Design and Statistical Issues Related to Habitat Analysis

2021 ◽  
pp. 95-116
Author(s):  
Joseph A. Veech
Keyword(s):  
Count Data ◽  
Detection Probability ◽  
Spatial Dispersion ◽  
Species Traits ◽  
Habitat Associations ◽  
Statistical Techniques ◽  
Focal Species ◽  
Habitat Analysis ◽  
Background Density ◽  
Statistical Issues

There are many different design and statistical issues that a researcher should consider when developing the data collection protocol or when interpreting results from a habitat analysis. One of the first considerations is simply the area to include in the study. This depends on the behavior (particularly mobility) of the focal species and logistical constraints. The amount of area also relates to the number of survey locations (plots, transects, or other) and their spatial placement. Survey data often include many instances of a species absent from a spatial sampling unit. These could be true absences or might represent very low species detection probability. There are different statistical techniques for estimating detection probability as well as analyzing data with a substantial proportion of zero-abundance values. The spatial dispersion of the species within the overall study area or region is never random. Even apart from the effect of habitat, individuals are often aggregated due to various environmental factors or species traits. This can affect count data collected from survey plots. Related to spatial dispersion, the overall background density of the species within the study area can introduce particular challenges in identifying meaningful habitat associations. Statistical issues such as normality, multicollinearity, spatial and temporal autocorrelation may be relatively common and need to be addressed prior to an analysis. None of these design and statistical issues presents insurmountable challenges to a habitat analysis.

scholarly journals Singing in the suburbs: point count surveys efficiently reveal habitat associations for nocturnal Orthoptera across an urban-to-rural gradient

2020 ◽  
Vol 24 (6) ◽  
pp. 1031-1043
Author(s):  
Darin J. McNeil ◽  
Christina M. Grozinger
Keyword(s):  
Site Occupancy ◽  
Habitat Associations ◽  
Survey Method ◽  
Population Declines ◽  
Detection Rates ◽  
Point Count ◽  
Point Counts ◽  
Focal Species ◽  
Habitat Gradient ◽  
Count Surveys

Abstract As evidence for global insect population declines continues to amass, several studies have indicated that Orthoptera (grasshoppers, crickets, and katydids) are among the most threatened insect groups. Understanding Orthoptera populations across large spatial extents requires efficient survey protocols, however, many previously established methods are expensive and/or labor-intensive. One survey method widely employed in wildlife biology, the aural point count, may work well for crickets and katydids (suborder: Ensifera) because males produce conspicuous, species-specific mating calls. We conducted repeated point count surveys across an urban-to-rural gradient in central Pennsylvania. Occupancy analyses of ten focal species indicated that, although detection probability rates varied by species from 0.43 to 0.98, detection rates compounded over five visits such that all focal species achieved cumulative > 0.90. Factors associated with site occupancy varied among species with some positively associated with urbanization (e.g., Greater Anglewing, Microcentrum rhombifolium), some negatively associated with urbanization (e.g., Sword-bearing Conehead, Neoconocephalus ensiger), and others exhibiting constant occupancy across a habitat gradient (e.g., Common True Katydid, Pterophylla camellifolia). Our community-level analysis revealed that different species’ habitat associations interacted such that intermediate levels of urbanization (i.e., suburbs) hosted the highest number of species. Implications for insect conservation Ultimately, our analyses clearly support the concept that aural point counts paired with static occupancy modeling can serve as an important tool for monitoring night-singing Orthoptera populations. Applications of point count surveys by both researchers and citizen scientists may improve our understanding Ensifera populations and help in the global conservation of these threatened insects.

scholarly journals Forest-type specialization strongly predicts avian responses to tropical agriculture

2019 ◽  
Vol 286 (1913) ◽  
pp. 20191724
Author(s):  
Jacob B. Socolar ◽  
David S. Wilcove
Keyword(s):  
Forest Type ◽  
Soil Nutrient ◽  
Predictive Performance ◽  
Species Traits ◽  
Habitat Associations ◽  
Habitat Specialization ◽  
Habitat Features ◽  
Specialist Species ◽  
Habitat Specialist Species ◽  
Poor Soil

Species’ traits influence how populations respond to land-use change. However, even in well-characterized groups such as birds, widely studied traits explain only a modest proportion of the variance in response across species. Here, we show that associations with particular forest types strongly predict the sensitivity of forest-dwelling Amazonian birds to agriculture. Incorporating these fine-scale habitat associations into models of population response dramatically improves predictive performance and markedly outperforms the functional traits that commonly appear in similar analyses. Moreover, by identifying habitat features that support assemblages of unusually sensitive habitat-specialist species, our model furnishes straightforward conservation recommendations. In Amazonia, species that specialize on forests along a soil–nutrient gradient (i.e. both rich-soil specialists and poor-soil specialists) are exceptionally sensitive to agriculture, whereas species that specialize on floodplain forests are unusually insensitive. Thus, habitat specialization per se does not predict disturbance sensitivity, but particular habitat associations do. A focus on conserving specific habitats that harbour highly sensitive avifaunas (e.g. poor-soil forest) would protect a critically threatened component of regional biodiversity. We present a conceptual model to explain the divergent responses of habitat specialists in the different habitats, and we suggest that similar patterns and conservation opportunities probably exist for other taxa and regions.

Issues Based on Species Life History and Behavior

2021 ◽  
pp. 89-94
Author(s):  
Joseph A. Veech
Keyword(s):  
Life History ◽  
Habitat Use ◽  
Life Histories ◽  
Radio Telemetry ◽  
Spatial Scales ◽  
Habitat Associations ◽  
Habitat Characteristics ◽  
Habitat Analysis ◽  
The Individual ◽  
And Behavior

Species vary tremendously in their life histories and behavior. The particular life history traits and behavior of the focal species must be considered when designing a study to examine habitat associations. For some species, individuals use different areas (of the landscape or territory) for breeding and foraging. As such, the important characteristics for the foraging and breeding habitats may be different. The dramatically different life stages of some organisms (e.g., amphibians and some insects) often correspond to equally dramatic differences in habitat use between juveniles and adults. For some species, habitat use differs among seasons. Species that are highly mobile and have individuals that move around substantially on a daily or weekly basis are particularly challenging for a habitat analysis. For these species, the most efficient and appropriate study design may be one that tracks individuals (through radio-telemetry or GPS) and analyzes the environmental or habitat characteristics at locations where the individual has stopped, rather than trying to survey for the species in pre-established and insufficiently small survey plots. In addition, individual movement and the issues mentioned above may necessitate that environmental variables are measured and analyzed at multiple spatial scales.

Goals of Characterizing a Species Habitat

2021 ◽  
pp. 73-80
Author(s):  
Joseph A. Veech
Keyword(s):  
Carrying Capacity ◽  
Species Interactions ◽  
Environmental Variables ◽  
Species Coexistence ◽  
Habitat Associations ◽  
Habitat Characteristics ◽  
Habitat Requirements ◽  
Distribution Models ◽  
Habitat Analysis ◽  
Great Utility

There are several reasons for conducting a habitat analysis and identifying the environmental (habitat) characteristics that a species associates with. (1) Knowledge of a species’ habitat requirements is crucial in restoring and managing habitat for the species. (2) Carrying capacity informs us about the potential (or lack thereof) for future population growth based on resource availability. Knowledge of a species’ habitat requirements allows us to interpret the importance of carrying capacity in a habitat-specific way. (3) The study of species interactions and the potential for species coexistence is supported by having knowledge of the habitat of each species under investigation. (4) Habitat preference and selection as eco-evolutionary processes continue to be widely studied by ecologists—interpretation of the results of such studies is best done with knowledge of the species–habitat associations. Such knowledge can also be useful in the design of preference and selection studies. (5) Knowledge of species–habitat associations can also be of great use in selecting the environmental variables to use in species distribution models. All five of these goals point to the great utility of conducting a habitat analysis as a supporting investigation or as a way to obtain knowledge to put to a practical purpose.

Analysis of the Habitat Associations of a Hypothetical Beetle Species

2021 ◽  
pp. 117-134
Author(s):  
Joseph A. Veech
Keyword(s):  
Woody Debris ◽  
Canopy Cover ◽  
Habitat Associations ◽  
Beetle Species ◽  
Habitat Analysis ◽  
Oak Trees ◽  
Environmental Predictor ◽  
Components Analysis ◽  
Classification And Regression ◽  
Study Sites

A dataset for a hypothetical ground-dwelling beetle species is used to illustrate five methods of habitat analysis: (1) comparison of group means, (2) multiple linear regression, (3) multiple logistic regression, (4) classification and regression trees, and (5) principal components analysis. The dataset consists of abundance (counts of individuals) recorded in each of 100 small survey plots located throughout forested study sites. The following environmental predictor variables were measured in each plot: percentage canopy cover, depth of leaf litter, volume of woody debris, ratio of oak to non-oak trees, and soil type. Techniques for assessing normality of each variable and multicollinearity among variables are discussed and recommended prior to conducting the habitat analysis. Assumptions, strengths, and weaknesses of each method are discussed.

Environmental conditions and call-broadcast influence detection of eastern forest owls during standardized surveys

The Condor ◽  
2020 ◽  
Vol 122 (3) ◽  
Author(s):  
Kyle A Lima ◽  
Erynn M Call ◽  
Thomas P Hodgman ◽  
David S Potter ◽  
Susan Gallo ◽  
...  
Keyword(s):  
Detection Probability ◽  
Environmental Variables ◽  
Monitoring Program ◽  
Bubo Virginianus ◽  
Focal Species ◽  
Strix Varia ◽  
Specific Effects ◽  
Affect Detection ◽  
Species Specific ◽  
Temperature Time

Abstract Owls play crucial roles in the environment and provide ecosystem services making them important to monitor and study. However, standardized methods for most species do not exist, and we lack understanding of the effects of many environmental variables and call-broadcast on detection of owls during surveys. We performed a multispecies occupancy analysis of owl monitoring data collected from 2004 to 2013 across the state of Maine to examine the effects of environmental variables, conspecific and heterospecific call-broadcast, and general survey protocols on detection of 3 forest owls: Northern Saw-whet Owl (Aegolius acadicus), Barred Owl (Strix varia), and Great Horned Owl (Bubo virginianus). We found that environmental variables such as cloud cover, precipitation, temperature, time of night, and wind had species-specific effects on detection probability, and ambient noise decreased detection probability for all species. Snow cover did not affect detection of any species. We also found that conspecific call-broadcast increased detection of each species, while heterospecific call-broadcast had variable effects. Specifically, we found that Long-eared and Barred owl broadcast increased the detection of Northern Saw-whet Owl, and our results suggest additional heterospecific effects may exist. Our study showed that, compared to the protocol of the Maine Owl Monitoring Program, surveys simultaneously examining all 3 of our focal species can increase efficiency and lower disturbance by only broadcasting Long-eared and Barred owl calls during a 10-min survey. We recommend that future owl surveys take into account species-specific effects of conspecific and heterospecific call-broadcast, and use our results when designing survey protocols that include one or more of our focal species.

scholarly journals Dealing with Varying Detection Probability, Unequal Sample Sizes and Clumped Distributions in Count Data

PLoS ONE ◽  
2012 ◽  
Vol 7 (7) ◽  
pp. e40923 ◽  
Author(s):  
D. Johan Kotze ◽  
Robert B. O’Hara ◽  
Susanna Lehvävirta
Keyword(s):  
Count Data ◽  
Detection Probability ◽  
Sample Sizes ◽  
Unequal Sample Sizes

scholarly journals Analysis Study of Radar Probability of Detection for Fluctuating and Non-fluctuating Targets

2017 ◽  
Vol 2 (1) ◽  
pp. 12-20
Author(s):  
Naceur AOUNALLAH ◽  
Ali KHALFA
Keyword(s):  
Detection Probability ◽  
Signal To Noise Ratio ◽  
False Alarm Probability ◽  
Detection Performance ◽  
Probability Of Detection ◽  
Statistical Techniques ◽  
Signal To Noise ◽  
Analysis Study ◽  
Graphical Simulation ◽  
Probability Number

The radar analyst can develop and use mathematical and statistical techniques that lead to accurate prediction or adapting models for estimating the target detection performance. In radar detection theory, detection probability, false alarm probability, number of samples non-coherently integrated for a detection test, and signal-to-noise ratio (SNR) are closely interrelated. The present paper is intended to provide an overview of the calculations of radar probability of detection and its related parameters. The main methods and procedures for predicting the detection performance of either non-fluctuating or fluctuating targets are described. Performance’s analysis of the studied models is included, along with some graphical simulation examples.

Biostatistical Trends in Pediatrics: Implications for the Future

PEDIATRICS ◽  
1983 ◽  
Vol 72 (1) ◽  
pp. 84-87
Author(s):  
Gregory F. Hayden
Keyword(s):  
Case Reports ◽  
Medical Knowledge ◽  
Statistical Techniques ◽  
Basic Principles ◽  
Biomedical Journals ◽  
Critical Insight ◽  
Statistical Issues ◽  
Published Research ◽  
Maximal Benefit

All physicians face the challenge of keeping abreast with a body of medical knowledge that is growing at an exponential rate. On this account, physicians often spend countless hours each month reading journals to "keep current" with recently described techniques of diagnosis and therapy. To gain maximal benefit from their reading, physicians must be able to assess the scientific merit of published research. They must evaluate the various claims and conclusions and then decide which are valid and applicable to their own clinic settings. This type of critical insight requires familiarity with basic principles of good study design and with biostatistical logic and procedures. Unfortunately, recent studies have demonstrated that physicians' concepts regarding statistics are often inaccurate, and even more disturbing, that readers are often willing to draw conclusions unsupported by the available data.1,2 Some authorities have therefore recommended remedial statistical training for physicians by means of increased attention to statistical issues in biomedical journals. The question of exactly which statistical concepts and techniques need to be mastered, however, remains largely unanswered. On this account, I reviewed several volumes of Pediatrics to determine which statistical techniques have appeared regularly, and whether the frequency and intensity of statistical analysis have recently changed enough that a familiarity with biostatistics that was adequate a few years ago may no longer be sufficient. I surveyed the scientific reports in volumes 9 (1952),29 (1962), 49(1972), and 69 (1982). I excluded editorials, book reviews, correspondence, and the like, and I classified remaining articles as review articles, case reports, or research reports.

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