Snow and land use alter pheasant survival in South Dakota

Kauth, Hilary R.; Gregory, Andrew J.; Kauth, Adam J.; Harsh, Sprih; Runia, Travis J.; Lonsinger, Robert C.

doi:10.1002/jwmg.22243

Cited by 4 publications

(5 citation statements)

References 45 publications

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“…Areas of Illinois with high first-order stream density also often include large amounts of heterogeneous edge habitat, such as the areas around the tributaries of the Illinois, Mississippi, and Ohio Rivers in western and southern Illinois. Pheasant occupancy was most associated with forest cohesion, indicating pheasant avoidance of forested areas (Schmitz and Clark 1999, Kauth 2020) and selection of agricultural areas (which are highly negatively correlated with forest cover in Illinois; Nielson et al 2008). Surprisingly, the vast majority of the models with the lowest AIC value for pheasant included only forest cohesion, and not other covariates known to be associated with pheasant occupancy, such as proportion of grassland.…”

Section: Discussionmentioning

confidence: 99%

“…Bobwhites are associated with heterogeneous landscapes and edges, and tend to use woody cover more than pheasants do (Twedt et, Wilson, and Keister 2007, Duren et al 2011, Rosenblatt et al 2022). Pheasants are primarily associated with open grassland habitats (Clark, Schmitz, and Bogenschutz 1999, Jorgensen et al 2014, Kauth 2020), although pheasants may use wetlands and wooded areas for shelter from severe winter weather (Gabbert et al 1999). Degradation of these preferred habitats is a major cause of population declines in both species (Brennan and Kuvlesky Jr. 2005, Hernández et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…We expected that bobwhites would be most associated with heterogeneous landscapes, which in Illinois would likely be reflected by a mixed landscape of forest and agriculture (Veech 2006, Duren et al 2011, Rosenblatt et al 2022). We expected that pheasants would be most associated with grasslands, row-crop agriculture, and first-order streams, which are often surrounded by grasslands (Jorgensen et al 2014, Kauth 2020). Finally, we expected that upland survey routes would be primarily located in highly suitable bobwhite and pheasant habitats relative to BBS routes and Illinois as a whole, resulting in substantial differences between upland and integrated models in terms of predicted occupancy and parameter inference.…”

Section: Introductionmentioning

confidence: 99%

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Integrating multiple data sources improves prediction and inference for upland game bird occupancy models

Emmet

Benson

Allen

et al. 2023

Ornithological Applications

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Bird populations have declined across North America over the past several decades. Bird monitoring programs are essential for monitoring populations, but often must strike a balance between efficiency of data collection and spatial biases. Species- or habitat-specialist-specific monitoring programs may be helpful for increasing efficiency of sampling and understanding effects of management actions, but may be subject to preferential sampling bias if they are used to assess large-scale occupancy or abundance and monitoring is largely focused in high-quality habitat. More general monitoring programs, such as the North American Breeding Bird Survey (BBS) and eBird, may not preferentially sample specialists’ habitats but are subject to other forms of bias and often do not efficiently sample specialists’ habitats. We used an integrated occupancy model combining data from eBird, BBS, and Illinois state surveys of upland game bird habitat areas to estimate drivers of Northern Bobwhite (Colinus virginianus) and Ring-Necked Pheasant (Phasianus colchicus) occupancy and compare inference from single-visit, multi-visit, and integrated monitoring programs. We fit sets of candidate models using every combination of the 3 datasets except for eBird by itself, to better understand how differences in spatial biases between programs affect ecological inference. We found that, for both bobwhite and pheasant, state surveys of upland habitat increased the predictive ability of models, and BBS data usually improved inference on occupancy parameters when it was integrated with other data sources. Integrating multiple data sources partially resolved the spatial gaps in each monitoring program, while also increasing precision of parameter estimates. Integrated models may be capable of combining the higher sampling efficiency of targeted monitoring programs with the more even spatial coverage of broad-scale monitoring programs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating multiple data sources improves prediction and inference for upland game bird occupancy models

Emmet

Benson

Allen

et al. 2023

Ornithological Applications

View full text Add to dashboard Cite

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“…Apart from a mosaic of habitat that is necessary to fulfill the life stage requirements for pheasants, pheasant populations are significantly impacted by harsh weather conditions [ 20 , 26 ]. For example, drought is known to limit resources (e.g., concealment and food), which could necessitate increased movements and decreased rates of pheasant survival and reproduction [ 61 ], and harsh winter conditions (e.g., high snow depth) which can have severe negative impacts on pheasant survival [ 62 ]. The summer of 2012 was one of the harshest droughts in South Dakota history [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Habitat amount or landscape configuration: Emerging HotSpot analysis reveals the importance of habitat amount for a grassland bird in South Dakota

2022

Self Cite

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Habitat loss and fragmentation are two important drivers of biodiversity decline. Understanding how species respond to landscape composition and configuration in dynamic landscapes is of great importance for informing the conservation and management of grassland species. With limited conservation resources, prescribed management targeted at the appropriate landscape process is necessary for the effective management of species. We used pheasants (Phasianus colchicus) across South Dakota, USA as a model species to identify environmental factors driving spatiotemporal variation in population productivity. Using an emerging Hotspot analysis, we analyzed annual count data from 105 fixed pheasant brood routes over a 24-year period to identify high (HotSpot) and low (ColdSpot) pheasant population productivity areas. We then applied classification and regression tree modeling to evaluate landscape attributes associated with pheasant productivity among spatial scales (500 m and 1000 m). We found that the amount of grassland at a local spatial scale was the primary factor influencing an area being a HotSpot. Our results also demonstrated non-significant or weak effects of fragmentation per se on pheasant populations. These findings are in accordance with the habitat amount hypothesis highlighting the importance of habitat amount in the landscape for maintaining and increasing the pheasant population. We, therefore, recommend that managers should focus on increasing the total habitat area in the landscape and restoring degraded habitats. Our method of identifying areas of high productivity across the landscape can be applied to other species with count data.

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“…Due to these limitations, the Cox Proportional Hazard Model is beginning to gain in popularity as an alternative model to examine annual survivorship (Princée and Glatston 2016, Tomasevic and Marzluff 2020, Kauth et al 2022). Cox models use constant hazard h(t) function to model an exponential distribution of survival times.…”

Section: Discussionmentioning

confidence: 99%

Annual Survivorship and Movement Ecology of Migrant American Kestrels (Falco sparverius) Overwintering in North Texas

Kaleta

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American Kestrels (Falco sparverius) are a small falcon with an expansive range across North America. However, kestrels have been reported to be declining since the 1960s, with the primary cause of decline unknown. With previous research focusing on the summer breeding season, researchers have called for additional investigation in understudied wintering and migratory periods. In Chapter 2, I quantified annual survivorship against five covariates of migrant kestrels wintering in north Texas to contribute to population level analyses across the kestrel's expansive range. Notably, I found that juvenile survival is similar to that of adults once on the wintering grounds, and that aspects of urbanization may increase survival in wintering kestrels. In Chapter 3, I outlined kestrel movement ecology by quantifying migration phenology, performance, and patterns. Additionally, I identified breeding and stopover sites and analyzed both winter and summer home ranges. In this analysis, I contributed three additional migration tracks to the five currently published. Further, I reported the first loop and indirect migration patterns to our knowledge. Overall, this research highlights understudied aspects of the kestrel full annual cycle in the winter and migratory periods, providing insight into possible causes for kestrel declines.

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Snow and land use alter pheasant survival in South Dakota

Cited by 4 publications

References 45 publications

Integrating multiple data sources improves prediction and inference for upland game bird occupancy models

Integrating multiple data sources improves prediction and inference for upland game bird occupancy models

Habitat amount or landscape configuration: Emerging HotSpot analysis reveals the importance of habitat amount for a grassland bird in South Dakota

Annual Survivorship and Movement Ecology of Migrant American Kestrels (Falco sparverius) Overwintering in North Texas

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