160 research outputs found
Effects of Photo and Genotype-Based Misidentification Error on Estimates of Survival, Detection and State Transition using Multistate Survival Models
We simulated multistate capture histories (CHs) by varying state survival (ϕ), detection (p) and transition (ψ), number of total capture occasions and releases per capture occasion and then modified these scenarios to mimic false rejection error (FRE), a common misidentification error, resulting from the failure to match samples of the same individual. We then fit a multistate model and estimated accuracy, bias and precision of state-specific ϕ, p and ψ to better understand the effects of FRE on different simulation scenarios. As expected, ϕ, and p, decreased in accuracy with FRE, with lower accuracy when CHs were simulated under a shorter-term study and a lower number of releases per capture occasion (lower sample size). Accuracy of ψ estimates were robust to FRE except in those CH scenarios simulated using low sample size. The effect of FRE on bias was not consistent among parameters and differed by CH scenario. As expected, ϕ was negatively biased with increased FRE (except for the low ϕ low p CH scenario simulated with a low sample size), but we found that the magnitude of bias differed by scenario (high p CH scenarios were more negatively biased). State transition was relatively unbiased, except for the low p CH scenarios simulated with a low sample size, which were positively biased with FRE, and high p CH scenarios simulated with a low sample size. The effect of FRE on precision was not consistent among parameters and differed by scenario and sample size. Precision of ϕ decreased with FRE and was lowest with the low ϕ low p CH scenarios. Precision of p estimates also decreased with FRE under all scenarios, except the low ϕ high p CH scenarios. However, precision of ψ increased with FRE, except for those CH scenarios simulated with a low sample size. Our results demonstrate how FRE leads to loss of accuracy in parameter estimates in a multistate model with the exception of ψ when estimated using an adequate sample size
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Relationship between landscape structure and avian abundance patterns in the Oregon Coast Range
Human-induced fragmentation of forests is increasing, yet the consequences of these landscape changes to vertebrate communities are poorly understood. Despite progress in our understanding of how bird communities
respond to forest fragmentation caused by agricultural or urban development, we have little understanding of these dynamics in landscapes undergoing intensive forest management, where mature forest islands are separated by younger forest stands of varying ages. I developed a conceptual framework on vertebrate-habitat relationships in spatially complex landscapes and applied this landscape ecological perspective in the design and implementation of a field study on the relationship between landscape structure and breeding bird abundance patterns in the central Oregon Coast Range. I sampled 10
subbasins (250-300 ha) in each of 3 basins based on the proportion of subbasin in a large sawtimber condition and the spatial distribution pattern of that sawtimber within the subbasin. I systematically sampled each subbasin for birds during the breeding season and developed digital vegetation cover maps for each subbasin. I developed a spatial analysis program for quantifying landscape structure using the Arc/Info Geographic Information System. Using analysis of variance and regression procedures, I quantified the independent effects of habitat area and habitat pattern on several bird species, focussing on species
associated with large sawtimber. Species varied dramatically in the strength and
nature of the relationship between abundance and several gradients in habitat area and pattern at the subbasin scale. Relationships between bird abundance and landscape structure were generally weak; landscape structure typically explained less than one-third to one-half of the variation in each species' abundance among the 30 subbasins. Most species were positively associated with gradients in increasing landscape heterogeneity or fragmentation of their habitats; that is, they were associated with the more fragmented habitats. Only winter wrens showed consistent evidence of association with the least fragmented landscapes. The results are interpreted within the context of the
conceptual framework outlined in the second chapter and within the scope and limitations of the study
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Human-eagle interactions on the lower Columbia River
During the past decade (1978-87), breeding success and
productivity of bald eagles on the lower Columbia River (LCR) has
been far below state and regional averages and well below levels
required for delisting under the Endangered Species Act by the
Pacific States Bald Eagle Recovery Plan. Human disturbance was
suspected as one possible cause for this depressed productivity. I
investigated the response of breeding bald eagles to human activities
in foraging areas on the LCR during Spring and Summer, 1985 and 1986.
Based on preliminary observations I developed a conceptual model
for understanding human-eagle interactions in foraging areas. This
model contrasts two forms of human disturbance. In the first type of
interaction, a moving or actively approaching human forces a direct
confrontation with an eagle. This type of interaction is extremely
rare on the LCR and accounts for a minor proportion of an eagle's
time-energy budget. Only 20% of all moving human activities observed
during this study resulted in close contact (i.e. <500 m) with
eagles; less than 6% of all human-eagle encounters within 500 m
resulted in a visible disturbance to an eagle.
In the second type of interaction, an eagle is presented with
several alternative foraging destinations, several of which may have
human activities occurring nearby. In this situation, the eagle has
the freedom to choose an activity pattern given the existing pattern
of human activities. This type of interaction represents the major
form of human-eagle interaction on the LCR. To investigate this, I
studied six pairs of eagles in each of two years; each pair was
sampled three times during the breeding season, roughly corresponding
to incubation, nestling, and fledgling stages of the nesting cycle.
Each sample consisted of a 3-day control period, during which I
monitored "normal" eagle activity patterns; and a 3-day influence
period, during which I "disturbed" (i.e. stationary boat with
observer) a high-use foraging area. I compared eagle activity
patterns within 1200 m of the experimental disturbance between
sampling periods. On the average, eagles avoided an area within
300-400 m of the human activity. In most cases, eagles spent less
time and had fewer foraging attempts in the entire sample area during
the influence period. Eagle responses were consistent among pairs
and among nesting stages; although, eagle foraging activity increased
dramatically and was more concentrated in the high-use areas during
the later nesting stages.
Based on these results, I developed a model of human-eagle
interactions in foraging areas. I used this model and the results of
this study to develop several alternative management recommendations
involving temporal and spatial restrictions of human activity. I
recommend buffer zones 400 in wide around high-use foraging areas as
the single most appropriate and practical strategy
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Designing Sustainable Landscapes: Modeling Ecological Integrity
Integrity [updated 3/7/2017] -- This document describes our coarse filter assessment based on the concept of landscape ecological integrity. Here, we define ecological integrity and the four major components of integrity that we quantify: intactness, resiliency, ecosystem diversity and adaptive capacity, and describe the various indices used to quantify each component
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Designing Sustainable Landscapes: Stream gradient settings variable
Stream gradient is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Stream gradient is a measure of the percent slope of a stream, which is a primary determinate of water velocity and thus sediment and nutrient transport, and habitat for aquatic plants, invertebrate, fish, and other organisms. Stream gradient is often approximated by categories such as pool, riffle, run, and cascade. Stream gradient is 0% for lentic waterbodies, palustrine, and uplands. It ranges from 0% to infinity (theoretically) for streams.https://scholarworks.umass.edu/data/1011/thumbnail.jp
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Designing Sustainable Landscapes: Local and Regional Conductance
Local and HUC6 regional conductance are two of the principal Designing Sustainable Landscapes (DSL) landscape conservation design (LCD) products, which are best understood in the context of the full LCD process described in detail in the technical document on landscape design (McGarigal et al 2017). These particular products were initially developed for the Connecticut River watershed as part of the Connect the Connecticut project (www.connecttheconnecticut.org) — a collaborative partnership under the auspices of the North Atlantic Landscape Conservation Cooperative (NALCC), and subsequently developed for the entire Northeast region as part of the Nature\u27s Network project (www.naturesnetwork.org).https://scholarworks.umass.edu/data/1047/thumbnail.jp
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Designing Sustainable Landscapes: Climate Stress Metric
Climate is a major factor in determining ecosystem distribution, composition, structure and function. Therefore, with climate change it is reasonable to anticipate heterogeneous climate stress across the landscape in response to heterogeneous shifts in climate normals (Iverson et al. 2014). The climate stress metric assesses the estimated climate stress that may be exerted on a focal cell in 2080 based on departure from the current climate niche breadth of the corresponding ecosystem. Essentially, this metric measures the magnitude of climate change stress at the focal cell based on the current climate niche of the corresponding ecosystem and the predicted change in climate (i.e., how much is the climate of the focal cell moving away from the current climate niche of the corresponding ecosystem) between 2010-2080 based on the average of two climate change scenarios (see below) (Fig. 1). Cells where the predicted climate suitability in the future decreases (i.e., climate is becoming less suitable for that ecosystem) are considered stressed, and the stress increases as the predicted climate becomes less suitable based on the ecosystem\u27s current climate niche model. Conversely, cells where the predicted climate suitability in the future increases (i.e., climate is improving for that ecosystem) are considered unstressed and assigned a value of zero.
The climate stress metric is an element of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2017). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. As a stressor metric, climate stress values range from 0 (no effect from climate stress) to a theoretical maximum of 1 (severe effect; although in real landscapes, the metric never reaches 1). Note that the climate stress metric is computed separately for each ecosystem because each ecosystem has its own estimated climate niche (see below). This contrasts with all other stressor metrics, which are computed ihttps://scholarworks.umass.edu/data/1037/thumbnail.jp
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Designing Sustainable Landscapes: Climate Data
Climate [updated 5/9/2018] -- This document describes how we model climate change. More specifically, this document describes the source of the climate change data that we are using and the methods we are applying to downscale the data to meet our needs
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Designing Sustainable Landscapes: Traffic settings variable
Traffic is one of several ecological settings variables that collectively characterize the biophysical setting of each 30 m cell at a given point in time (McGarigal et al 2017). Traffic measures the estimated probability of an animal crossing the road being hit by a vehicle given the mean traffic rate, an important determinant of landscape connectivity for mobile terrestrial organisms. It is based on an empirical model of mean vehicles per day, using point counts of traffic, and a transformation to estimate the mortality rate for road crossings. Traffic is a dynamic settings variable, increasing in future timesteps with urban growth.https://scholarworks.umass.edu/data/1012/thumbnail.jp
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Designing Sustainable Landscapes: Watershed habitat loss, watershed imperviousness, road salt, sediment, nutrients, and dam intensity metrics
This document describes a suite of stressor metrics that assess the various effects of development in the watershed of the focal cell, as opposed to a (usually) circular window around the focal cell, as with the other metrics. These metrics are used for lotic, lentic, and wetland systems. All effects are weighted by a the time of flow from each stressor source to the focal cell, thus, stressor sources that fall within a stream have a greater effect than those in distant uplands within the watershed. These share a common algorithm, but each has unique parameters. These metrics are elements of the ecological integrity analysis of the Designing Sustainable Landscapes (DSL) project (see technical document on integrity, McGarigal et al 2014). Consisting of a composite of 21 stressor and resiliency metrics, the index of ecological integrity (IEI) assesses the relative intactness and resiliency to environmental change of ecological systems throughout the northeast. These stressor metrics range from 0 (no effect) to maximum values that differ for each metric (severe effect). See Table 1 for parameters for each metric.https://scholarworks.umass.edu/data/1025/thumbnail.jp
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