Habitat Selection Models Research Articles

Aggregations from using inadvertent social information: a form of ideal habitat selection

Social information in breeding site selection has received extensive study; however, few attempts have been made to link this process to pre‐existing models. We examine the importance of social information to three pertinent models of habitat selection that describe breeding aggregations and spatial patterns: 1) the ideal despotic distribution (IDD) which considers conspecific competition and habitat availability, 2) the perceptual constraints model which accounts for patch selection when animals experience a threshold of undetectable difference in quality, and 3) the “neighbourhood model” which predicts concordance between resources and settlers can be disrupted by conspecific attraction when resources are patchy. These models all predict initial settlers will select a high quality patch first. However, their predictions of subsequent settlement behaviour in remaining patches differ: the IDD predicts subsequent settlers will be distributed regularly, the perceptual constraints model predicts a random distribution, and the neighbourhood model predicts clustering from conspecific attraction. We examined which model best described settlement patterns of bobolink Dolichonyx oryzivorus and savannah sparrow Passerculus sandwichensis, in the context of social information. We observed settlement timing, quantified available resources, and determined where they occurred in the highest (local population “core”) and lowest densities (local population “periphery”). We then assessed whether individuals in the periphery settled in greater concordance with resources or conspecific presence. Core territories were clustered strongly on relevant resources, and these territory holders were older than in the periphery. Peripheral territories were likewise clustered but did not always co‐occur with the best available resources, matching the neighbourhood model prediction that social information may not always direct them to the best sites available. This suggests older individuals used their own experience to locate ideal habitat, whereas younger individuals attempted to aggregate on seemingly ideal habitat by using conspecific location; such information asymmetry due to age can be viewed as an “ideal aggregative distribution”.

Using probability of occurrence to assess potential interaction between wind farms and a residual population of golden eagle Aquila chrysaetos in NW Spain

We evaluate the areas with potential negative impacts in a golden eagle population derived of the development of wind farms. At present, the entire golden eagle Galician population (5–6 pairs) is located within an area of about 2,000 km2. Grid squares of 10 × 10 km UTM in the province were scored for current and future wind turbine density and probability of occurrence of golden eagle. This probability was obtained using cartographic models of habitat selection for two different historic periods. Potential risk index (PRI) was calculated for each grid square by multiplying the wind turbine density score by the probability of occurrence score. With the PRIs obtained a cartographic model of potential impact of wind farms on the golden eagle population was constructed. No significant correlation was observed between current wind turbine density and the probability of occurrence of golden eagle. A significant positive correlation was observed between current and future wind turbine density and the probability of occurrence of golden eagle. The areas with highest potential risk are eastern and the central mountains of Ourense where the species breeds. The risk model presented could be applied to future wind farm proposals and monitor potential interactions of golden eagles with wind farms in the Province of Ourense.

Importance of light, temperature, zooplankton and fish in predicting the nighttime vertical distribution of Mysis diluviana

The opossum shrimp Mysis diluviana (formerly M. relicta) performs large amplitude diel vertical migrations in Lake Ontario and its nighttime distribution is influenced by temperature, light and the distribution of its predators and prey. At one location in southeastern Lake Ontario, we measured the vertical distribution of mysids, mysid predators (i.e. planktivorous fishes) and mysid prey (i.e. zooplank- ton), in addition to light and temperature, on 8 occasions from May to September, 2004 and 2005. We use these data to test 3 different predictive models of mysid habitat selection, based on: (1) laboratory- derived responses of mysids to different light and temperature gradients in the absence of predator or prey cues; (2) growth rate of mysids, as estimated with a mysid bioenergetics model, given known prey densities and temperatures at different depths in the water column; (3) ratio of growth rates (g) and mor- tality risk (μ) associated with the distribution of predatory fishes. The model based on light and temper- ature preferences was a better predictor of mysid vertical distribution than the models based on growth rate and g:μ on all 8 occasions. Although mysid temperature and light preferences probably evolved as mechanisms to reduce predation while increasing foraging intake, the response to temperature and light alone predicts mysid vertical distribution across seasons in Lake Ontario.

FINE‐SCALE HABITAT MODELING OF A TOP MARINE PREDATOR: DO PREY DATA IMPROVE PREDICTIVE CAPACITY

Predators and prey assort themselves relative to each other, the availability of resources and refuges, and the temporal and spatial scale of their interaction. Predictive models of predator distributions often rely on these relationships by incorporating data on environmental variability and prey availability to determine predator habitat selection patterns. This approach to predictive modeling holds true in marine systems where observations of predators are logistically difficult, emphasizing the need for accurate models. In this paper, we ask whether including prey distribution data in fine-scale predictive models of bottlenose dolphin (Tursiops truncatus) habitat selection in Florida Bay, Florida, U.S.A., improves predictive capacity. Environmental characteristics are often used as predictor variables in habitat models of top marine predators with the assumption that they act as proxies of prey distribution. We examine the validity of this assumption by comparing the response of dolphin distribution and fish catch rates to the same environmental variables. Next, the predictive capacities of four models, with and without prey distribution data, are tested to determine whether dolphin habitat selection can be predicted without recourse to describing the distribution of their prey. The final analysis determines the accuracy of predictive maps of dolphin distribution produced by modeling areas of high fish catch based on significant environmental characteristics. We use spatial analysis and independent data sets to train and test the models. Our results indicate that, due to high habitat heterogeneity and the spatial variability of prey patches, fine-scale models of dolphin habitat selection in coastal habitats will be more successful if environmental variables are used as predictor variables of predator distributions rather than relying on prey data as explanatory variables. However, predictive modeling of prey distribution as the response variable based on environmental variability did produce high predictive performance of dolphin habitat selection, particularly foraging habitat.

Land‐use changes may explain the recent range expansion of the Black‐shouldered Kite Elanus caeruleus in southern Europe

Occasional observations of Black‐shouldered Kites Elanus caeruleus in Europe date back to the mid‐19th century, but it was only recorded as a breeding species in the early 1960s in Portugal and a few years later in neighbouring Spain. This recent colonization, possibly from Africa where the species is abundant, may be due to climate change, land‐use changes in southern Europe, or both. As a first step to understanding this range expansion process we have developed a habitat selection model using data from the current strongholds of its European distribution. Comparing the proportion of different habitat types around 46 breeding sites and 45 randomly chosen plots, we have found that the area of cultivated parklands known as dehesas in Spain is a strong predictor of the current distribution range of breeding pairs of Black‐shouldered Kites. Specifically, the percentage of dehesas with planted cereal and a low density of trees (i.e. < 7 trees/ha and thus a savannah‐like habitat) within the study plots explained 44.6% of the residual deviance in our model. The minimal adequate model classified 81.3% of breeding sites and random plots correctly. Our results suggest that Black‐shouldered Kites may have taken advantage of the gradual increase of cultivated dehesas in the second half of the 20th century to expand its range in Europe. This particular type of dehesa is structurally similar to the African savannahs where the species thrives and may offer a higher density of rodents than traditional dehesas, which primarily contain pastureland for livestock ranching.

Habitat selection by feral cats and dingoes in a semi-arid woodland environment in central Australia

Habitat use by feral cats and dingoes was examined within a heterogeneous semi-arid woodland site in central Australia over 2 years. Density estimates of feral cats based on tracks were higher in mulga habitat than in open habitat. Isodar analysis implied that this pattern of habitat use by feral cats was consistent with the consumer-resource model of density-dependent habitat selection, which is an ideal free solution. The reason why mulga supported higher densities of feral cats was unclear. Foraging success of feral cats may be higher in the mulga because the stalk and ambush hunting tactics typically employed by felids are well suited to dense cover. Mulga may also have offered feral cats more protection from dingo predation. Dingo activity was distributed uniformly across habitats. The dingo isodar was statistically non-significant, suggesting that habitat selection by dingoes was independent of density.

Habitat selection by a rare forest antelope: A multi-scale approach combining field data and imagery from three sensors

It can be difficult to further scientific understanding of rare or endangered species that live in inaccessible habitat using traditional methods, such as probabilistic modeling based on field data collection. Remote sensing (RS) can be an important source of information for the study of these animals. A key advantage of RS is its ability to provide information over an animal's complete range, but models incorporating RS data are limited by RS's ability to detect important habitat features. In this study, we focus on the rare, poorly-understood mountain bongo antelope (Tragelaphus euryceros isaaci) which survives in the wild in isolated pockets of montane forest in Kenya. We hypothesize that mountain bongo habitat is multi-scaled. We analyzed field and RS data (derived from SPOT, ASTER, and MODIS) ranging in scale from 0.02–85.93 ha to test our hypothesis. Important microhabitat features were identified through logistic regression models of vegetation structure data collected in plots (0.04 ha) of bongo presence (n=36) and absence (n=90). Models were selected using an information theoretic approach. We analyzed the correlations between microhabitat (four canopy and four understorey structure measures) and RS variables derived using spectral mixture (SMA) and texture analysis; most ASTER and SPOT variables were significantly related with canopy structure variables (max|r|=0.56), but correlations between understorey structure and all but two RS variables were insignificant. Further logistic regression modeling showed that combining field microhabitat (primarily understorey structure variables) and larger-scaled RS measures (ASTER spectral mixture analysis variables aggregated to 450 m (20.25 ha)) provided superior models of bongo habitat selection than those based on field or RS data only. The results demonstrate that: 1) forest canopy characteristics at scales of ~20 ha and understorey structural conditions at the micro-scale of 0.04 ha were the most important features influencing bongo habitat selection; 2) models for predicting bongo habitat distribution must incorporate both micro- and macro-habitat variables; 3) optical RS data may characterize important micro-scale canopy variables with reasonable accuracy, but are ineffective for detecting understorey features (unless alternative techniques such as forest structural indices can be successfully applied); 4) RS and field data are both essential for understanding bongo habitat selection. The technique employed here for understanding this rare antelope's habitat selection may also be applied in studies of other large herbivores.

The effects of perceived mortality risk on habitat selection in a terrestrial salamander

Animals select microhabitats based on food availability, physiological cost, and mortality risk relative to other available habitats. The best habitat selection models take into account a trade-off between predation risk and foraging success. We investigated the effect of simulated predation on microhabitat use in light of differences in prey availability in Plethodon cinereus, a small terrestrial salamander which displays nighttime plant-climbing behavior. We tested the hypothesis that plant climbing is a predator avoidance behavior in P. cinereus by causing autotomization of salamanders’ tails to simulate attempted predation and subsequently tracking them with fluorescent powder. We found that, on average, simulated predation increased the maximum height climbed. To ensure that salamanders were not climbing to access greater numbers of prey, we measured prey abundance on and above the ground and found more prey on the ground than on plants. Finally, we conducted observations of unmanipulated individuals of P. cinereus and found that weather variables affected the height climbed and males climbed higher than females, perhaps due to lower energetic costs in males. We conclude that salamanders use the plant habitat as a refuge from predation despite reduced foraging potential and increased physiological cost.

Greater Sage‐Grouse Winter Habitat Selection and Energy Development

AbstractRecent energy development has resulted in rapid and large‐scale changes to western shrub‐steppe ecosystems without a complete understanding of its potential impacts on wildlife populations. We modeled winter habitat use by female greater sage‐grouse (Centrocercus urophasianus) in the Powder River Basin (PRB) of Wyoming and Montana, USA, to 1) identify landscape features that influenced sage‐grouse habitat selection, 2) assess the scale at which selection occurred, 3) spatially depict winter habitat quality in a Geographic Information System, and 4) assess the effect of coal‐bed natural gas (CBNG) development on winter habitat selection. We developed a model of winter habitat selection based on 435 aerial relocations of 200 radiomarked female sage‐grouse obtained during the winters of 2005 and 2006. Percent sagebrush (Artemisiaspp.) cover on the landscape was an important predictor of use by sage‐grouse in winter. The strength of habitat selection between sage‐grouse and sagebrush was strongest at a 4‐km2scale. Sage‐grouse avoided coniferous habitats at a 0.65‐km2scale and riparian areas at a 4‐km2scale. A roughness index showed that sage‐grouse selected gentle topography in winter. After controlling for vegetation and topography, the addition of a variable that quantified the density of CBNG wells within 4 km2improved model fit by 6.66 Akaike's Information Criterion points (Akaike wt = 0.965). The odds ratio for each additional well in a 4‐km2area (0.877; 95% CI = 0.834‐ 0.923) indicated that sage‐grouse avoid CBNG development in otherwise suitable winter habitat. Sage‐grouse were 1.3 times more likely to occupy sagebrush habitats that lacked CBNG wells within a 4‐km2area, compared to those that had the maximum density of 12.3 wells per 4 km2allowed on federal lands. We validated the model with 74 locations from 74 radiomarked individuals obtained during the winters of 2004 and 2007. This winter habitat model based on vegetation, topography, and CBNG avoidance was highly predictive (validationR2= 0.984). Our spatially explicit model can be used to identify areas that provide the best remaining habitat for wintering sage‐grouse in the PRB to mitigate impacts of energy development.

Modelling the habitat requirements of leopard Panthera pardus in west and central Asia

Summary Top predators are seen as keystone species of ecosystems. Knowledge of their habitat requirements is important for their conservation and the stability of the wildlife communities that depend on them. The goal of our study was to model the habitat of leopard Panthera pardus in west and central Asia, where it is endangered, and analyse the connectivity between different known populations in the Caucasus to enable more effective conservation management strategies to be implemented. Presence and absence data for the species were evaluated from the Caucasus, Middle East and central Asia. Habitat variables related to climate, terrain, land cover and human disturbance were used to construct a predictive model of leopard habitat selection by employing a geographic information system (GIS) and logistic regression. Our model suggested that leopards in west and central Asia avoid deserts, areas with long‐duration snow cover and areas that are near urban development. Our research also provides an algorithm for sample data management, which could be used in modelling habitats for similar species. Synthesis and applications. This model provides a tool to improve search effectiveness for leopard in the Caucasus, Middle East and central Asia as well as for the conservation and management of the species. The model can predict the probable distribution of leopards and the corridors between various known populations. Connectivity patterns can be used to facilitate corridor planning for leopard conservation, especially in the Caucasus, where the leopard is a top priority conservation species. Also, as top predators are often associated with high biodiversity, the leopard habitat model could help to identify biodiversity hotspots. The protection of biodiversity hotspots is seen as the most effective way to conserve biodiversity globally.

Habitat selection models for European wildcat conservation

Populations of the European wildcat ( Felis silvestris) are only slowly recovering in Central Europe after a severe decline in the last centuries and require specific conservation plans in many areas. However, detailed information on wildcat occurrence and habitat requirements is still scarce and controversial. We present a fine-scale habitat selection model for wildcats based on detailed species and land use information and evaluate its accuracy to predict habitat distribution in new areas. We analysed habitat use within home ranges using single locations of 12 radio-tracked individuals from south western Germany. Several competing models were fitted and compared using generalised linear mixed models (GLMM) and information-theoretic approaches. Radio-tracking data of 9 and 10 wildcats from two distant areas were used to evaluate the models. The selected model predicted habitat associated to close distance to forest, watercourses and meadows and a critical distance to villages, single houses and roads. To predict area suitable for home ranges we superimposed rules derived from home range attributes at a higher level of selection. Predictions from the combination of the fine-scale habitat model and home range rules matched well with more than 2000 wildcat observations of south-western Germany. We discuss the application of the model in wildcat conservation for finding potential reintroduction sites, identifying small isolated populations and aiding in the evaluation of the needs of mitigation and compensation within the scope of the European Habitats Directive.

Wetland Selection by Mallard Broods in Canada's Prairie‐Parklands

ABSTRACT Although brood survival has a pronounced effect on population growth in mallards (Anas platyrhynchos), knowledge of brood ecology is more limited than for other vital rates. During 1993–1997 we collected wetland selection data from 210 radiomarked mallard broods on 15 study areas located throughout the Canadian Prairie‐Parklands. We used information‐theoretic approaches to select the best‐approximating model of habitat selection in relation to wetland characteristics. Wetland permanence, cover type, width of flooded emergent vegetation, and interactions between these variables and date, moisture level, and dominant species of emergent vegetation were all important predictors of wetland selection. Mallard broods selected deeper wetlands, especially later in the brood‐rearing season. Mallard broods also selected wetlands with large central expanses of open water and wide peripheral zones of flooded emergent cover. These habitat characteristics can most easily be met in landscapes that already contain an abundance and diversity of natural wetland habitats. Where such wetlands are unavailable, restoration or management of deeper wetlands may be necessary to meet the habitat requirements of mallard ducklings.

Robust predictions of species distribution: Spatial habitat models for a brood parasite

Empirical habitat selection models aid managers by predicting distribution patterns, but recent critiques urge a fuller application of available methods. We used a suite of habitat modelling techniques to identify factors influencing the distribution of brown-headed cowbirds ( Molothrus ater), a brood parasite capable of causing population declines in some host species. We used data on cowbird occurrence in the Southern Gulf Islands, Canada, to estimate the influence of landscape features on cowbird distribution beginning with a set of a priori models based on literature. Best models were also constructed using stepwise logistic and autologistic regression, and using only widely available data, and these models and their predictive maps were then evaluated. Cowbirds were predicted by proximity to potential feeding areas and landscape context. The best logistic model included landcover, cattle, and distance to urban area and agriculture. Model performance was improved by autologistic regression, and models restricted to widely available data were only slightly poorer. All models were robust to internal validation using bootstrapping and when compared to an independent empirical dataset of parasitism rates of a host on 12 sites monitored for up to 8 years. Our predictive maps for cowbirds should help land managers make spatial predictions about cowbird impact on particular hosts, as well as evaluate land-use decisions that could influence cowbird abundance and host fitness. We evaluated several approaches to habitat modelling of interest to conservationists and make suggestions for future studies when spatial predictions are likely to affect decisions in conservation and land use planning.

Nest selection by snow petrels Pagodroma nivea in East Antarctica : Validating predictive habitat selection models at the continental scale

Little is known on the factors controlling distribution and abundance of snow petrels in Antarctica. Studying habitat selection through modeling may provide useful information on the relationships between this species and its environment, especially relevant in a climate change context, where habitat availability may change. Validating the predictive capability of habitat selection models with independent data is a vital step in assessing the performance of such models and their potential for predicting species’ distribution in poorly documented areas. From the results of ground surveys conducted in the Casey region (2002–2003, Wilkes Land, East Antarctica), habitat selection models based on a dataset of 4000 nests were created to predict the nesting distribution of snow petrels as a function of topography and substrate. In this study, the Casey models were tested at Mawson, 3800 km away from Casey. The location and characteristics of approximately 7700 snow petrel nests were collected during ground surveys (Summer 2004–2005). Using GIS, predictive maps of nest distribution were produced for the Mawson region with the models derived from the Casey datasets and predictions were compared to the observed data. Models performance was assessed using classification matrixes and Receiver operating characteristic (ROC) curves. Overall correct classification rates for the Casey models varied from 57% to 90%. However, two geomorphologically different sub-regions (coastal islands and inland mountains) were clearly distinguished in terms of habitat selection by Casey model predictions but also by the specific variations in coefficients of terms in new models, derived from the Mawson data sets. Observed variations in the snow petrel aggregations were found to be related to local habitat availability. We discuss the applicability of various types of models (GLM, CT) and investigate the effect of scale on the prediction of snow petrel habitats. While the Casey models created with data collected at the nest scale did not perform well at Mawson due to regional variations in nest micro-characteristics, the predictive performance of models created with data compiled at a coarser scale (habitat units) was satisfactory. Substrate type was the most robust predictor of nest presence between Casey and Mawson. This study demonstrate that it is possible to predict at the large scale the presence of snow petrel nests based on simple predictors such as topography and substrate, which can be obtained from aerial photography. Such methodologies have valuable applications in the management and conservation of this top predator and associated resources and may be applied to other Antarctic, Sub-Antarctic and lower latitudes species and in a variety of habitats.

Geostatistics reveal the scale of habitat selection

We developed a new model of habitat selection to detect the extent at which organisms respond to habitat. Based on the premise that selection of a resource entails a change in its variance, the model compares two variograms: sites used by animals versus the general environment. With an artificial landscape, we explored this model by varying the extent of perception by animals, which was revealed as the approximate point of maximum divergence in variance. Selection for greater resource abundance produced variograms with lower variance than the general environment, whereas selection for extremes of resource abundance generated the converse. We applied the model to winter resource selection of muskoxen. Variograms in forage abundance diverged sharply at 200 m, consistent with a conventional analysis that demonstrated the strongest selection at that scale. The model appears widely applicable and capable of revealing the spatial scales at which organisms react to their environment.

Sequential settlement and site dependence in a migratory raptor

Habitat selection models, such as the ideal free, ideal despotic, site-dependent, and conspecific cuing models, are of great importance to behavioral ecologists given their capability to predict habitat distributions and to link individual behavior to population processes. However, there have been relatively few field tests of their predictions. We tested the 4 models by studying the process of sequential settlement on territory in 2 distant populations of a migratory raptor, the black kite Milvus migrans. Results were mainly consistent with the site-dependent model: on arrival, kites settled on progressively lower quality territories, and earlier arriving individuals were older, larger, and in better body condition than later arriving ones, leading to a state-dependent arrival sequence also predicted by a previous theoretical model of settlement pattern. Occupation of superior territories by superior phenotypes resulted in cascading advantages for earlier arriving individuals in terms of subsequent reproductive performance. At the population level, the populations expanded/retracted from lower quality sites during population increases/declines. The above scenario was consistent across the 2 populations, and a review of the literature uncovered a remarkably consistent picture of state-dependent arrival, progressive monopolization of best quality sites, and cascading effects on subsequent breeding performance. We propose as a general paradigm of sequential settlement the following process: 1) arrival date is a reliable surrogate of phenotype quality, 2) early-arriving individuals have preferential access to the best quality sites and partners, 3) the above conditions cascade into a number of benefits ultimately related to higher fitness for earlier arriving individuals.

Habitat selection and breeding success in a forest-nesting Alcid, the marbled murrelet, in two landscapes with different degrees of forest fragmentation

We studied habitat selection and breeding success in marked populations of a protected seabird (family Alcidae), the marbled murrelet (Brachyramphus marmoratus), in a relatively intact and a heavily logged old-growth forest landscape in south-western Canada. Murrelets used old-growth fragments either proportionately to their size frequency distribution (intact) or they tended to nest in disproportionately smaller fragments (logged). Multiple regression modelling showed that murrelet distribution could be explained by proximity of nests to landscape features producing biotic and abiotic edge effects. Streams, steeper slopes and lower elevations were selected in both landscapes, probably due to good nesting habitat conditions and easier access to nest sites. In the logged landscape, the murrelets nested closer to recent clearcuts than would be expected. Proximity to the ocean was favoured in the intact area. The models of habitat selection had satisfactory discriminatory ability in both landscapes. Breeding success (probability of nest survival to the middle of the chick rearing period), inferred from nest attendance patterns by radio-tagged parents, was modelled in the logged landscape. Survivorship was greater in areas with recent clearcuts and lower in areas with much regrowth, i.e. it was positively correlated with recent habitat fragmentation. We conclude that marbled murrelets can successfully breed in old-growth forests fragmented by logging.

Broad-scale habitat use by red-legged partridge (Alectoris rufa) in a low-density area in northwestern Spain

We developed broad-scale habitat selection models for the distribution of red-legged partridge Alectoris rufa in a low-density area in northwestern Spain, the Baixa-Limia site of community importance (SCI). The fieldwork consisted of ground surveys in 1 × 1 km squares. For habitat selection analysis, we used a 2 × 2 km grid integrating the information obtained in the 1 × 1 km squares. As predictors we used environmental variables measured on digital 1:50,000 scale cartography using a geographical information system (GIS). The red-legged partridge was scarce in the study area. The logistic regression analysis carried out on data from the squares with probable and confirmed breeding included the area of scrubland and pastureland with a positive sign. Using the breeding index category (BIC) three variables produced a slightly positive response: area of scrubland and pastureland, length of border between scrublands and forests, and length of border between forests and dams. The difficulty for modelling the habitat selection of this species could be due to human activities (hunting, habitat loss, restocking of hunt species), and may have modified their habitat preferences. Furthermore, the occupation of suboptimal habitats would distort the real habitat preferences.

Habitat selection models to account for seasonal persistence in radio telemetry data

Models for the analysis of habitat selection data incorporate covariates in an independent multinomial selections model (McCracken et al. 1998) Ramsey and Usner 2003 and an extension of that model to include a persistence parameter (2003). In both cases, all parameters are assumed to be fixed through time. Radio telemetry data collected for habitat selection studies typically consist of animal relocations through time, suggesting the need for an extension to these models. We use a Bayesian approach that allows for the habitat selection probabilities, persistence parameter, or both, to change with season. These extensions are particularly important when movement patterns are expected to differ seasonally and/or when availabilities of habitats change throughout the study period due to weather or migration. We implement and compare the models using radio telemetry data for westslope cutthroat trout in two streams in eastern Oregon.

RETURN RATE, FIDELITY, AND DISPERSAL IN A BREEDING POPULATION OF FLAMMULATED OWLS (OTUS FLAMMEOLUS)

Abstract We estimated annual return rate, fidelity, and breeding dispersal in a migratory population of Flammulated Owls (Otus flammeolus) in central Colorado. Return rates, based on capture-recapture histories of 39 males and 52 females from 1981 to 2003, were higher for males (84%) than for females (45%). Annual recapture probability was higher for females, because breeders are easier to capture than nonbreeders and females always attempted to nest, whereas some males were unpaired (did not nest) for up to four years. Territory fidelity was male biased (92%, vs. 56% for females, adjusted for undetected emigration), and mean tenure on territories was more than twice as long for males as for females. Females, but not males, had lower return rates to territories in the year following nesting failure compared with females whose nests were successful. Most males appeared to occupy one territory their entire reproductive lives, countering predictions of habitat-selection models that individuals should move to...