used uniform and half normal models and assessed model fit using χ2 analysis. We were unsuccessful in fitting models to 635 northern bobwhite Colinus virginianus observations from 85 avian point locations spanning 6 y (P ≤ 0.05). Most observations (74%) occurred in the outermost (>100-m) distance radius. Our results violated the assumptions that all observations at the point are detected. The assumption that birds were assigned to the correct distance interval also was probably violated. We caution managers in implementing avian point counts with distance sampling when estimating northern bobwhite population density. We recommend exploring other approaches such as occupancy-estimation and modeling for estimating detection probabilities.
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Chen, Chao, Jaimyoung Kwon, John Rice, Alexander Skabardonis, and Pravin Varaiya. "Detecting Errors and Imputing Missing Data for Single-Loop Surveillance Systems." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 160–67. http://dx.doi.org/10.3141/1855-20.
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Single-loop detectors provide the most abundant source of traffic data in California, but loop data samples are often missing or invalid. A method is described that detects bad data samples and imputes missing or bad samples to form a complete grid of clean data, in real time. The diagnostics algorithm and the imputation algorithm that implement this method are operational on 14,871 loops in six districts of the California Department of Transportation. The diagnostics algorithm detects bad (malfunctioning) single-loop detectors from their volume and occupancy measurements. Its novelty is its use of time series of many samples, instead of basing decisions on single samples, as in previous approaches. The imputation algorithm models the relationship between neighboring loops as linear and uses linear regression to estimate the value of missing or bad samples. This gives a better estimate than previous methods because it uses historical data to learn how pairs of neighboring loops behave. Detection of bad loops and imputation of loop data are important because they allow algorithms that use loop data to perform analysis without requiring them to compensate for missing or incorrect data samples.
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Rudershausen, Paul J., Jeffery H. Merrell, and Jeffrey A. Buckel. "Factors Influencing Colonization and Survival of Juvenile Blue Crabs Callinectes sapidus in Southeastern U.S. Tidal Creeks." Diversity 13, no. 10 (October 7, 2021): 491. http://dx.doi.org/10.3390/d13100491.
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Tidal creeks along the southeastern U.S. and Gulf of Mexico coastlines provide nursery habitats for commercially and ecologically important nekton, including juvenile blue crabs Callinectes sapidus, a valuable and heavily landed seafood species. Instream and watershed urbanization may influence the habitat value that tidal creeks provide to blue crabs. We investigated natural and anthropogenic factors influencing juvenile blue crab occupancy dynamics in eight first-order tidal creeks in coastal North Carolina (USA). An auto-logistic hierarchical multi-season (dynamic) occupancy model with separate ecological and observation sub-models was fitted to juvenile blue crab presence/absence data collected over replicate sampling visits in multiple seasons at three fixed trapping sites in each creek. Colonization and survival are the processes operating on occupancy that are estimated with this formulation of the model. Covariates considered in the ecological sub-model included watershed imperviousness, the percent of salt marsh in each creek’s high tide area, percent salt marsh edge, site-level water depth, and site-level salinity. Temperature, salinity, and dissolved oxygen were covariates considered in the observation sub-model. In the ecological sub-model, watershed imperviousness was a meaningful negative covariate and site-level salinity was a positive covariate of survival probability. Imperviousness and salinity were each marginally meaningful on colonization probability. Water temperature was a positive covariate of detection probability in the observation sub-model. Mean estimated detection probability across all sites and seasons of the study was 0.186. The results suggest that development in tidal creek watersheds will impact occupancy dynamics of juvenile blue crabs. This places an emphasis on minimizing losses of natural land cover classes in tidal creek watersheds to reduce the negative impacts to populations of this important species. Future research should explore the relationship between imperviousness and salinity fluctuations in tidal creeks to better understand how changing land cover influences water chemistry and ultimately the demographics of juvenile blue crabs.
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Acquaah, Yaa Takyiwaa, Balakrishna Gokaraju, Raymond C. Tesiero, and Gregory H. Monty. "Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy." Remote Sensing 13, no. 19 (September 26, 2021): 3847. http://dx.doi.org/10.3390/rs13193847.
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The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.
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Schirmer, Pascal A., Iosif Mporas, and Akbar Sheikh-Akbari. "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors." Energies 13, no. 9 (May 1, 2020): 2148. http://dx.doi.org/10.3390/en13092148.
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A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.
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Hutchinson, Rebecca, Li-Ping Liu, and Thomas Dietterich. "Incorporating Boosted Regression Trees into Ecological Latent Variable Models." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1343–48. http://dx.doi.org/10.1609/aaai.v25i1.7801.
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Important ecological phenomena are often observed indirectly. Consequently, probabilistic latent variable models provide an important tool, because they can include explicit models of the ecological phenomenon of interest and the process by which it is observed. However, existing latent variable methods rely on hand-formulated parametric models, which are expensive to design and require extensive preprocessing of the data. Nonparametric methods (such as regression trees) automate these decisions and produce highly accurate models. However, existing tree methods learn direct mappings from inputs to outputs — they cannot be applied to latent variable models. This paper describes a methodology for integrating nonparametric tree methods into probabilistic latent variable models by extending functional gradient boosting. The approach is presented in the context of occupancy-detection (OD) modeling, where the goal is to model the distribution of a species from imperfect detections. Experiments on 12 real and 3 synthetic bird species compare standard and tree-boosted OD models (latent variable models) with standard and tree-boosted logistic regression models (without latent structure). All methods perform similarly when predicting the observed variables, but the OD models learn better representations of the latent process. Most importantly, tree-boosted OD models learn the best latent representations when nonlinearities and interactions are present.
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Timokhin, Stanislav, Mohammad Sadrani, and Constantinos Antoniou. "Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data." Smart Cities 3, no. 3 (August 6, 2020): 818–41. http://dx.doi.org/10.3390/smartcities3030042.
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Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising.
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Corral-De-Witt, Danilo, Sabbir Ahmed, Faroq Awin, José Luis Rojo-Álvarez, and Kemal Tepe. "An Accurate Probabilistic Model for TVWS Identification." Applied Sciences 9, no. 20 (October 10, 2019): 4232. http://dx.doi.org/10.3390/app9204232.
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Television White Spaces (TVWS)-based cognitive radio systems can improve spectrum efficiency by facilitating opportunistic usage of television broadcasting spectrum by secondary users without interfering with primary users. Previously applied models introduce missed detection errors, giving a limited estimation of the spectrum occupancy, which does not correspond to the reality of its usage, hence resulting in a partial waste of this resource. Considering jointly parameters like false alarm probability and detection probability, this article proposes a probabilistic model that can identify TVWS with improved accuracy. The proposed model considers energy detection criteria, combined with simultaneous sensing of the noise and of the signal from primary users. In order to demonstrate the model effectiveness, a low-cost Mobile Spectrum Sensing Station prototype was designed, implemented, and subsequently mounted on a vehicle. More than eight million spatio-temporally variant data samples were collected by scanning the UHF-TV spectrum of 500–698 MHz in the city of Windsor, ON, Canada. Analysis of the collected data showed that the proposed model achieves an accuracy improvement of about 9.6% compared to existing models, demonstrating that TVWS vary with spatial displacement and increasing significantly in the rural areas. Even in the most crowded spectrum zone, about 28% of the channels are identified as TVWS, and this number increases to a maximum of 60% in less crowded regions in urban areas. We conclude that the proposed model improves the TVWS detection compared with other used models, and also that the elements considered in this research contribute to reduce the complexity of the mathematical calculations while maintaining the accuracy. A low-cost open-source sensing station has been designed and tested, which represents an operative and useful data source in this setting.
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FERRER-PARIS, JOSÉ R., and ADA SÁNCHEZ-MERCADO. "Making inferences about non-detection observations to improve occurrence predictions in Venezuelan Psittacidae." Bird Conservation International 30, no. 3 (January 22, 2020): 406–22. http://dx.doi.org/10.1017/s0959270919000522.
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SummaryThe global decline in psittacid populations highlights the need for monitoring programmes that allow us to estimate the level of confidence that can be placed in a non-detection observation in order to assess changes in range status. We used the detection/non-detection records for 26 psittacid species detected during the first national bird monitoring programme in Venezuela carried out in 2010 by the Neotropical Biodiversity Mapping Initiative. We fitted occupancy models and evaluate the suitability of the data to explain the lack of detections given the current sampling effort, and the expected occurrence probabilities due to environmental conditions (conditional probability of occurrence; ΨCONDL). We were able to fit reliable models for 13 of the 26 species detected. For Green-rumped Parrotlet Forpus passerinus, Blue-headed Parrot Pionus menstrus, and Orange-winged Amazon Amazona amazonica, the probability of detection (p) under the current sampling effort was too low (> 0.2) in areas where environmental conditions would imply high ΨCONDL (< 0.3). This suggests that sampling effort should be increased to generate reliable estimations of occurrence. In contrast, for Scarlet Macaw Ara macao, Yellow-crowned Amazon Amazona ochrocephala, Orange-chinned Parakeet Brotogeris jugularis and Brown-throated Parakeet Eupsittula pertinax the model estimated high p (< 0.3) and low ΨCONDL (> 0.2), suggesting that the species are reliably detected and better models could be obtained by including other predictive variables related to temporal use of resources and habitat heterogeneity. To improve the effectiveness of parrot monitoring programme in Neotropical countries, we suggest increasing the sampling effort, developing several surveys per year, and including variables related with temporal use of resources and habitat heterogeneity.
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Rouchier, Simon. "Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification." Energies 15, no. 10 (May 11, 2022): 3534. http://dx.doi.org/10.3390/en15103534.
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A Bayesian data analysis workflow offers great advantages to the process of measurement and verification, including the estimation of savings uncertainty regardless of the chosen numerical model. However, it is still rarely used in practice, perhaps because practitioners are less familiar with the required tools. The present work documents a Bayesian methodology for the assessment of energy savings at the scale of a whole facility, following an energy-conservation measure. The first model, an energy signature commonly used in practice, demonstrates the steps of the Bayesian workflow and illustrates its advantages. The posterior distributions obtained by training this first model are used as prior distributions for a second, more complex model. This so-called “hidden Markov energy signature” model combines the energy signature with a hidden Markov model at an hourly resolution, and allows detection of occupancy. It has a large number of parameters and would likely not be identifiable without the Bayesian workflow. The results illustrate the advantages of the Bayesian methodology for measurement and verification: a probabilistic description of all variables, including predictions of energy use and savings; the applicability to any model structure; the ability to include prior knowledge to facilitate training complex models. Savings are estimated by the new hidden Markov energy-signature model with a much lower uncertainty than with a lower-resolution model. The highlights of the paper are twofold: it serves as a tutorial on Bayesian inference for measurement and verification; it also proposes a new flexible model structure for hourly prediction of energy use and occupancy detection.
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Bush, Alex, Wendy A. Monk, Zacchaeus G. Compson, Daniel L. Peters, Teresita M. Porter, Shadi Shokralla, Michael T. G. Wright, Mehrdad Hajibabaei, and Donald J. Baird. "DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness." Proceedings of the National Academy of Sciences 117, no. 15 (March 26, 2020): 8539–45. http://dx.doi.org/10.1073/pnas.1918741117.
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The complexity and natural variability of ecosystems present a challenge for reliable detection of change due to anthropogenic influences. This issue is exacerbated by necessary trade-offs that reduce the quality and resolution of survey data for assessments at large scales. The Peace–Athabasca Delta (PAD) is a large inland wetland complex in northern Alberta, Canada. Despite its geographic isolation, the PAD is threatened by encroachment of oil sands mining in the Athabasca watershed and hydroelectric dams in the Peace watershed. Methods capable of reliably detecting changes in ecosystem health are needed to evaluate and manage risks. Between 2011 and 2016, aquatic macroinvertebrates were sampled across a gradient of wetland flood frequency, applying both microscope-based morphological identification and DNA metabarcoding. By using multispecies occupancy models, we demonstrate that DNA metabarcoding detected a much broader range of taxa and more taxa per sample compared to traditional morphological identification and was essential to identifying significant responses to flood and thermal regimes. We show that family-level occupancy masks high variation among genera and quantify the bias of barcoding primers on the probability of detection in a natural community. Interestingly, patterns of community assembly were nearly random, suggesting a strong role of stochasticity in the dynamics of the metacommunity. This variability seriously compromises effective monitoring at local scales but also reflects resilience to hydrological and thermal variability. Nevertheless, simulations showed the greater efficiency of metabarcoding, particularly at a finer taxonomic resolution, provided the statistical power needed to detect change at the landscape scale.
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O'Brien, Timothy G., and Margaret F. Kinnaird. "A picture is worth a thousand words: the application of camera trapping to the study of birds." Bird Conservation International 18, S1 (August 7, 2008): S144—S162. http://dx.doi.org/10.1017/s0959270908000348.
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AbstractThis study reviews the use of remotely triggered still cameras, known as camera traps, in bird research and suggests new methods useful for analyzing camera trap data. Camera trapping may be most appropriate for large, ground-dwelling birds, such as cracids and pheasants. Recent applications include documentation of occurrence of rare species and new species records, nest predation studies and behavioural studies including nest defence, frugivory, seed dispersal, and activity budgets. If bird postures are analyzed, it may be possible to develop behavioural time budgets. If birds are marked or individually identifiable, abundance may be estimated through capture-recapture methods typically used for mammals. We discourage use of relative abundance indices based on trapping effort because of the difficulty of standardizing surveys over time and space. Using the Great Argus Pheasant Argus argusianus, a cryptic, terrestrial, forest bird as an example, we illustrate applications of occupancy analysis to estimate proportion of occupied habitat and finite mixture models to estimate abundance when individual identification is not possible. These analyses are useful because they incorporate detection probabilities < 1 and covariates that affect the sample site or the observation process. Results are from camera trap surveys in the 3,568 km2 Bukit Barisan Selatan National Park, Indonesia. We confirmed that Great Argus Pheasants prefer primary forest below 500 m. We also find a decline in occupancy (6–8% yr−1). Point estimates of abundance peak in 2000, followed by a sharp decline. We discuss the effects of rarity, detection probability and sampling effort on accuracy and precision of estimates.
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Daines, Tanner J., Grant G. Schultz, and Gregory S. Macfarlane. "Evaluating Real Time Ramp Meter Queue Length and Wait Time Estimation." Future Transportation 2, no. 4 (October 1, 2022): 807–27. http://dx.doi.org/10.3390/futuretransp2040045.
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Models to predict ramp meter queue length from traffic detector data are potentially useful tools in improving traffic operations and safety. Existing research, however, has been based on microscopic simulation or relied on extensive calibration of Kalman filter and related models to produce reliable queue length estimates. This research seeks to develop methodologies for improving and simplifying the calibration process of existing queue length models by applying loop detector data including volume, occupancy, and the metering rate data for ramp meters along I-15 in Utah. A conservation model and several variations of a Kalman filter model generated estimated queues that were compared to observed queue lengths in 60 s bins. A modified Kalman filter model and a new heuristic model derived from cluster analysis—the models that yielded the best results—provided queue length estimates that were generally within approximately eight vehicles of the observed queue length. Using the ramp metering rate, the queue length estimates were converted into wait times that were generally within approximately 30 s of the actual wait time, producing a viable method to predict wait time from up-to-the-minute traffic detection information with relatively little required calibration. The implementation of the ramp meter queue length and wait time estimation algorithms presented in this research will allow departments of transportation to better assess freeway and ramp conditions, which can then aid in reducing congestion throughout the freeway network.
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Monneret, René-Jean, René Ruffinoni, David Parish, David Pinaud, and Marc Kéry. "The Peregrine population study in the French Jura mountains 1964–2016: use of occupancy modeling to estimate population size and analyze site persistence and colonization rates." Ornis Hungarica 26, no. 2 (December 1, 2018): 69–90. http://dx.doi.org/10.1515/orhu-2018-0016.
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Abstract We summarize key results of the first 53 years of one of the longest-running avian population studies in the world, on the Peregrine Falcon (Falco peregrinus), in the French Jura mountains (12,714 km2), launched in 1964. A total of 449 cliff sites in 338 potential Peregrine territories were surveyed: 287 (85%) of these territories were occupied by an adult pair at least once, while in 51 (15%) we never detected an adult pair. Most sites were visited several times during a breeding season to survey occupancy and later fecundity, but the proportion of sites visited was highly variable over the years. We highlight the power of the Bayesian implementation of site-occupancy models (MacKenzie et al. 2002, 2003) to analyze data from raptor population studies: to correct population size estimates for sites not visited in a given year and for the biasing effects of preferential sampling (when better sites are more likely to be checked). In addition, these models allow estimation and modeling of the site-level persistence and colonization rates, which can provide important clues about drivers of population dynamics, even without individually marking any birds. Changes in the dynamics rates may serve as early-warning signals for subsequent population declines. Since 1964, the observed number of adult pairs varied between 17 in 1972 and 196 in 2008, but the proportion of sites visited increased from 43% in 1964 to 80–90% after 2002. Hence, this raw population total must be an underestimate. We found strong evidence for preferential sampling in our study. Correcting for this, we estimated 56 pairs in 1964, after which the population dropped to a minimum of 18 in 1972, but then recovered rapidly, leveling off somewhat around 1995 and reaching a maximum of 200–210 adult pairs during 2000–2012. This was then followed by a decline to 170–190 pairs. In any one year, the raw counts underestimated the true population size by 5–39% (mean 11%), due to sites not being visited (this correction ignores imperfect detection though). Site persistence rates declined from 78% to less than 60% during 1967–1972, and then increased rapidly to over 90% during 1980–1990, suggesting that once pesticide effects vanished, individual survival probability increased rapidly and as a consequence also site persistence. Since the 1990s, persistence has declined slowly, which may indicate decreasing adult survival. In contrast, colonization rates increased steadily from about 3% in the early years to maxima of 46–49% during 1994–2001, but declined thereafter and currently reach about 33%. Taller cliffs had greater persistence and colonization rates than medium or small cliffs. Both the decline in colonization and in persistence rates during the last 15 years may reflect density-dependence, predation by the expanding European Eagle Owl (Bubo bubo) population, human persecution or any as yet unknown factors. Importantly, we note that both persistence and colonization rates began to decline many years before the recent population decline became apparent. Thus, analysis of population studies using dynamic occupancy models can provide early-warning signals for future population declines. Our study demonstrates the benefits of modern analytical methods that can correct for several key deficiencies in probably all raptor population studies: incomplete coverage of sites and imperfect detection (though we only dealt with the former here). Occupancy models, possibly accounting for preferential sampling, appear to represent the logical analytical framework for abundance in raptor population studies.
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Harris, Julianne E., Gregory S. Silver, Jeffrey C. Jolley, R. D. Nelle, and Timothy A. Whitesel. "A Stepwise Approach to Assess the Occupancy State of Larval Lampreys in Streams." Journal of Fish and Wildlife Management 11, no. 1 (October 1, 2019): 226–37. http://dx.doi.org/10.3996/112018-jfwm-107.
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Abstract Pacific Lamprey Entosphenus tridentatus is an ecologically and culturally important anadromous species of conservation concern for which fisheries managers use information on occupancy state in streams to assess species status and inform stream management decisions. Here we developed a stepwise approach that incorporates the potential for nondetection and a preselected expected maximum probability of stream occupancy if field crews do not document larval Pacific Lamprey during sampling. Our approach includes seven steps: define the occupancy question; select the maximum acceptable probability of occupancy, if the species is not documented during sampling; define an assumed detection probability for the target organism; calculate required sampling effort; select sampling units; conduct sampling; and interpret sampling results into probabilistic occupancy conclusions. We examined detection probability of our approach for larval lamprey using data from multiple occupied streams in the Pacific Northwest. We illustrated our approach by evaluating Balm Grove Dam as a barrier to Pacific Lamprey migration in Gales Creek, Oregon. Bayesian estimates of detection probability in occupied streams ranged from 0.15 to 0.94, with an overall median of 0.70 (95% credible interval: 0.60–0.79). Assuming detection probability is at least 0.15 (i.e., lowest estimate), 19 reaches are required for the expected maximum probability of occupancy to be not more than 0.05, if the species is not documented through our sampling approach. Although detected downstream, we detected no larvae upstream of Balm Grove Dam; thus, we conclude that the maximum probability of occupancy upstream of Balm Grove Dam was not more than 0.05 at an assumed detection probability of 0.4, suggesting the dam as a barrier to adult migration. We provide an occupancy assessment tool with standardized sampling requirements that incorporates the potential for nondetection and the flexibility to select an expected maximum probability of occupancy if researchers document no larvae, to aid management and restoration in a single stream.
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Hamedani, Kian, Lingjia Liu, Shiya Liu, Haibo He, and Yang Yi. "Deep Spiking Delayed Feedback Reservoirs and Its Application in Spectrum Sensing of MIMO-OFDM Dynamic Spectrum Sharing." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 1292–99. http://dx.doi.org/10.1609/aaai.v34i02.5484.
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In this paper, we introduce a deep spiking delayed feedback reservoir (DFR) model to combine DFR with spiking neuros: DFRs are a new type of recurrent neural networks (RNNs) that are able to capture the temporal correlations in time series while spiking neurons are energy-efficient and biologically plausible neurons models. The introduced deep spiking DFR model is energy-efficient and has the capability of analyzing time series signals. The corresponding field programmable gate arrays (FPGA)-based hardware implementation of such deep spiking DFR model is introduced and the underlying energy-efficiency and recourse utilization are evaluated. Various spike encoding schemes are explored and the optimal spike encoding scheme to analyze the time series has been identified. To be specific, we evaluate the performance of the introduced model using the spectrum occupancy time series data in MIMO-OFDM based cognitive radio (CR) in dynamic spectrum sharing (DSS) networks. In a MIMO-OFDM DSS system, available spectrum is very scarce and efficient utilization of spectrum is very essential. To improve the spectrum efficiency, the first step is to identify the frequency bands that are not utilized by the existing users so that a secondary user (SU) can use them for transmission. Due to the channel correlation as well as users' activities, there is a significant temporal correlation in the spectrum occupancy behavior of the frequency bands in different time slots. The introduced deep spiking DFR model is used to capture the temporal correlation of the spectrum occupancy time series and predict the idle/busy subcarriers in future time slots for potential spectrum access. Evaluation results suggest that our introduced model achieves higher area under curve (AUC) in the receiver operating characteristic (ROC) curve compared with the traditional energy detection-based strategies and the learning-based support vector machines (SVMs).
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Zambanini, Sebastian, Ana-Maria Loghin, Norbert Pfeifer, Elena Màrmol Soley, and Robert Sablatnig. "Detection of Parking Cars in Stereo Satellite Images." Remote Sensing 12, no. 13 (July 7, 2020): 2170. http://dx.doi.org/10.3390/rs12132170.
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In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details to make individual cars recognizable and the time interval between the stereo shots allows to reason about the moving or static condition of a car. Consequently, we describe a complete processing pipeline where raw satellite images are georeferenced, ortho-rectified, equipped with a digital surface model and an inclusion layer generated from Open Street Model vector data, and finally analyzed for parking cars by means of an adapted Faster R-CNN oriented bounding box detector. As a test site for the proposed approach, a new publicly available dataset of the city of Barcelona labeled with parking cars is presented. On this dataset, a Faster R-CNN model directly trained on the two ortho-rectified stereo images achieves an average precision of 0.65 for parking car detection. Finally, an extensive empirical and analytical evaluation shows the validity of our idea, as parking space occupancy can be broadly derived in fully visible areas, whereas moving cars are efficiently ruled out. Our evaluation also includes an in-depth analysis of the stereo occlusion problem in view of our application scenario as well as the suitability of using a reconstructed Digital Surface Model (DSM) as additional data modality for car detection. While an additional adoption of the DSM in our pipeline does not provide a beneficial cue for the detection task, the stereo images provide essentially two views of the dynamic scene at different timestamps. Therefore, for future studies, we recommend a satellite image acquisition geometry with smaller incidence angles, to decrease occlusions by buildings and thus improve the results with respect to completeness.
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Dostine, P. L., S. J. Reynolds, A. D. Griffiths, and G. R. Gillespie. "Factors influencing detection probabilities of frogs in the monsoonal tropics of northern Australia: implications for the design of monitoring studies." Wildlife Research 40, no. 5 (2013): 393. http://dx.doi.org/10.1071/wr13057.
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Context Failure to acknowledge potential bias from imperfect detection of cryptic organisms such as frogs may compromise survey and monitoring programmes targeting these species. Aims The aims of the present study were to identify proximate factors influencing detection probabilities of a range of frog species in monsoonal northern Australia, and to estimate the number of repeat censuses required at a site to have confidence that non-detected species are absent. Methods Data on detection or non-detection of frog species based on calling individuals were recorded during 10 wet-season censuses of 29 survey sites in the Darwin region. Factors influencing detection probabilities were identified using occupancy models; model selection was based on the Akaike information criterion. Sampling effort for individual species was calculated using model predictions at different stages of the wet season. Key results The covariate water temperature featured in the best-supported models for 7 of the 14 frog species. Six of these species were more likely to be detected when water temperatures were below 30°C. Detection probabilities were also correlated with the number of days since the commencement of the wet season, time since last significant rainfall, air temperature and time after sunset. Required sampling effort for individual species varied throughout the wet season. For example, a minimum of two repeat censuses was required for detection of Litoria caerulea in the early wet season, but this number increased to 13 in the middle stage of the wet season. Conclusions Variability in environmental conditions throughout the wet season leads to variability in detection probabilities of frog species in northern Australia. Lower water temperatures, mediated by rainfall immediately before or during surveys, enhances detectability of a range of species. For most species, three repeat surveys under conditions resulting in a high detection probability are sufficient to determine presence at a site. Implications Survey and monitoring programmes for frogs in tropical northern Australia will benefit from the results of the present study by allowing targeting of conditions of high detection probability for individual species, and by incorporating sufficient repeat censuses to provide accurate assessment of the status of individual species at a site.
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Schank, Cody J., Michael V. Cove, Marcella J. Kelly, Clayton K. Nielsen, Georgina O’Farrill, Ninon Meyer, Christopher A. Jordan, et al. "A Sensitivity Analysis of the Application of Integrated Species Distribution Models to Mobile Species: A Case Study with the Endangered Baird’s Tapir." Environmental Conservation 46, no. 03 (July 19, 2019): 184–92. http://dx.doi.org/10.1017/s0376892919000055.
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SummarySpecies distribution models (SDMs) are statistical tools used to develop continuous predictions of species occurrence. ‘Integrated SDMs’ (ISDMs) are an elaboration of this approach with potential advantages that allow for the dual use of opportunistically collected presence-only data and site-occupancy data from planned surveys. These models also account for survey bias and imperfect detection through the use of a hierarchical modelling framework that separately estimates the species–environment response and detection process. This is particularly helpful for conservation applications and predictions for rare species, where data are often limited and prediction errors may have significant management consequences. Despite this potential importance, ISDMs remain largely untested under a variety of scenarios. We performed an exploration of key modelling decisions and assumptions on an ISDM using the endangered Baird’s tapir (Tapirus bairdii) as a test species. We found that site area had the strongest effect on the magnitude of population estimates and underlying intensity surface and was driven by estimates of model intercepts. Selecting a site area that accounted for the individual movements of the species within an average home range led to population estimates that coincided with expert estimates. ISDMs that do not account for the individual movements of species will likely lead to less accurate estimates of species intensity (number of individuals per unit area) and thus overall population estimates. This bias could be severe and highly detrimental to conservation actions if uninformed ISDMs are used to estimate global populations of threatened and data-deficient species, particularly those that lack natural history and movement information. However, the ISDM was consistently the most accurate model compared to other approaches, which demonstrates the importance of this new modelling framework and the ability to combine opportunistic data with systematic survey data. Thus, we recommend researchers use ISDMs with conservative movement information when estimating population sizes of rare and data-deficient species. ISDMs could be improved by using a similar parameterization to spatial capture–recapture models that explicitly incorporate animal movement as a model parameter, which would further remove the need for spatial subsampling prior to implementation.