scholarly journals Northern bobwhite select for shrubby thickets interspersed in grasslands during fall and winter

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255298
Author(s):  
Alisha R. Mosloff ◽  
Mitch D. Weegman ◽  
Frank R. Thompson ◽  
Thomas R. Thompson

Resource selection is a key component in understanding the ecological processes underlying population dynamics, particularly for species such as northern bobwhite (Colinus virginianus), which are declining across their range in North America. There is a growing body of literature quantifying breeding season resource selection in bobwhite; however, winter information is particularly sparse despite it being a season of substantial mortality. Information regarding winter resource selection is necessary to quantify the extent to which resource requirements are driving population change. We modeled bobwhite fall and winter resource selection as a function of vegetation structure, composition, and management from traditionally (intensively) managed sites and remnant (extensively managed) grassland sites in southwest Missouri using multinomial logit discrete choice models in a Bayesian framework. We captured 158 bobwhite from 67 unique coveys and attached transmitters to 119 individuals. We created 671 choice sets comprised of 1 used location and 3 available locations. Bobwhite selected for locations which were closer to trees during the winter; the relative probability of selection decreased from 0.45 (85% Credible Interval [CRI]: 0.17–0.74) to 0.00 (85% CRI: 0.00–0.002) as distance to trees ranged from 0–313 m. The relative probability of selection increased from near 0 (85% CRI: 0.00–0.01) to 0.33 (85% CRI: 0.09–0.56) and from near 0 (85% CRI: 0.00–0.00) to 0.51 (85% CRI: 0.36–0.71) as visual obstruction increased from 0 to 100% during fall and winter, respectively. Bobwhite also selected locations with more woody stems; the relative probability of selection increased from near 0.00 (85% CRI: 0.00–0.002) to 0.30 (85% CRI: 0.17–0.46) and near 0.00 (85% CRI: 0.00–0.001) to 0.35 (85% CRI: 0.22–0.55) as stem count ranged from 0 to 1000 stems in fall and winter, respectively. The relative probability of selection also decreased from 0.35 (85% CRI: 0.20–0.54) to nearly 0 (85% CRI: 0.00–0.001) as percent grass varied from 0 to 100% in fall. We suggest that dense shrub cover in close proximity to native grasslands is an important component of fall and winter cover given bobwhite selection of shrub cover and previously reported survival benefits in fall and winter.

Author(s):  
Megan Rhuemann ◽  
Sue Wolff

Sagebrush habitats (Artemisia spp.) across the western United States have been continuously altered since the arrival of early European settlers. Habitat loss and fragmentation in sagebrush-dominated habitats has been attributed to domestic livestock, introduction of non-native vegetation, agricultural expansion, urbanization, and changes in ecological processes that regulate ecosystems (Knick et al. 2003). These alterations have resulted in landscape level changes; for example, it is estimated that between 50-60% of the nearly 63 million hectares once covered by sagebrush in the west have been either completely converted to non-native grasslands or now contain non­native grasses in the understory (Miller and Eddleman 2001, West 2000 and 1996). The encroachment of non-native plants that compete with native vegetation has been identified as one of the most serious threats to the health and integrity of sagebrush ecosystems throughout the west (Paige and Ritter 1999).


2015 ◽  
Vol 282 (1812) ◽  
pp. 20151001 ◽  
Author(s):  
Bonnie G. Waring ◽  
Leonor Álvarez-Cansino ◽  
Kathryn E. Barry ◽  
Kristen K. Becklund ◽  
Sarah Dale ◽  
...  

Plant species leave a chemical signature in the soils below them, generating fine-scale spatial variation that drives ecological processes. Since the publication of a seminal paper on plant-mediated soil heterogeneity by Paul Zinke in 1962, a robust literature has developed examining effects of individual plants on their local environments (individual plant effects). Here, we synthesize this work using meta-analysis to show that plant effects are strong and pervasive across ecosystems on six continents. Overall, soil properties beneath individual plants differ from those of neighbours by an average of 41%. Although the magnitudes of individual plant effects exhibit weak relationships with climate and latitude, they are significantly stronger in deserts and tundra than forests, and weaker in intensively managed ecosystems. The ubiquitous effects of plant individuals and species on local soil properties imply that individual plant effects have a role in plant–soil feedbacks, linking individual plants with biogeochemical processes at the ecosystem scale.


2002 ◽  
Vol 357 (1425) ◽  
pp. 1211-1219 ◽  
Author(s):  
Charles J. Krebs

To understand why population growth rate is sometimes positive and sometimes negative, ecologists have adopted two main approaches. The most common approach is through the density paradigm by plotting population growth rate against population density. The second approach is through the mechanistic paradigm by plotting population growth rate against the relevant ecological processes affecting the population. The density paradigm is applied a posteriori , works sometimes but not always and is remarkably useless in solving management problems or in providing an understanding of why populations change in size. The mechanistic paradigm investigates the factors that supposedly drive density changes and is identical to Caughley's declining population paradigm of conservation biology. The assumption that we can uncover invariant relationships between population growth rate and some other variables is an article of faith. Numerous commercial fishery applications have failed to find the invariant relationships between stock and recruitment that are predicted by the density paradigm. Environmental variation is the rule, and non–equilibrial dynamics should force us to look for the mechanisms of population change. If multiple factors determine changes in population density, there can be no predictability in either of these paradigms and we will become environmental historians rather than scientists with useful generalizations for the population problems of this century. Defining our questions clearly and adopting an experimental approach with crisp alternative hypotheses and adequate controls will be essential to building useful generalizations for solving the practical problems of population management in fisheries, wildlife and conservation.


2021 ◽  
Author(s):  
◽  
Michelle McLellan

<p>Identifying the mechanisms causing population change is essential for conserving small and declining populations. Substantial range contraction of many carnivore species has resulted in fragmented global populations with numerous small isolates in need of conservation. Here I investigate the rate and possible agents of change in two threatened grizzly bear (Ursus arctos) populations in southwestern British Columbia, Canada. I use a combination of population vital rates estimates, population trends, habitat quality analyses, and comparisons to what has been described in the literature, to carefully compare among possible mechanisms of change. First, I estimate population density, realized growth rates (λ), and the demographic components of population change for each population using DNA based capture-recapture data in both spatially explicit capture-recapture (SECR) and non-spatial Pradel robust design frameworks. The larger population had 21.5 bears/1000km2 and between 2006 and 2016 was growing (λPradel = 1.02 ± 0.02 SE, λsecr = 1.01 ± 4.6 x10-5 SE) following the cessation of hunting. The adjacent but smaller population had 6.3 bears/1000km2 and between 2005 and 2017 was likely declining (λPradel = 0.95 ± 0.03 SE, λsecr = 0.98 ± 0.02 SE). Estimates of apparent survival and recruitment indicated that lower recruitment was the dominant factor limiting population growth in the smaller population.  Then I use data from GPS-collared bears to estimate reproduction, survival and projected population change (λ) in both populations. Adult female survival was 0.96 (95% CI: 0.80-0.99) in the larger population (McGillvary Mountains or MM) and 0.87 (95% CI: 0.69-0.95) in the small, isolated population (North Stein-Nahatlatch or NSN). Cub survival was also higher in the MM (0.85, 95%CI: 0.62-0.95) than the NSN population (0.33, 95%CI: 0.11-0.67). This analysis identifies both low adult female survival and low cub survival as the demographic factors associated with population decline in the smaller population. By comparing the vital rates from these two populations with other small populations, I suggest that when grizzly bear populations are isolated, there appears to be a tipping point (de Silva and Leimgruber 2019) around 50 individuals, below which adult female mortality, even with intensive management, becomes prohibitive for population recovery. This analysis provides the first detailed estimates of population vital rates for a grizzly bear population of this size, and this information has been important for subsequent management action. To determine whether bottom-up factors (i.e. food) are limiting population growth and recovery in the small isolated population I use resource selection analysis from GPS collar data. I develop resource selection functions (RSF) for four dominant foraging seasons: the spring-early summer season when bears feed predominantly on herbaceous plants and dig for bulbs, the early fruit season where they feed on low elevation berries and cherries, the huckleberry season and the post berry season when foraging behaviours are most diverse but whitebark pine nuts are a relatively common food source. The differences in overall availability of high-quality habitats for different food types, especially huckleberries, between populations suggests that season specific bottom-up effects may account for some differences in population densities. Resource selections are a very common tool used for estimating resource distribution and availability, however, their ability to estimate food abundance on the ground are usually not tested. I assessed the accuracy of the resulting RSF models for predicting huckleberry presence and abundance measured in field plots. My results show that berry specific models did predict berry abundance in previously disturbed sites though varied in accuracy depending on how the models were categorized and projected across the landscape. Finally, I combine spatially explicit capture-recapture methods and models developed from resource selection modelling to estimate the effect of seasonal habitat availability and open road density, as a surrogate for top-down effects, on the bear density in the two populations. I found that population density is most strongly connected to habitats selected during a season when bears fed on huckleberries, the major high-energy food bears eat during hyperphagia in this area, as well as a large baseline difference between populations. The abundance of high-quality huckleberry habitat appears to be an important factor enabling the recovery of the larger population that is also genetically connected to other bears. The adjacent, smaller and genetically isolated population is not growing. The relatively low abundance of high-quality berry habitat in this population may be contributing to the lack of growth of the population. However, it is likely that the legacy of historic mortality and current stochastic effects, inbreeding effects, or other Allee effects, are also contributing to the continued low density observed. While these small population effects may be more challenging to overcome, this analysis suggests that the landscape can accommodate a higher population density than that currently observed.</p>


2021 ◽  
Author(s):  
◽  
Michelle McLellan

<p>Identifying the mechanisms causing population change is essential for conserving small and declining populations. Substantial range contraction of many carnivore species has resulted in fragmented global populations with numerous small isolates in need of conservation. Here I investigate the rate and possible agents of change in two threatened grizzly bear (Ursus arctos) populations in southwestern British Columbia, Canada. I use a combination of population vital rates estimates, population trends, habitat quality analyses, and comparisons to what has been described in the literature, to carefully compare among possible mechanisms of change. First, I estimate population density, realized growth rates (λ), and the demographic components of population change for each population using DNA based capture-recapture data in both spatially explicit capture-recapture (SECR) and non-spatial Pradel robust design frameworks. The larger population had 21.5 bears/1000km2 and between 2006 and 2016 was growing (λPradel = 1.02 ± 0.02 SE, λsecr = 1.01 ± 4.6 x10-5 SE) following the cessation of hunting. The adjacent but smaller population had 6.3 bears/1000km2 and between 2005 and 2017 was likely declining (λPradel = 0.95 ± 0.03 SE, λsecr = 0.98 ± 0.02 SE). Estimates of apparent survival and recruitment indicated that lower recruitment was the dominant factor limiting population growth in the smaller population.  Then I use data from GPS-collared bears to estimate reproduction, survival and projected population change (λ) in both populations. Adult female survival was 0.96 (95% CI: 0.80-0.99) in the larger population (McGillvary Mountains or MM) and 0.87 (95% CI: 0.69-0.95) in the small, isolated population (North Stein-Nahatlatch or NSN). Cub survival was also higher in the MM (0.85, 95%CI: 0.62-0.95) than the NSN population (0.33, 95%CI: 0.11-0.67). This analysis identifies both low adult female survival and low cub survival as the demographic factors associated with population decline in the smaller population. By comparing the vital rates from these two populations with other small populations, I suggest that when grizzly bear populations are isolated, there appears to be a tipping point (de Silva and Leimgruber 2019) around 50 individuals, below which adult female mortality, even with intensive management, becomes prohibitive for population recovery. This analysis provides the first detailed estimates of population vital rates for a grizzly bear population of this size, and this information has been important for subsequent management action. To determine whether bottom-up factors (i.e. food) are limiting population growth and recovery in the small isolated population I use resource selection analysis from GPS collar data. I develop resource selection functions (RSF) for four dominant foraging seasons: the spring-early summer season when bears feed predominantly on herbaceous plants and dig for bulbs, the early fruit season where they feed on low elevation berries and cherries, the huckleberry season and the post berry season when foraging behaviours are most diverse but whitebark pine nuts are a relatively common food source. The differences in overall availability of high-quality habitats for different food types, especially huckleberries, between populations suggests that season specific bottom-up effects may account for some differences in population densities. Resource selections are a very common tool used for estimating resource distribution and availability, however, their ability to estimate food abundance on the ground are usually not tested. I assessed the accuracy of the resulting RSF models for predicting huckleberry presence and abundance measured in field plots. My results show that berry specific models did predict berry abundance in previously disturbed sites though varied in accuracy depending on how the models were categorized and projected across the landscape. Finally, I combine spatially explicit capture-recapture methods and models developed from resource selection modelling to estimate the effect of seasonal habitat availability and open road density, as a surrogate for top-down effects, on the bear density in the two populations. I found that population density is most strongly connected to habitats selected during a season when bears fed on huckleberries, the major high-energy food bears eat during hyperphagia in this area, as well as a large baseline difference between populations. The abundance of high-quality huckleberry habitat appears to be an important factor enabling the recovery of the larger population that is also genetically connected to other bears. The adjacent, smaller and genetically isolated population is not growing. The relatively low abundance of high-quality berry habitat in this population may be contributing to the lack of growth of the population. However, it is likely that the legacy of historic mortality and current stochastic effects, inbreeding effects, or other Allee effects, are also contributing to the continued low density observed. While these small population effects may be more challenging to overcome, this analysis suggests that the landscape can accommodate a higher population density than that currently observed.</p>


2010 ◽  
Vol 67 (3) ◽  
pp. 327-333
Author(s):  
Sandra Vergara Cardozo ◽  
Bryan Frederick John Manly ◽  
Carlos Tadeu dos Santos Dias

Based on a review of most recent data analyses on resource selection by animals as well as on recent suggestions that indicate the lack of an unified statistical theory that shows how resource selection can be detected and measured, the authors suggest that the concept of resource selection function (RSF) can be the base for the development of a theory. The revision of discrete choice models (DCM) is suggested as an approximation to estimate the RSF when the choice of animal or groups of animals involves different sets of available resource units. The definition of RSF requires that the resource which is being studied consists of discrete units. The statistical method often used to estimate the RSF is the logistic regression but DCM can also be used. The theory of DCM has been well developed for the analysis of data sets involving choices of products by humans, but it can also be applicable to the choice of habitat by animals, with some modifications. The comparison of the logistic regression with the DCM for one choice is made because the coefficient estimates of the logistic regression model include an intercept, which are not presented by the DCM. The objective of this work was to compare the estimates of the RSF obtained by applying the logistic regression and the DCM to the data set on habitat selection of the spotted owl (Strix occidentalis) in the north west of the United States.


Oecologia ◽  
2021 ◽  
Vol 195 (4) ◽  
pp. 937-948
Author(s):  
Emily A. Sinnott ◽  
Mitch D. Weegman ◽  
Thomas R. Thompson ◽  
Frank R. Thompson

2005 ◽  
Vol 35 (10) ◽  
pp. 2387-2393 ◽  
Author(s):  
Jérôme Lemaître ◽  
Marc-André Villard

We analyzed the relative influence of foraging substrate characteristics as predictors of the probability of use by the pileated woodpecker (Dryocopus pileatus L.) and determined threshold values for significant predictors. We sampled used and available substrates around 126 stations distributed in an intensively managed forest in northwestern New Brunswick, Canada. We developed a resource selection function (RSF), validated by a resampling procedure, and compared selection ratios for significant predictors. Diameter at breast height (DBH) of trees and snags was the most significant predictor, probably reflecting nesting selection by its main prey, carpenter ants (Camponotus spp.). The pileated woodpecker preferred deciduous substrates with DBH >35 cm and coniferous substrates with DBH >30 cm. Among deciduous substrates, it preferred snags over living trees, but there was no such preference for coniferous substrates. American beech (Fagus grandifolia Ehrh.) was clearly preferred over all other species. The RSF we developed and the thresholds we obtained should help forest managers and conservation planners assess habitat quality for this keystone species.


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