Using thermal drones to validate historical koala surveys.

It is well known that, due to that inherent differences in their underlying causal mechanisms, different types of crime will have variable impacts on different groups of people. Furthermore, the locations of vulnerable groups of people are highly temporally dynamic. Hence an accurate estimate of the true population at risk in a given place and time is vital for reliable crime rate calculation and hotspot generation. However, the choice of denominator is fraught with difficulty because data describing popular movements, rather than simply residential location, are limited. This research will make use of new ‘crowd-sourced’ data in an attempt to create more accurate estimates of the population at risk for mobile crimes such as street robbery. Importantly, these data are both spatially and temporally referenced and can therefore be used to estimate crime rate significance in both space and time. Spatio-temporal cluster hunting techniques will be used to identify crime hotspots that are significant given the size of the ambient population in the area at the time.

A detailed simulation model was used to examine the unpredictability of the efficacy of aerial applications of Bacillus thuringiensis against spruce budworm, Choristoneura fumiferana (Clemens). The results of new field trials were used to further validate the simulation model. Simulations indicated that mortality caused by B. thuringiensis in the first 48 h after a spray, total mortality, and final defoliation could be predicted by multiple regression (r2 > 0.82) using spray parameters (potency, droplet density, and diameter), budworm population age, density, and mortality from causes other than B. thuringiensis, exposure-period duration, and postspray temperature as predictors. The proportion of budworm larvae that had ingested a lethal dose within 48 h after a spray was a good predictor of total mortality (r2 > 0.91). Variation in temperature contributed little to variation in efficacy measured as population reduction and foliage protection indices. However, sampling error in estimates of population density, final defoliation, and 48-h mortality considerably reduced the predictability of efficacy. Coefficients of determination diminished from r2 > 0.8 with no sampling error to r2 < 0.05 when sampling error was 30% of the sample mean. There was also a pronounced negative bias in estimates of foliage protection and population reduction when sampling error exceeded 20% of the mean. Mismatching control and treated populations with respect to the magnitude of extraneous mortality during the 6th instar caused large biases in efficacy estimates. It is recommended that the use of population reduction and foliage protection indices be avoided as much as possible because of the large effect of sampling error on their values. Rather, the use of well replicated, homogeneous treatment plots carefully matched with nearby untreated controls is recommended. Treatment efficacy can also be assessed with the model of Cooke and Régnière (1996) in combination with sampling aimed at estimating stage-specific survival rates in treated and untreated plots.

A note on mesh selection and sampling efficiency in benthic studies

1. In an experiment conducted with eight pairs of monozygous twin heifer calves aged from 7 to 9 months, it was shown that the mean haemoglobin concentration of those reared on a high plane of nutrition was higher than the corresponding value of those reared on low-plane rations.2. The relative anaemia of the low-plane animals was microcytic but normochromic in type. Its cause must await further investigation, but these features would not be inconsistent with a deficiency of iron.3. For this type of haematological study, monozygous twins proved more efficient than unrelated animals, as shown by the smaller variability within than between pairs.4. Day-to-day variation in the same animal must be considered an important possible source of sampling error.

In addition to the initial thermal conductivity value, the thermal conductivity of closed-cell foamed plastics over long periods of time depends to a large degree on the gas exchange processes between the blowing agent and the ambient air. It is very important to know and to show whether the cell gas remains in the foam for a short time or, to a large extent, for the whole lifetime of the foam material. A lifetime of between c.20 to 50 years is assumed for thermal insulation materials in construction, and it will be shown that blowing agents remain to a large extent in the foam material over such periods. The method most extensively applied for measuring cell-gas composition is to take a sample using a gas-tight syringe and then analyse by gas chromatography. In addition to the measuring method, possible sources of sampling errors and measuring uncertainty are indicated. In order to show the relationship between thermal conductivity and cell-gas composition, thermal conductivity curves for PUR rigid foam materials blown with CFC 11 over a period of 30 years, and PUR foam materials blown with HCFC 141b or pentane over a period of 9 years are presented, with the corresponding measured values for cell-gas composition. Cell-gas measurement continues to be applied as proof of the blowing agent used for consumers and official bodies, and also for compliance with regulations.

A graphic method for quickly estimating an animal population density and the precision of the estimate in field survey is proposed in this paper. The properties of this method are as follows : (1) The density estimation is made only from the frequency of sampling usits having zero individuals and one individual assuming a negative binomial or Poisson distribution as the spatial pattern of the animal; (2) No preliminary survey before the main survey is required; (3) Estimates of the population density and the precision of these estimates are obtained in the field; (4) If required, one can improve the precision by taking more additioanl sampling units immediately in the field; (5) Much time and effort in counting individuals and estimating their density may be saved by using this method.

This paper will critically assess the utility of conventional and novel data sources for building fine-scale spatio-temporal estimates of the ambient population. It begins with a review of data sources employed in existing studies of the ambient population, followed by preliminary analysis to further explore the utility of each dataset. The identification and critiquing of data sources which may be useful for building estimates of the ambient population are novel contributions to the literature. This paper will provide a framework of reference for researchers within urban analytics and other areas where an accurate measurement of the ambient population is required. This work has implications for national and international applications where accurate small area estimates of the ambient population are crucial in the planning and management of urban areas, the development of realistic models and informing policy. This research highlights workday population estimates, in conjunction with footfall camera and Wi-Fi sensors data as potentially valuable for building estimates of the ambient population.

Camera Trap Distance Sampling (CTDS) enables population density estimation of unmarked species from camera trap data by incorporating animal-to-camera distances. In this study, we systematically evaluate the sensitivity of CTDS-derived density estimates to variation in key pipeline parameters, using a high-resolution camera trap dataset from Central Europe. Over a 12-month period, 110 cameras were deployed across a forested area of 94 km 2 . Animal-to-camera distances were estimated via monocular depth estimation. To reduce manual annotation effort, we introduce an automated masking approach for calibration images based on the Segformer model, saving an estimated minimum of two minutes per image. Using red deer ( Cervus elaphus ) as model species, a full-factorial sensitivity study (n = 135 parameter configurations) was conducted, varying MegaDetector confidence threshold, depth sampling method, mean trigger intervals and right-hand distance truncation schemes. Results show that population density estimates are highly sensitive to the choice of depth sampling method, MegaDetector confidence threshold and mean trigger interval. Parameter-induced variation led to differences of up to 200% in population estimates relative to the lowest estimate, twice as much as the variation observed between spatial subsets of the study area differing in vegetation characteristics. However, relative differences between the two sub-areas remained largely consistent across configurations. These results highlight the critical impact of technical choices within CTDS workflows and emphasize the need for transparent reporting and standardization of key pipeline parameters. All CTDS analyses using automated pipelines should therefore be accompanied by dataset-specific sensitivity analyses to account for parameter uncertainty and validation challenges. Overall, our study demonstrates both the methodological challenges and practical applicability of automated CTDS for large-scale wildlife monitoring. • Automated masking reduces manual effort in depth calibration by ∼ 2 min per image. • CTDS parameter variation caused 200% difference, twice our spatial density variation. • Depth sampling, confidence thresholds and trigger intervals affect density estimates. • Automated CTDS requires standardization and dataset-specific sensitivity analyses.

Cognitive impairment is present in approximately 30% of patients with amyotrophic lateral sclerosis (ALS) and, especially when severe, has a negative impact on survival and caregiver burden. Our 2010 meta-analysis...

Surveys of restored oyster reefs need to produce accurate population estimates to assess the efficacy of restoration. Due to the complex structure of subtidal oyster reefs, one effective and efficient means to sample is by patent tongs, rather than SCUBA, dredges, or bottom cores. Restored reefs vary in relief and oyster density, either of which could affect survey efficiency. This study is the first to evaluate gear (the first full grab) and survey (which includes selecting a specific half portion of the first grab for further processing) efficiencies of hand-operated patent tongs as a function of reef height and oyster density on subtidal restoration reefs. In the Great Wicomico River, a tributary of lower Chesapeake Bay, restored reefs of high- and low-relief (25–45 cm, and 8–12 cm, respectively) were constructed throughout the river as the first large-scale oyster sanctuary reef restoration effort (sanctuary acreage > 20 ha at one site) in Chesapeake Bay. We designed a metal frame to guide a non-hydraulic mechanical patent tong repeatedly into the same plot on a restored reef until all oysters within the grab area were captured. Full capture was verified by an underwater remotely-operated vehicle. Samples (n = 19) were taken on nine different reefs, including five low- (n = 8) and four high-relief reefs (n = 11), over a two-year period. The gear efficiency of the patent tong was estimated to be 76% (± 5% standard error), whereas survey efficiency increased to 81% (± 10%) due to processing. Neither efficiency differed significantly between young-of-the-year oysters (spat) and adults, high- and low-relief reefs, or years. As this type of patent tong is a common and cost-effective tool to evaluate oyster restoration projects as well as population density on fished habitat, knowing the gear and survey efficiencies allows for accurate and precise population estimates.

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.

The potential impact of Doppler wind lidar (DWL) observations from a proposed optical autocovariance wind lidar (OAWL) instrument is quantified in observing system simulation experiments (OSSEs). The OAWL design would provide profiles of useful wind vectors along a ground track to the left of the International Space Station (ISS), which is in a 51.6° inclination low-Earth orbit (LEO). These observations are simulated realistically, accounting for cloud and aerosol distributions inferred from the OSSE nature runs (NRs), and measurement and sampling error sources. The impact of the simulated observations is determined in both global and regional OSSE frameworks. The global OSSE uses the ECMWF T511 NR and the NCEP operational Global Data Assimilation System at T382 resolution. The regional OSSE uses an embedded hurricane NR and the NCEP operational HWRF data assimilation system with outer and inner domains of 9- and 3-km resolution, respectively.The global OSSE results show improved analyses and forecasts of tropical winds and extratropical geopotential heights. The tropical wind RMSEs are significantly reduced in the analyses and in short-term forecasts. The tropical wind improvement decays as the forecasts lengthen. The regional OSSEs are limited but show some improvements in hurricane track and intensity forecasts.

A study on Punjab urial ( Ovis vignei punjabiensis) was conducted in Kalabagh Game reserve, during November 2024 to April 2025 to find out its population density and population estimation. Eleven potnetial sites namely; Jaba, Saran, Dheranwali, Rodhan

Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high resolution, gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within EPA’s EnviroAtlas Dasymetric Population Map for conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas Dasymetric Population Map, we found that IDM’s population estimates for U.S Census Bureau blocks improved across all states in CONUS. We also found that IDM performed better in states with larger urban areas than in states that are sparsely populated. Future updates of the Dasymetric Population Map might benefit from stratified (e.g., urban/exurban/rural) multi-state sampling of population density rather than state-specific sampling. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas.

Accurate estimation of population parameters for imperiled wildlife is crucial for effective conservation decision‐making. Population density is commonly used for monitoring imperiled species across space and time, and spatial capture–recapture (SCR) models can produce unbiased density estimates. However, many imperiled species are restricted to fragmented remnant habitats in landscapes severely modified by humans, which can alter animal space use in ways that violate typical SCR model assumptions, possibly cryptically biasing density estimates and misinforming conservation actions. Using data from a two‐year camera‐trapping survey in the Central Pacific Coast region, Mexico, we demonstrate the potential importance to endangered jaguar (Panthera onca) conservation of considering non‐circular home ranges when estimating population density with SCR. Strong evidence existed that jaguars had elliptical home ranges wherein movements primarily occurred along linearly arranged coastal habitats that the camera array aligned with. Accounting for this movement with the SCR anisotropic detection function transformation, density estimates were 30%–32% higher than estimates from standard SCR models that assumed circular home ranges. Given much of suitable jaguar habitat in Mexico is fragmented and linearly oriented along coastlines and mountain ranges, accommodating irregular space use in SCR may be critical for obtaining reliable density estimates to inform effective jaguar conservation.