It is clear that the above can lead to confusion when scientists of different countries are trying to communicate with each other. Another example is the burrowing rodent called a gopher found throughout the western United States. In the southeastern United States the term gopher refers to a burrowing turtle very similar to the desert tortoise found in the American southwest. One final example; two North American mammals known as the elk and the caribou are known in Europe as the reindeer and the elk. We never sing “Rudolph the Red-nosed elk”! Confused? This was the reason for creating an internationally recognized system of naming organisms. To avoid confusion, living organisms are assigned a scientific name based on Latin or Latinized words. The English sparrow is Passer domesticus or Passer domesticus (italics or underlining these two names is the official written representation of a scientific name). Using a uniform naming system allows scientists from all over the world to recognize exactly which life form a scientist is referring to. The naming process is called the binomial system of nomenclature. Passer is comparable to a surname and is called the genus, while domesticus is the specific or species name (like your given name) of the English sparrow. Now scientists can give all sparrow-like birds the genus Passer but the species name will vary. All similar genera (plural for genus) can be grouped into another, “higher” category (see below). Study the following for a more through understanding of taxonomy. Taxonomy Analogy Kingdom: Animalia Country

Diversity of life

Summary
Reliable assessment of animal populations is a long-standing challenge in wildlife ecology. Technological advances have led to widespread adoption of camera traps (CTs) to survey wildlife distribution, abundance and behaviour. As for any wildlife survey method, camera trapping must contend with sources of sampling error such as imperfect detection. Early applications focused on density estimation of naturally marked species, but there is growing interest in broad-scale CT surveys of unmarked populations and communities. Nevertheless, inferences based on detection indices are controversial, and the suitability of alternatives such as occupancy estimation is debatable.
We reviewed 266 CT studies published between 2008 and 2013. We recorded study objectives and methodologies, evaluating the consistency of CT protocols and sampling designs, the extent to which CT surveys considered sampling error, and the linkages between analytical assumptions and species ecology.
Nearly two-thirds of studies surveyed more than one species, and a majority used response variables that ignored imperfect detection (e.g. presence–absence, relative abundance). Many studies used opportunistic sampling and did not explicitly report details of sampling design and camera deployment that could affect conclusions.
Most studies estimating density used capture–recapture methods on marked species, with spatially explicit methods becoming more prominent. Few studies estimated density for unmarked species, focusing instead on occupancy modelling or measures of relative abundance. While occupancy studies estimated detectability, most did not explicitly define key components of the modelling framework (e.g. a site) or discuss potential violations of model assumptions (e.g. site closure). Studies using relative abundance relied on assumptions of equal detectability, and most did not explicitly define expected relationships between measured responses and underlying ecological processes (e.g. animal abundance and movement).
Synthesis and applications. The rapid adoption of camera traps represents an exciting transition in wildlife survey methodology. We remain optimistic about the technology's promise, but call for more explicit consideration of underlying processes of animal abundance, movement and detection by cameras, including more thorough reporting of methodological details and assumptions. Such transparency will facilitate efforts to evaluate and improve the reliability of camera trap surveys, ultimately leading to stronger inferences and helping to meet modern needs for effective ecological inquiry and biodiversity monitoring.

https://besjournals.onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2664.12432

REVIEW: Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes

Camera Traps in Animal Ecology

The difficulty of making valid comparisons between historical and contemporary data is an obstacle to documenting range change in relation to environmental modifications. Recent statistical advances use occupancy modeling to estimate simultaneously the probability of detection and the probability of occupancy, and enable unbiased comparisons between historical and modern data; however, they require repeated surveys at the same locations within a time period. We present two models for explicitly comparing occupancy between historical and modern eras, and discuss methods to measure range change. We suggest that keepers of historical data have crucial roles in curating and aiding accessibility to data, and we recommend that collectors of contemporary specimen data organize their sampling efforts to include repeated surveys to estimate detection probabilities.

Detecting range shifts from historical species occurrences: new perspectives on old data

Summary 1. Occupancy estimation and modelling based on detection‐nondetection data provide an effective way of exploring change in a species’ distribution across time and space in cases where the species is not always detected with certainty. Today, many monitoring programmes target multiple species, or life stages within a species, requiring the use of multiple detection methods. When multiple methods or devices are used at the same sample sites, animals can be detected by more than one method. 2. We develop occupancy models for multiple detection methods that permit simultaneous use of data from all methods for inference about method-specific detection probabilities. Moreover, the approach permits estimation of occupancy at two spatial scales: the larger scale corresponds to species’ use of a sample unit, whereas the smaller scale corresponds to presence of the species at the local sample station or site. 3. We apply the models to data collected on two different vertebrate species: striped skunks Mephitis mephitis and red salamanders Pseudotriton ruber . For striped skunks, large-scale occupancy estimates were consistent between two sampling seasons. Small-scale occupancy probabilities were slightly lower in the late winter/spring when skunks tend to conserve energy, and movements are limited to males in search of females for breeding. There was strong evidence of method-specific detection probabilities for skunks. As anticipated, large- and small-scale occupancy areas completely overlapped for red salamanders. The analyses provided weak evidence of method-specific detection probabilities for this species. 4. Synthesis and applications. Increasingly, many studies are utilizing multiple detection methods at sampling locations. The modelling approach presented here makes efficient use of detections from multiple methods to estimate occupancy probabilities at two spatial scales and to compare detection probabilities associated with different detection methods. The models can be viewed as another variation of Pollock’s robust design and may be applicable to a wide variety of scenarios where species occur in an area but are not always near the sampled locations. The estimation approach is likely to be especially useful in multispecies conservation programmes by providing efficient estimates using multiple detection devices and by providing device-specific detection probability estimates for use in survey design.

https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2664.2008.01509.x

Multi-scale occupancy estimation and modelling using multiple detection methods

Large-scale, multispecies monitoring programs are widely used to assess changes in wildlife populations but they often assume constant detectability when documenting species occurrence. This assumption is rarely met in practice because animal populations vary across time and space. As a result, detectability of a species can be influenced by a number of physical, biological, or anthropogenic factors (e.g., weather, seasonality, topography, biological rhythms, sampling methods). To evaluate some of these influences, we estimated site occupancy rates using species-specific detection probabilities for meso- and large terrestrial mammal species on Cape Cod, Massachusetts, USA. We used model selection to assess the influence of different sampling methods and major environmental factors on our ability to detect individual species. Remote cameras detected the most species (9), followed by cubby boxes (7) and hair traps (4) over a 13-month period. Estimated site occupancy rates were similar among sampling methods for most species when detection probabilities exceeded 0.15, but we question estimates obtained from methods with detection probabilities between 0.05 and 0.15, and we consider methods with lower probabilities unacceptable for occupancy estimation and inference. Estimated detection probabilities can be used to accommodate variation in sampling methods, which allows for comparison of monitoring programs using different protocols. Vegetation and seasonality produced species-specific differences in detectability and occupancy, but differences were not consistent within or among species, which suggests that our results should be considered in the context of local habitat features and life history traits for the target species. We believe that site occupancy is a useful state variable and suggest that monitoring programs for mammals using occupancy data consider detectability prior to making inferences about species distributions or population change.

/pdf/estimating-site-occupancy-and-detection-probability-40xzx5l7yd.pdf

Estimating Site Occupancy and Detection Probability Parameters for Meso‐ And Large Mammals in a Coastal Ecosystem

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Neil W. Talancy

Papers

Multi-scale occupancy estimation and modelling using multiple detection methods

Estimating Site Occupancy and Detection Probability Parameters for Meso‐ And Large Mammals in a Coastal Ecosystem

An inventory of terrestrial mammals at national parks in the Northeast Temperate Network and Sagamore Hill National Historic Site