scholarly journals Nest Distribution of Four Priority Raptor Species In Colorado

2021 ◽  
Author(s):  
Kevin Aagaard ◽  
Reesa Yale Conrey ◽  
James H. Gammonley

ABSTRACT Raptors face threats such as habitat modification, climate change, and environmental pollutants in many parts of the western USA, where rapid human population growth exacerbates such pressures. However, information about distribution of raptor nests at broad spatial scales that could inform conservation efforts is lacking. To provide a contemporary estimate of nest distribution of four raptor species of special conservation concern (Bald Eagle [Haliaeetus leucocephalus], Ferruginous Hawk [Buteo regalis], Golden Eagle [Aquila chrysaetos], and Prairie Falcon [Falco mexicanus]) throughout Colorado, we used a statewide database of raptor nesting locations to inform species distribution models for monitoring and management efforts. We used generalized linear models to identify the relationship between nest locations and explanatory covariates relating to land cover, temperature, topography, and prey distribution. We investigated the effect of different methods for selecting the sample of locations available to raptors, comparing four selection frames: sampling from the observed locations of the target-group (i.e., other raptor nests), sampling from within a spatial buffer around observed locations, sampling from outside of the same buffer, or complete random sampling of the background locations without respect to observations. Out-of-sample validation techniques indicated strong predictive accuracy of our models. Each raptor species was best represented by a different one of the four approaches to sample available locations, refuting our expectation that models accounting for bias would perform better than those that did not. Our findings were consistent with generally understood habitat associations of these species. These models can be used to identify hot spots with high relative probability of use by breeding raptors and to inform future monitoring practices that use a standardized, stratified sampling design.

Oecologia ◽  
2021 ◽  
Author(s):  
Felicity A. Edwards ◽  
David P. Edwards ◽  
Keith C. Hamer ◽  
Tom M. Fayle

AbstractTropical rainforest disturbance and conversion are critical drivers of biodiversity loss. A key knowledge gap is understanding the impacts of habitat modification on mechanisms of community assembly, which are predicted to respond differently between taxa and across spatial scales. We use a null model approach to detect trait assembly of species at local- and landscape-scales, and then subdivide communities with different habitat associations and foraging guilds to investigate whether the detection of assembly mechanisms varies between groups. We focus on two indicator taxa, dung beetles and birds, across a disturbance gradient of primary rainforest, selectively logged rainforest, and oil palm plantations in Borneo, Southeast Asia. Random community assembly was predominant for dung beetles across habitats, whereas trait convergence, indicative of environmental filtering, occurred across the disturbance gradient for birds. Assembly patterns at the two spatial scales were similar. Subdividing for habitat association and foraging guild revealed patterns hidden when focusing on the overall community. Dung beetle forest specialists and habitat generalists showed opposing assembly mechanisms in primary forest, community assembly of habitat generalists for both taxa differed with disturbance intensity, and insectivorous birds strongly influenced overall community assembly relative to other guilds. Our study reveals the sensitivity of community assembly mechanisms to anthropogenic disturbance via a shift in the relative contribution of stochastic and deterministic processes. This highlights the need for greater understanding of how habitat modification alters species interactions and the importance of incorporating species’ traits within assessments.


2021 ◽  
Author(s):  
Devin R de Zwaan ◽  
Niloofar Alavi ◽  
Greg W Mitchell ◽  
David R Lapen ◽  
Jason Duffe ◽  
...  

Effective conservation planning often requires difficult decisions when at-risk species inhabit economically valuable landscapes or if the needs of multiple threatened species do not align. In the agriculture-dominated landscape of eastern Ontario and southwestern Quebec, Canada, conflicting habitat requirements exist between threatened grassland birds benefiting from certain agriculture practices and those of a diverse woodland bird community dependent on forest recovery. Using multi-scale species distribution models with Breeding Bird Survey (BBS) data, we assessed habitat suitability for 8 threatened grassland and forest specialists within this region. We also identified landscapes that jointly maximize occurrence of the 8 focal species and diversity of the overall grassland and forest communities. Influential habitat associations differed among species at the territory (200m radius) and landscape level (1km), highlighting the importance of considering multiple spatial scales. Species diversity was maximized when forest or grassland/pasture cover approached 40-50%, indicating a positive response to land cover heterogeneity. We identified species diversity hotspots near Lake Huron, as well as along the shore and southeast of the St. Lawrence River. These areas represent mosaic landscapes, balancing forest patches, wetland, grassland/pasture, and row crops such as corn, soybean, and cereals. Despite drastic landscape changes associated with agroecosystems, we demonstrate that targeted habitat protection and enhancement that prioritizes land cover diversity can maximize protection of bird communities with directly contrasting needs. We highlight multiple pathways to achieve this balance, including forest retention or separating row crops with hedgerows and wooded fence-lines, improving flexibility in conservation approaches.


2017 ◽  
Vol 28 (3) ◽  
pp. 375-389 ◽  
Author(s):  
LUIS TAPIA ◽  
ADRIÁN REGOS ◽  
ALBERTO GIL-CARRERA ◽  
JESÚS DOMÍNGUEZ

SummaryThe aim of this study was to assess the temporal transferability of species distribution models (SDMs) and their potential implications for bird conservation. We quantified the loss and fragmentation of Montagu’s Harrier Circus pygargus and Common Kestrel Falco tinnunculus habitats over 13 years (2001–2014) in a highly dynamic landscape in north-western Spain. For this purpose, priority habitats for the target species were modelled at four different spatial scales using an ensemble forecasting framework. To explore the temporal transferability of our ensemble predictions, the models were back-projected to the land cover conditions in 2001 and evaluated using historical occurrence data. In addition, models calibrated with historical data were projected to the land cover conditions in 2014 and evaluated using updated occurrence data. Changes in availability and connectivity of suitable habitats between both years were estimated at four spatial scales from a set of widely-used indicators. SDMs showed a good predictive accuracy but with limited temporal transferability due to changes in the species-habitat relationships between 2001 and 2014. The results showed a decrease in the avaliability of suitable habitats of 33.4% and 47.7% for Montagu’s Harrier and Common Kestrel, respectively; with the subsequent increase in their fragmentation. However, our estimates were found to be strongly dependent on the scale of analysis and model transferability. Changes in habitat availability and connectivity ranged from -48% to +54% for Montagu’s Harrier, and from +116% to +5.6% for Common Kestrel. We call for caution when using SDMs beyond the model calibration time period to guide bird conservation. This is especially important for raptors, often characterised by low population sizes and large home ranges, and particularly sensitive to unstable, highly dynamic environmental conditions. In light of these results, specific, long-standing monitoring protocols remain essential to ensure accurate modelling performance and reliable future projections.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Menelaos Pavlou ◽  
Gareth Ambler ◽  
Rumana Z. Omar

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.


2019 ◽  
Vol 76 (7) ◽  
pp. 2349-2361
Author(s):  
Benjamin Misiuk ◽  
Trevor Bell ◽  
Alec Aitken ◽  
Craig J Brown ◽  
Evan N Edinger

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.


1996 ◽  
Vol 6 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Julia K. Parrish ◽  
Robert T. Paine

SummarySeabird populations suffer from a variety of natural and human-induced sources of mortality and loss of lifetime reproductive output. On the outer coast of Washington State, Common Murre Uria aalge populations have been in decline for approximately the last decade and are currently reproductively active only at Tatoosh Island. These murres nest in two basic habitat types: crevices (25% of the population) and larger cliff-top subcolonies (75%). Murres in cliff-top subcolonies have suffered dramatic reductions in reproductive success in recent years relative to conspecifics nesting in the crevices, primarily due to egg predation by Glaucous-winged Gulls Larus glaucescens and Northwestern Crows Corvus caurinus, facilitated by the presence of Bald Eagles Haliaeetus leucocephalus. Because predator removal is not feasible and creation of additional crevice habitat is difficult, expensive and potentially ineffective, we have designed a temporary habitat modification (the “silk forest”) which replaces the natural vegetation cover and modifies the interaction between murres and eagles. Within the test subcolony, murres nesting under and immediately adjacent to the silk forest produced nearly twice as many eggs per square metre as their conspecifics nesting in adjacent exposed-ground areas.


2018 ◽  
Vol 32 (3) ◽  
pp. 240-245
Author(s):  
Patrick J. Sullivan ◽  
Olufemi O. Fasina ◽  
Andrew C. Cushing. BVSc

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