N-mixture Modeling Research Articles

Camera Trap Methods and Drone Thermal Surveillance Provide Reliable, Comparable Density Estimates of Large, Free-Ranging Ungulates.

Camera traps and drone surveys both leverage advancing technologies to study dynamic wildlife populations with little disturbance. Both techniques entail strengths and weaknesses, and common camera trap methods can be confounded by unrealistic assumptions and prerequisite conditions. We compared three methods to estimate the population density of white-tailed deer (Odocoileus virgnianus) in a section of Pilot Mountain State Park, NC, USA: (1) camera trapping using mark-resight ratios or (2) N-mixture modeling and (3) aerial thermal videography from a drone platform. All three methods yielded similar density estimates, suggesting that they converged on an accurate estimate. We also included environmental covariates in the N-mixture modeling to explore spatial habitat use, and we fit models for each season to understand temporal changes in population density. Deer occurred in greater densities on warmer, south-facing slopes in the autumn and winter and on cooler north-facing slopes and in areas with flatter terrain in the summer. Seasonal density estimates over two years suggested an annual cycle of higher densities in autumn and winter than in summer, indicating that the region may function as a refuge during the hunting season.

Distribution, Abundance, and Status of Barbour's Map Turtle (Graptemys barbouri) in Florida

We collected information to determine the status of Barbour's map turtle (Graptemys barbouri) in Florida by conducting standardized surveys of occupied watersheds in 2014–2015, including the Choctawhatchee, Chipola, Apalachicola, and Ochlockonee rivers. Single-pass basking surveys were used to determine the distribution and relative abundance of G. barbouri. Multiple-pass basking surveys and N-mixture modeling were used in sections of the Apalachicola and Ochlockonee rivers to estimate detectability and abundance of map turtles. Observed counts were compared with available information from previous surveys to make inferences about population change. A total of 5917 G. barbouri observations were recorded during single-pass surveys along 502 river kilometers (rkm) and we expanded the documented range by 76 rkm. Further, we used multiple-pass surveys to estimate 2079 G. barbouri along six 2-km sections (12 km total) of the Apalachicola River (173.3 turtles/rkm) and 292 G. barbouri along five 5-km sections (25 km total) of the Ochlockonee River (11.7 turtles/rkm). The present study is the most comprehensive distributional survey for G. barbouri in Florida to date. The species was documented for the first time in 2 counties and observed counts were consistently higher than in previous surveys. Overall, the Florida population appears secure with evidence of expansion in both the Choctawhatchee and Ochlockonee drainages.

Ontogenetic shifts in mesohabitat use of young-of-year Rio Grande blue sucker in the big bend region of the Rio Grande

Alteration of flow regimes by anthropogenic activities is one of the primary environmental problems in riverine systems. Understanding how hydrologic conditions can affect ontogenetic habitat shifts of imperiled fishes is important in order to develop conservation and management strategies for each life-history stage. We examined relationships between the abundance of young-of-the-year (YOY) Rio Grande Blue Sucker and various abiotic variables in the Trans-Pecos region of the Rio Grande in Texas, USA. We used open N-mixture modeling to better understand the factors affecting ontogenetic habitat shifts of the imperiled aridland river fish. In addition, we examined differences in Rio Grande Blue Sucker total length among three mesohabitat types (pool, riffle, and run). The results of open N-mixture modeling suggested that as pool area increased, the abundance of YOY Rio Grande Blue Sucker increased. Total length of YOY Rio Grande Blue Sucker also significantly differed among the three mesohabitat types. The total lengths of YOY Rio Grande Blue Sucker in pool habitats were lower than in other mesohabitats, suggesting that YOY Rio Grande Blue Sucker undergo ontogenetic habitat shifts into greater current velocity habitats as they grow. The habitat associations we documented support the growing body of research emphasizing the importance of maintaining sufficient and appropriately timed flows to avoid prolonged low flows that limit habitat availability for native fish species during sensitive life stages in the Rio Grande and other aridland rivers.

Elevation and landscape change drive the distribution of a montane, endemic grassland bird.

ContextTropical montane habitats support high biodiversity and are hotspots of endemism, with grasslands being integral components of many such landscapes. The montane grasslands of the Western Ghats have seen extensive land‐use change over anthropogenic timescales. The factors influencing the ability of grassland‐dependent species to persist in habitats experiencing loss and fragmentation, particularly in montane grasslands, are poorly known.ObjectivesWe studied the relationship between the Nilgiri pipit Anthus nilghiriensis, a threatened endemic bird that typifies these montane grasslands, and its habitat, across most of its global distribution. We examined what habitat features make remnant grasslands viable, which is necessary for their effective management.MethodsWe conducted 663 surveys in 170 sites and used both single‐season occupancy modeling and N‐mixture modeling to account for processes influencing detection, presence, and abundance.ResultsElevation had a positive influence on species presence, patch size had a moderate positive influence, and patch isolation had a moderate negative influence. Species abundance was positively influenced by elevation and characteristics related to habitat structure, and negatively influenced by the presence of invasive woody vegetation.ConclusionsThe strong effect of elevation on the highly range‐restricted Nilgiri pipit is likely to make it vulnerable to climate change. This highly range‐restricted species is locally extinct at several locations, and persists at low densities in remnants of its habitat left by recent fragmentation. Our findings indicate a need to control and reverse the spread of exotic woody invasives to preserve the grasslands themselves and the specialist species dependent upon them.

New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys.

Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N‐mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N‐mixture model and (b) a modified Horvitz–Thompson (H‐T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry‐assisted ground surveys.The modified H‐T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (n = 138 detected objects), a particularly strong result given the difficulty in attaining accuracy found with previous methods.The results suggested that N‐mixture models in their current form may not be the most appropriate approach to estimating the abundance of wildlife detected in RPAS surveys with automated detection, and accurate estimates could be made with approaches that account for spurious detections.

Temporal patterns of three sympatric pheasant species in the Nanling Mountains: N-mixture modeling applied to detect abundance

BackgroundThe reliability of long-term population estimates is crucial for conservation and management purposes. Most previous studies assume that count indices are proportionally related to abundance; however, this assumption may not hold when detection varies spatially and temporally. We examined seasonal variations in abundance of three bird species (Cabot’s Tragopan Tragopan caboti, Silver Pheasant Lophura nycthemera, and White-necklaced Partridge Arborophila gingica) along an elevational gradient, using N-mixture models that take into account imperfect detection in our bird data.MethodsCamera-trapping was used to monitor temporal activity patterns of these species at Guangdong Nanling National Nature Reserve from December 2013 to November 2017 (4 seasons per year). For abundance analysis (N-mixture modeling), we divided a year into 4 seasons, i.e. 3 months per season, and performed the analysis by season. Elevation was incorporated into the N-mixture model as a covariate that may affect abundance. We compared the N-mixture model with a null model (no covariate model) and selected the better model based on AIC values to make an inference.ResultsFrom 24 sampling sites, we obtained 6786 photographs of 8482 individuals of 44 bird species and 26 mammal species. Silver Pheasant was photographed much more frequently and showed higher temporal activity frequency than White-necklaced Partridge or Cabot’s Tragopan. Silver Pheasant was camera-captured most frequently in summer, and other two species in winters. All three species had two daytime activity peaks: between 6:00 a.m. and 10:00 a.m., and between 5:00 p.m. and 7:00 p.m., respectively. Our estimated abundance and detection probability from the N-mixture model were variable by season. In particular, all three species showed greater abundance in summer than in winter, and estimated abundance patterns of all three species were more similar with observed camera-trapping counts in summers. Moreover, in winter, elevation had a positive impact on abundance of Silver Pheasant and Cabot’s Tragopan, but not on White-necklaced Partridge.ConclusionsOur results demonstrate that the N-mixture model performed well in the estimation of temporal population abundance at local fixed permanent plots in mountain habitat in southern China, based on the modeling of repeated camera-trapping counts. The seasonal differences in abundance of the three endemic bird species and the strong effect of elevation on abundance of two species in winter were only indicative of variations in spatio-temporal distribution within species and between species. In identifying suitable habitat for endemic pheasants, the positive elevational effect also suggests that more attention should be paid to conservation of areas with higher elevation in the Nanling Mountains.

Abundance and habitat associations of two Florida crayfishes, Procambarus paeninsulanus (Faxon, 1914) and P. fallax (Hagen, 1870) (Decapoda: Astacoidea), assessed with N-mixture modeling

Abundance and habitat associations of two Florida crayfishes, Procambarus paeninsulanus (Faxon, 1914) and P. fallax (Hagen, 1870) (Decapoda: Astacoidea), assessed with N-mixture modeling

Dynamic N-occupancy models: estimating demographic rates and local abundance from detection-nondetection data.

Occupancy modeling is a widely used analytical technique for assessing species distributions and range dynamics. However, occupancy analyses frequently ignore variation in abundance of occupied sites, even though site abundances affect many of the parameters being estimated (e.g., extinction, colonization, detection probability). We introduce a new model ("dynamic N-occupancy") capable of providing accurate estimates of local abundance, population gains (reproduction/immigration), and apparent survival probabilities while accounting for imperfect detection using only detection/nondetection data. Our model utilizes heterogeneity in detection based on variations in site abundances to estimate latent demographic rates via a dynamic N-mixture modeling framework. We validate our model using simulations across a wide range of values and examine the data requirements, including the number of years and survey sites needed, for unbiased and precise estimation of parameters. We apply our model to estimate spatiotemporal heterogeneity in abundances of barred owls (Strix varia) within a recently invaded region in Oregon (USA). Estimates of apparent survival and population gains are consistent with those from a nearby radio-tracking study and elucidate how barred owl abundances have increased dramatically over time. The dynamic N-occupancy model greatly improves inferences on individual-level population processes from occupancy data by explicitly modeling the latent population structure.

Estimating the Effects of Habitat and Biological Interactions in an Avian Community.

We used repeated sightings of individual birds encountered in community-level surveys to investigate the relative roles of habitat and biological interactions in determining the distribution and abundance of each species. To analyze these data, we developed a multispecies N-mixture model that allowed estimation of both positive and negative correlations between abundances of different species while also estimating the effects of habitat and the effects of errors in detection of each species. Using a combination of single- and multispecies N-mixture modeling, we examined for each species whether our measures of habitat were sufficient to account for the variation in encounter histories of individual birds or whether other habitat variables or interactions with other species needed to be considered. In the community that we studied, habitat appeared to be more influential than biological interactions in determining the distribution and abundance of most avian species. Our results lend support to the hypothesis that abundances of forest specialists are negatively affected by forest fragmentation. Our results also suggest that many species were associated with particular types of vegetation as measured by structural attributes of the forests. The abundances of 6 of the 73 species observed in our study were strongly correlated. These species included large birds (American Crow (Corvus brachyrhynchos) and Red-winged Blackbird (Agelaius phoeniceus)) that forage on the ground in open habitats and small birds (Red-eyed Vireo (Vireo olivaceus), House Wren (Troglodytes aedon), Hooded Warbler (Setophaga citrina), and Prairie Warbler (Setophaga discolor)) that are associated with dense shrub cover. Species abundances were positively correlated within each size group and negatively correlated between groups. Except for the American Crow, which preys on eggs and nestlings of small song birds, none of the other 5 species is known to display direct interactions, so we suspect that the correlations may have been associated with species-specific responses to habitat components not adequately measured by our covariates.

Coping with heterogeneity to detect species on a large scale: N-mixture modeling applied to red-legged partridge abundance

The reliability of long-term population estimates is crucial for conservation and management purposes. Most game species population monitoring programs assume that count indices are proportionally related to abundance. However, this assumption is untenable when detection varies spatially and temporally. We assessed whether N-mixture models, which allow detection modeling using spatially and temporally repeated count data, were relevant for monitoring the population of red-legged partridge (Alectoris rufa). Unbiased estimates are needed for calculating hunting quotas for this game species. We used the simple aural playback point-count method and adatasetof 121 point-transects(n ¼16,288 counts) collected from 1992 to 2010. Covariates such as date, hour, wind, rain, and vegetation density influenced detection probability. Estimated abundance ranged from 0 to 15 males per point and exhibited variable coefficients of variation depending on sites (range: 0.14-1.31, mean: 0.44). We found a positive and linear relationship between the abundance estimated by the N-mixture model and the densities provided by 2 other counting methods, square sampling and kilometric abundance index, but not with blank beat. We established a maximum detection radius for the playback surveys, which enabled conversion of abundance at the points into density. The N-mixture modeling approach is more cost-effective for game species monitoring than capture-recapture methods or square sampling, and more reliable than indices of relative abundances. 2014 The Wildlife Society.

Accounting for incomplete detection: What are we estimating and how might it affect long-term passerine monitoring programs?

A primary objective of ecological monitoring programs typically includes the efficient detection of population trends. Passerines as a group are important ecological indicators and are often included in such programs to provide information on multiple species with a single survey technique. However, commonly used field and analytical approaches may not provide appropriate inference or sensitivity due to assumption violations and differences in the proportion of the population exposed to sampling. Recent methodological developments utilizing repeated point counts and an N-mixture modeling approach for analysis may produce more consistent and interpretable estimates applicable to the superpopulation of individuals using a site during the breeding season. These estimates should be more useful for monitoring because they are not conditioned on presence or availability as are most single-visit approaches. We used repeated count data collected in Denali National Park and Preserve, Alaska (Denali) between 1995 and 2009 from 12 common passerine species to assess variation in presence and availability throughout the season, estimate trends in superpopulation abundance, and provide recommendations for the design of future monitoring programs. We found that variation in detection due to presence and availability was large and differed among species. After accounting for these sources of variation, we estimated abundance of Wilson’s warblers (Wilsonia pusilla) had declined by approximately 48% and fox sparrow (Passerella iliaca) abundance had increased by approximately 250% over 15years. Combined, our results suggest that if trend estimation is a priority, passerine monitoring programs should formally address all components of the detection process, including the probabilities of presence and availability.