Evaluating Live trapping and Camera-based Indices of Small Mammal Density

2021 ◽  
Author(s):  
Mitchell Alan Parsons ◽  
ALISHIA ORLOFF ◽  
Laura Prugh
Keyword(s):  
Small Mammal ◽  
Abundant Species ◽  
Monitoring Methods ◽  
Density Estimates ◽  
Full Dataset ◽  
Live Trapping ◽  
Minimum Number ◽  
Capture Recapture ◽  
Two Indices ◽  
Best Fit

Density estimates are integral to wildlife management, but they can be costly to obtain. Indices of density may provide efficient alternatives, but calibration is needed to ensure the indices accurately reflect density. We evaluated several indices of small mammal density using live trapping and motion-activated cameras in Washington’s Cascade Mountains. We used linear regression to compare spatially-explicit capture recapture density estimates of mice, voles, and chipmunks to four indices. Two indices were based on live trapping (minimum number alive and number of captures per 100 trap nights) and two indices were based on photos from motion-activated cameras (proportion of cameras detecting a species and the number of independent detections). We evaluated how the accuracy of trap-based indices increased with trapping effort using subsets of the full dataset (n = 7 capture occasions per site). Most indices provided reliable indicators of small mammal density, and live trapping indices (R2=0.64 – 0.98) outperformed camera-based indices (R2=0.24 – 0.86). All indices performed better for more abundant species. The effort required to estimate each index varied, and indices that required more effort performed better. These findings should help managers, conservation practitioners, and researchers select small mammal monitoring methods that best fit their needs.

scholarly journals GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 362
Author(s):  
Arshad Jamal ◽  
Tahir Mahmood ◽  
Muhamad Riaz ◽  
Hassan M. Al-Ahmadi
Keyword(s):  
Real Time ◽  
Control Charts ◽  
Negative Binomial ◽  
Highway Safety ◽  
Monitoring Methods ◽  
Count Data Models ◽  
Crash Data ◽  
Safety Surveillance ◽  
Study Results ◽  
Best Fit

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

Survey and control of small mammal populations on two hardwood plantations in southern Ontario

1974 ◽  
Vol 50 (5) ◽  
pp. 181-185 ◽  
Author(s):  
Andrew Radvanyi
Keyword(s):  
Small Mammal ◽  
Late Summer ◽  
Control Measures ◽  
Effective Control ◽  
Southern Ontario ◽  
Live Trapping ◽  
Surrounding Areas ◽  
And Control ◽  
Rodent Populations ◽  
Ingredient Cost

Live trapping and tagging methods were employed to assess small mammal populations within two hardwood plantations in southern Ontario. Excessive girdling damage in past years to young planted trees necessitated an evaluation of rodent populations and development of effective control measures. The application of an anticoagulant rodenticide to oat groats bait broadcast over the study area at an ingredient cost of approximately three dollars per acre virtually wiped out the small mammals. Reinvasion from surrounding areas was, however, fairly rapid, particularly during late summer. Further research on longer term control measures using poisoned bait feeder stations is recommended.

Estimation Methodology in Contemporary Small Mammal Capture-Recapture Studies

1983 ◽  
Vol 64 (2) ◽  
pp. 253-260 ◽  
Author(s):  
J. D. Nichols ◽  
K. H. Pollock
Keyword(s):  
Small Mammal ◽  
Capture Recapture

scholarly journals Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture-recapture modelling

2021 ◽  
Author(s):  
Soumen Dey ◽  
Richard Bischof ◽  
Pierre P. A. Dupont ◽  
Cyril Milleret
Keyword(s):  
Home Range ◽  
Space Use ◽  
Relative Bias ◽  
Diagnostic Tools ◽  
List Type ◽  
Detection Function ◽  
Density Estimates ◽  
Use Patterns ◽  
Wide Range ◽  
Capture Recapture

AbstractSpatial capture-recapture (SCR) is now used widely to estimate wildlife densities. At the core of SCR models lies the detection function, linking individual detection probability to the distance from its latent activity center. The most common function (half-normal) assumes a bivariate normal space use and consequently detection pattern. This is likely an oversimplification and misrepresentation of real-life animal space use patterns, but studies have reported that density estimates are relatively robust to misspecified detection functions. However, information about consequences of such misspecification on space use parameters (e.g. home range area), as well as diagnostic tools to reveal it are lacking.We simulated SCR data under six different detection functions, including the half-normal, to represent a wide range of space use patterns. We then fit three different SCR models, with the three simplest detection functions (half-normal, exponential and half-normal plateau) to each simulated data set. We evaluated the consequences of misspecification in terms of bias, precision and coverage probability of density and home range area estimates. We also calculated Bayesian p-values with respect to different discrepancy metrics to assess whether these can help identify misspecifications of the detection function.We corroborate previous findings that density estimates are robust to misspecifications of the detection function. However, estimates of home range area are prone to bias when the detection function is misspecified. When fitted with the half-normal model, average relative bias of 95% kernel home range area estimates ranged between −25% and 26% depending on the misspecification. In contrast, the half-normal plateau model (an extension of the half-normal) returned average relative bias that ranged between −26% and −4%. Additionally, we found useful heuristic patterns in Bayesian p-values to diagnose the misspecification in detection function.Our analytical framework and diagnostic tools may help users select a detection function when analyzing empirical data, especially when space use parameters (such as home range area) are of interest. We urge development of additional custom goodness of fit diagnostics for Bayesian SCR models to help practitioners identify a wider range of model misspecifications.

scholarly journals Density Estimates of Unmarked Large Mammals at Camera Traps Vary among Models, Species, and Years, Signalling Importance of Model Assumptions

2021 ◽  
Author(s):  
Jason Fisher ◽  
Joanna Burgar ◽  
Melanie Dickie ◽  
Cole Burton ◽  
Rob Serrouya
Keyword(s):  
Density Estimation ◽  
Black Bear ◽  
Camera Traps ◽  
Mammal Species ◽  
Density Estimates ◽  
Movement Rates ◽  
Social Species ◽  
Marked Variability ◽  
Capture Recapture ◽  
Best Fitting

Density estimation is a key goal in ecology but accurate estimates remain elusive, especially for unmarked animals. Data from camera-trap networks combined with new density estimation models can bridge this gap but recent research has shown marked variability in accuracy, precision, and concordance among estimators. We extend this work by comparing estimates from two different classes of models: unmarked spatial capture-recapture (spatial count, SC) models, and Time In Front of Camera (TIFC) models, a class of random encounter model. We estimated density for four large mammal species with different movement rates, behaviours, and sociality, as these traits directly relate to model assumptions. TIFC density estimates were typically higher than SC model estimates for all species. Black bear TIFC estimates were ~ 10-fold greater than SC estimates. Caribou TIFC estimates were 2-10 fold greater than SC estimates. White-tailed deer TIFC estimates were up to 100-fold greater than SC estimates. Differences of 2-5 fold were common for other species in other years. SC estimates were annually stable except for one social species; TIFC estimates were highly annually variable in some cases and consistent in others. Tests against densities obtained from DNA surveys and aerial surveys also showed variable concordance and divergence. For gregarious animals TIFC may outperform SC due to the latter model’s assumption of independent activity centres. For curious animals likely to investigate camera traps, SC may outperform TIFC, which assumes animal behavior is unaffected by cameras. Unmarked models offer great possibilities, but a pragmatic approach employs multiple estimators where possible, considers the ecological plausibility of assumptions, and uses an informed multi-inference approach to seek estimates from models with assumptions best fitting a species’ biology.

scholarly journals Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models

PLoS ONE ◽  
2020 ◽  
Vol 15 (9) ◽  
pp. e0238870
Author(s):  
Shannon M. Gaukler ◽  
Sean M. Murphy ◽  
Jesse T. Berryhill ◽  
Brent E. Thompson ◽  
Benjamin J. Sutter ◽  
...  
Keyword(s):  
Small Mammal ◽  
Capture Recapture

scholarly journals Hints for possible low redshift oscillation around the best fit ΛCDM model in the expansion history of the universe

2020 ◽  
Author(s):  
L Kazantzidis ◽  
H Koo ◽  
S Nesseris ◽  
L Perivolaropoulos ◽  
A Shafieloo
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Absolute Magnitude ◽  
Density Parameter ◽  
Statistical Fluctuations ◽  
Full Dataset ◽  
Λcdm Model ◽  
History Of ◽  
Best Fit ◽  
The Universe

Abstract We search for possible deviations from the expectations of the concordance ΛCDM model in the expansion history of the Universe by analysing the Pantheon Type Ia Supernovae (SnIa) compilation along with its Monte Carlo simulations using redshift binning. We demonstrate that the redshift binned best fit ΛCDM matter density parameter Ω0m and the best fit effective absolute magnitude $\cal M$ oscillate about their full dataset best fit values with considerably large amplitudes. Using the full covariance matrix of the data taking into account systematic and statistical errors, we show that at the redshifts below z ≈ 0.5 such oscillations can only occur in 4 to 5% of the Monte Carlo simulations. While statistical fluctuations can be responsible for this apparent oscillation, we might have observed a hint for some behaviour beyond the expectations of the concordance model or a possible additional systematic in the data. If this apparent oscillation is not due to statistical or systematic effects, it could be due to either the presence of coherent inhomogeneities at low z or due to oscillations of a quintessence scalar field.

scholarly journals Changes in small mammal assemblage structure across a rain forest/open forest ecotone

1998 ◽  
Vol 14 (2) ◽  
pp. 187-198 ◽  
Author(s):  
STEPHEN E. WILLIAMS ◽  
HELENE MARSH
Keyword(s):  
Species Richness ◽  
Small Mammal ◽  
Rain Forest ◽  
Habitat Heterogeneity ◽  
Large Change ◽  
Open Forest ◽  
Live Trapping ◽  
Sclerophyll Forest ◽  
World Heritage Area ◽  
Forest Ecotone

The effect of the change in vegetation structure from closed rain forest to tall open forest on the small mammal assemblage was studied by live trapping at three sites where the ecotone was very narrow (> 20 m) near the southern end of the Wet Tropics World Heritage Area of Australia. Habitat heterogeneity was significantly higher in the mixed open forest/ecotone area than in the adjacent rain forest. There was a large change in the struture of the small mammal assemblage coincident with the vegetation discontinuity. Although the species richness of small mammals was relatively constant across the gradient, the evenness and diversity of the assemblage declined across the transition from open forest into rain forest and biomass increased, largely due to the high abundance of Rattus fuscipes in the rain forest. The results suggest that the species richness of the small mammal assemblage was not determined by the spatial heterogeneity of the vegetation struture. The species composition of the rain forest is probably related to the historical biogeography of the area whereas the species richness of the wet sclerophyll forest is probably due to a mass-area effect from the adjcant large areas of rain forest and dry sclerophyll forest. However, the evenness, and therefore the diversity of the assemblage, was strongly affected by habitat heterogeneity.

Optimized inversion of borehole temperature data

Geophysics ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 331-336 ◽  
Author(s):  
Gordon R. J. Cooper ◽  
Michael Q. W. Jones
Keyword(s):  
Calculated Data ◽  
Temperature Data ◽  
Rate Of Change ◽  
Absolute Difference ◽  
Minimum Number ◽  
Model Size ◽  
Borehole Temperature ◽  
The Difference ◽  
Best Fit ◽  
Number Of Iterations

A comparison is made between the effectiveness of the inversion of borehole temperature data (for the purpose of climate reconstruction) by the least‐squares (L2) technique and the minimization of the absolute difference between the observed and calculated data (L1) technique. The L1 technique is found to require approximately half the number of iterations to reach the practically achievable minimum error compared to the L2 technique. The choice of which technique to use depends on the statistics of the difference between the observed and calculated data, and it can be advantageous to switch techniques during the inversion process. The inversion damping is also adjusted during the course of the inversion, based on the rate of change of the difference between the observed and calculated data. The aim is to get the best fit of the model to the data while minimising the model size, in the minimum number of iterations. A method of adjusting the damping to achieve this is suggested.

scholarly journals Bringing clarity to the clouded leopard Neofelis diardi: first density estimates from Sumatra

Oryx ◽  
2014 ◽  
Vol 48 (4) ◽  
pp. 536-539 ◽  
Author(s):  
Rahel Sollmann ◽  
Matthew Linkie ◽  
Iding A. Haidir ◽  
David W. Macdonald
Keyword(s):  
Mixed Forest ◽  
Camera Trap ◽  
Forest Loss ◽  
Data Sets ◽  
Panthera Tigris ◽  
Density Estimates ◽  
Clouded Leopard ◽  
Data Limitations ◽  
Capture Recapture ◽  
Study Sites

AbstractWe use data from camera-trap surveys for tigers Panthera tigris in combination with spatial capture–recapture models to provide the first density estimates for the Sunda clouded leopard Neofelis diardi on Sumatra. Surveys took place during 2004–2007 in the Kerinci landscape. Densities were 0.385–1.278 per 100 km2. We found no statistically significant differences in density among four study sites or between primary and mixed forest. Because the data sets are too small to account for differences in detection parameters between sexes, density is probably underestimated. Estimates are comparable to previous estimates of 1–2 per 100 km2 from the lowlands of central Sabah, on Borneo. Data limitations suggest that camera-trap surveys for Sunda clouded leopards require traps spaced more closely, to increase the chance of recaptures at different traps. Nevertheless, these first density estimates for clouded leopards on Sumatra provide a benchmark for measuring future conservation impact on an island that is undergoing rapid forest loss.

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