From Tree-cavity to Landscape: Habitat Preferences and Fitness Operates Across Scales for an Old Relict Species of Southern South-america

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Altamirano, Fernando J. Novoa, Zoltan Von Von Bernath, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3914394/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Animals select their habitats from available resources in a way that should maximize fitness, and thus habitat preferences are generally predicted to be adaptive. However, there may be a mismatch between habitat preferences and fitness due to factors such as limited availability or disturbance of breeding habitats. In this study, we examine whether preferred nesting habitat attributes are linked to fitness (nesting success and number of fledglings) of White-throated treerunner ( Pygarrhichas albogularis ), an obligate excavator and tree cavity nester across four spatial scales: ( 1 ) cavity, ( 2 ) nest-tree, ( 3 ) forest-stand, and ( 4 ) landscape. During eight breeding seasons (October to February), between 2010 and 2018, we found and monitored 65 treerunner nests in Andean Temperate Forests, Chile. Our results show a multiscale response for both habitat preferences and fitness: both nest-tree and landscape scales were the most influential variables for nesting habitat preferences, while all habitat scales influenced fitness. The probability that a given habitat is used for nesting increased with larger trees, advanced tree decay classes, and forest cover. Nesting success was positively related with cavity entrance diameter, height, and distance from the forest edge. On the other hand, the number of fledglings decrease with larger trees and increase with decay class. Our findings suggest a match between habitat preferences and fitness. Finally, treerunners prefer areas with a relatively high forest cover and their nesting success increased with relatively lower tree density, suggesting that old-growth forests comprise the best integration of multiscale habitat attributes for this species. Biological sciences/Ecology/Biodiversity Biological sciences/Ecology/Conservation Biological sciences/Ecology/Forest ecology Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Animals select their habitats from available resources in a way that should maximize fitness 1 . Thus, it is expected that habitat attributes associated with habitat selection will also be those most strongly linked to fitness 2 . Habitat preferences (i.e. the final pattern of habitat used with respect to its availability) will generally be adaptive, under the pressures of natural selection, if a species obtains maximum fitness 3,4 . However, there may be a mismatch between habitat preferences and fitness by factors such as temporal changes in habitat features after territory establishment 5 and rapid disturbance of reproductive habitats 6,7 . Habitat attributes influence fitness via the costs (e.g. predation risk) and benefits (e.g. food availability) of habitat preferences 8 . There is evidence that avian habitat preferences are scale-dependent and hierarchical phenomena 9 . Scales at which habitat selection may occur range from microsites selected for nesting or foraging to the entire geographic range of a species 10 . Thus, differences between nesting and available sites reported for excavators (i.e. species that excavate their nesting cavities in trees) may be because there are multiple scales operating in nesting site selection processes, from fine to coarse scales 11,12 . Excavators should assess, for example, the tradeoff between a secure nesting substrate for excavation and the distance to a foraging area 13 . Multiscale studies allow identifying important scales concerning individual perception of their habitats, otherwise difficult to detect when knowledge of the ecology of the study species is limited 14 . The White-throated treerunner ( Pygarrhichas albogularis King 1831), is a poorly known Furnariid endemic to South American temperate and Mediterranean ecosystems, mainly found in central and southern regions of Chile and Argentina 15 . This species is considered a “living fossil” or “old relict” as it is the only living species of the genus Pygarrhichas 16,17 . White-throated treerunner is one of the four species of tree cavity excavators in these ecosystems 18 . As an excavator, this species relies on habitats with presence of trees suitable for excavation 19 . Although this species has been suggested as a key habitat facilitator for several avian and mammalian cavity-nesting species in southern South America 20,21 , there is only one study on the ecology of treerunners and focused on foraging use of tree species 22 . There are occasional community level studies that included treerunners as part of an avian assemblage 23–32 . Thus, this research advances the knowledge exploring nesting habitat preference and fitness of treerunners in the centre of its distribution. In this study we examine the nesting tree preference of treerunners and whether their preferred habitat attributes are linked to fitness through a two-step modelling approach. First, we assess habitat preferences analyzing the link between habitat attributes and preferences of nesting sites at three spatial scales: ( 1 ) nest-tree scale, ( 2 ) forest-stand scale, and ( 3 ) landscape scale. Second, we assess whether habitat attributes of preferred reproduction sites are linked to fitness (represented as nesting success and number of fledglings) at the same three spatial scales, adding cavity characteristics as a fourth smaller spatial scale in the analysis. We hypothesize that ( 1 ) preferred reproductive habitat attributes differ to those of surrounding available habitat and that ( 2 ) habitat attributes at each spatial scale are linked to improved fitness, and ( 3 ) there is a match between attributes of habitat preferred and fitness. This study delivers better understanding of forests attributes that must be maintained to ensure habitat for this and other coexisting cavity nester species in south temperate forests 31 . Results Between 2010 and 2018, we located and monitored 65 White-throated treerunner ’ nests (273 to 1,342 m of elevation), 84.6% of which were excavated in Nothofagus trees (Table 2). In average, treerunners used mid-size trees for nesting (DBH = 40.6 cm) within a broad range of trees (DBH range: 14.1 - 123.1 cm). Old dead trees with advanced decay (55%) and live unhealthy trees (35%) contained the great majority of its nests, with only 8% and 2% for recently dead and live healthy trees, respectively (Table 2). When we look at the specific substrate decay, 80% of the nests where in old dead substrates, and only 14% and 6% in live unhealthy and recently dead substrates, respectively. Fifty-eight nests (89.2%) were in freshly excavated cavities. White-throated treerunner laid an average of 3 eggs (range: 1 – 5 eggs). The overall nest survival was 75.03% (95% CI = 62.77 – 89.62%; n = 61 including only nests with known outcome). Regarding unsuccessful nests (n = 10), 6 failed because of predation, 3 were abandoned, and 1 nest failed because eggs were not viable (Table 2). Breeding habitat preferences across spatial scales Variation in the use of breeding habitat was best predicted by a model that included two tree-nest scale variables (DBH and tree decay), one forest-stand scale variable (basal area), and one landscape scale variable (forest area; Table 3). However, basal area was not significant ( b = 4.64 ± 3.04, p > 0.05). The probability that a given habitat is used for nesting increased with larger trees, advanced tree decay, and forest area. Tree decay was the strongest predictor of nest presence, followed by forest area, and DBH. Treerunners avoided healthy living trees ( b = -10.10 ± 2.86, p < 0.01), and the probability of nesting in a tree increased positively with each increasing decay class (Fig. 2A): unhealthy living trees ( b = 3.53 ± 1.26, p < 0.01), recently dead trees ( b = 3.76 ± 1.46, p = 0.01), and old dead trees ( b = 7.35 ± 1.64, p < 0.01). Even when both forest area and DBH are positively associated with preferred breeding habitats, GLMMs showed that the extension of forest area ( b = 4.09 ± 2.11, p = 0.05, marginally significant; Fig. 2B) influenced 82 times more than DBH ( b = 0.04 ± 0.02, p = 0.01). Fitness outputs For nesting success, there were two spatial scales included in the best models: cavity scale (entrance diameter and height) and landscape scale (forest edge, Table 3). Entrance diameter and forest edge had a positive effect on nesting success (daily survival rate). Nests with bigger entrances and further from the forest edge were more successful. Height was positively and marginally associated with nesting success ( b = 1.093 95% CI = 0.241 – 2.837, p = 0.05; Fig. 3A). Regarding the number of fledglings produced, the final model only had parameters at the tree scale (DBH and decay class). DBH was negatively associated with number of fledglings ( b = -0.263; CI = -0.508 – -0.021; Table 3; Fig. 3B). Decay class had an important effect on number of fledglings, being positively associated with unhealthy living trees ( b = 3.180) and with recently dead trees ( b = 0.798), while long dead trees were negatively associated with the number of fledglings ( b = -0.403) (Fig. 3C). Discussion We found that both habitat preferences for nesting and nesting success measures for White-throated treerunner operate across multiple spatial scales. Similar to previous studies using multiscale approaches 7,13,33 , at least one variable at each scale influenced nest site preferences and/or fitness (number of fledglings and nesting success) for our study species. Both tree and landscape scales were the most important variables influencing nesting habitat preferences, while cavity, forest-stand, and landscape scale variables were the most important in our assessment of nesting success (daily survival rate and number of fledglings). White-throated treerunner showed preferences for areas with a relatively high forest cover and that their nesting success increased in forests with a relatively low density of trees. This may suggest that old-growth forests, with extensive areas of forest with relatively less, but larger, trees compared to second-growth forests 34 , contain the best complement of multiscale variables for this species. White-throated treerunner tends to excavate new cavities (89%) every nesting season, with the remaining 11% consisting of reused cavities, all of which were excavated by this species during previous years. Thus, we have determined that this is a primary cavity-nester (i.e. obligate excavators; mainly excavates their own cavities, but occasionally use existing excavated cavities; 35 . Breeding pairs might use existing cavities depending on food and/or nest predation risk (e.g. in case their breeding attempt failed in their fresh cavity). Similarly to Red-naped Sapsuckers ( Sphyrapicus nuchalis ), which mostly excavate their own cavities, but about 11–12% reuse existing cavities, mainly cavities that were excavated in previous years 36 . White-throated treerunner are considered to have similar breeding behavior to nuthatches in North America, but Red-Breasted nuthatch ( Sitta canadensis ) tend to be facultative excavators (i.e. excavate about 50% of their nests, excavation rates vary annually; Aitken et al. 2002, Norris & Martin 2012) 38,39 . Thus, based on our study, treerunners are more extensively primary cavity excavators like Red-naped Sapsuckers or Downy Woodpeckers ( Dryobates pubescens ) rather than nuthatches that are facultative excavators 19 . White-throated treerunner nesting habitat preferences were mainly associated with variables acting at both tree and landscape scales, preferring relatively larger trees in advanced decay classes present in forested areas. This partially agrees with the results found for functionally similar species in the northern Hemisphere, such as Red-naped Sapsucker ( Sphyrapicus nuchalis ) 13 . The latter species preferred nesting habitats associated with landscape scale attributes, similar to treerunners, but only tree scale attributes were linked to fitness 13 . This suggests a hierarchical process of habitat selection in White-throated treerunner 40 , in which several factors, at different spatial scales, influence the decision of choosing a given nest-site 7 . For example, the main trophic resource for treerunners are forest-dwelling arthropods in adult and larvae states 41,42 , which are probably more abundant in multi-stratified forested landscapes with presence of all tree decay classes 43 . On the other hand, this species is choosing variables at finer scale for nesting too. Like other cavity-nesting species in the study area 29 , tree scale attributes are the most influential factors for treerunners. This finding is similar to results reported for several primary cavity nesters such as woodpeckers in temperate forests of North America 19 and in tropical Atlantic forests of South America 44 , where excavators strongly select live unhealthy and standing dead trees as nesting sites. White-throated treerunner showed high values of nesting success, with 75% of its nests being successful. This high percentage strongly contrasts with the overall nesting success of avian communities in tropical forests of Brasil (42%) 45 and Dominican Republic (34%) 46 , but similar to other cavity nesters and excavators such as Red-Breasted nuthatch (84%) 47 , and Red-naped Sapsuckers (91%) 13 . We found that the entrance diameter of cavities was positively associated with nesting success, a counterintuitive finding, which might be associated with adult ability to successfully breed; and thus, stronger adults might be able to protect the nest and excavate larger cavities. Furthermore, cavity-nest predator assemblage in our study area is diverse but composed by relatively large animals, with Leopardus guigna , Dromiciops gliroides, Milvago chimango, Caracara plancus, Glaucidium nana , and Rattus rattus being the main predators 48 . Thus, even when predation was the main cause of nest failure, small differences in entrance diameter may not increase predation risk. On the other hand, and because the nesting substrate that treerunners choose for excavation is usually soft, excavate cavities higher in the forest might reduce the predation risk by terrestrial predators. Nesting success increased with the distance from the forest edge, suggesting that the selection of breeding habitats (selecting sites with high forest cover) is adaptive, and that decision is being translated in more breeding success 13 . Second-growth forests contain a larger number of trees and more chance to be close to edge compared to old-growth forests, as well as less options to nests higher in the canopy, suggesting that the first may not be ideal habitats to breed for White-throated treerunners. The number of fledglings of White-throated treerunners was associated only with tree scale attributes (i.e. DBH and decay). The latter is partially similar to the productivity of Red-naped Sapsuckers in the United States 13 , where cavity and tree scales were the most important attributes associated with the number of fledglings. Breeding outputs were positively related to southeastern orientation of cavity entrance for Red-naped Sapsuckers, while we did not find any relation between cavity aspect and fitness for treerunners. Our finding showing a lack of association between cavity orientation and fitness matches with recently published results for South American excavators, in which there is no general pattern of cavity orientation 49 . Instead, and unlike nesting success, the number of fledglings was associated with tree scale, being negatively associated with tree DBH. This can be responding to elevation, due to larger trees are present mainly at high elevations, where shorter breeding seasons can be translated in smaller clutch sizes 50 . On the other hand, we found that unhealthy live trees and recently dead trees increased the number of fledglings, while long dead trees negatively affected the number of fledglings. This might be also responding to adult ability to generate more breeding outputs; perhaps stronger adults select stronger substrate to excavate, leaving softer trees and brunches to the weaker pairs. Within forest biodiversity, cavity-nesters have globally become seriously threatened by deforestation 51 and conventional silviculture 44,52,53 , in which old and large cavity-bearing trees are often removed. This might also be the case for White-throated treerunners, and also for other cavity-nester species that nest in large decaying and standing dead trees in south temperate forests 29 . Despite their importance, these important large decaying and standing dead trees are not protected by current forestry laws in Chile 54 . Here, temperate forests are threatened by fragmentation, degradation, and deforestation 55,56 , as nearly 70% of its original extent has been lost 57 . Our reported relationship between habitat preferences and fitness could be crucial information to conserve White-throated treerunners and multiple coexisting species, as this excavator is known to play an important role structuring forest-dependent communities by providing cavities for other small-size vertebrates, including birds, marsupials, and bats 21,58 . Methods Study area and focal species We studied White-throated treerunners in Andean temperate forests of La Araucanía Region, Chile (39°16’S, 71°48’W, see Altamirano et al. 2017a for a full description of the study area). This area presents a mean daily temperature of 6.0°C and an average annual precipitation > 2.000 mm distributed throughout the year 21,59 . We surveyed 20 forest stands (20–40 ha each), corresponding to nine second-growth forest stands between 40 to 80 years old subjected to selective logging, and 11 old-growth forest stands over 200 years old. Broadleaf species dominated second-growth forests stands include Nothofagus obliqua , Nothofagus dombeyi , and Laurelia sempervirens , while old-growth stands were mixed conifer-broadleaf forests dominated by broadleaf species such as Laureliopsis philippiana and Nothofagus dombeyi associated with the conifer Saxegothaea conspicua al lower elevations (500–900 m). At higher elevations (900-1,600 m) of old-growth stands include Nothofagus pumilio (broadleaf) and Araucaria araucana (conifer). The understory of second- and old-growth forests were dominated by bamboo species ( Chusquea spp.), Azara spp., Berberis spp., and tree saplings. In Chile, White-throated treerunner is distributed between Santa Inés hill (32° 9'31.51"S; 71°29'32.76"O) and the Cape Horn Archipelago (55°58`59.61” S; 67°16´00.69” O, Martínez & González 2004). Because of its size, morphology, and habits, White-throated treerunner has been compared to “Nuthatches” (Sittidae, genus Sitta ) from North America, Europe, and Asia 60,61 . White-throated treerunner is strictly arboreal, does not fly long distances, and similar to nuthatches moves from tree to tree, climbing them vertically with its legs and tail 62,63 . It actively feeds on larvae, adult insects 15,64 and other arthropods 65 , by removing small pieces of bark along tree trunks and branches 15,62 . At early stages of the reproductive season, naturalist records indicate that treerunner excavates its nesting-cavities on highly decayed or burned trees 63,66 , and standing dead trees of small diameter at breast high (15 cm) 66 . Nest searching and monitoring During eight breeding seasons (October to February), between 2010 and 2018, we searched (6 h per day, 6 days per week) for occupied cavities of White-throated treerunner in each of the 20 forest stands. To find and monitor nests, we employed the protocol described in Martin and Geupel (1993), observing adult behavior such as repeated visits to the same tree, long periods out of sight after knowing its position on a tree or sudden flight out of a tree-cavity 68 . Nests in cavities lower than 2 m height were checked directly using a Ridgid camera, while a wireless monitoring system 69 mounted on a 15 m long telescopic pole 29,44,68 was used for cavities above 2 m height. A nest was considered active after we confirmed it contained at least one egg or nestling. Then, we obtained geographic coordinates for each nest using a handheld GPS with ± 10 m accuracy. Unique code was given to every nest, cavity, and tree containing a nest for monitoring (every 3 to 4 days) until fate to determine number of hatched eggs and fledglings, signs of depredation, or cavity availability for further nesting attempts. Habitat sampling across spatial scales Following Sadoti and Vierling (2010), we used a paired used-availability study design to infer habitat preference at four spatial scales (Table 1 , Fig. 1 ). At ( 1 ) cavity-scale, we measured entrance diameter (cm), diameter at cavity height (DCH, m), cavity height above the ground (m), aspect (°), internal cavity volume (cm 3 ), branch order (order of tree branches where excavated cavity was located; 1: main trunk; 2 secondary branch; 3: tertiary branch; and so forth), and substrate decay class (degree of decomposition of the specific substrate where a given nest was located, associated with the branch order). At ( 2 ) nest-tree scale, we recorded tree species, diameter at breast height (DBH), and decay class of nest-trees (1: live healthy tree; 2: live unhealthy tree; 3: recently dead tree; 4: old dead tree; and 5: naturally fallen tree; for details see 29 ). At ( 3 ) forest-stand scale, we established vegetation plots of 11,2 m radius (0,04 ha), with the nest-tree at the center of the plot and recorded both DBH and decay classes for every tree with DBH > 12.5. These data allowed us to calculate habitat attributes including tree density, mean DBH and standard deviation, decay class mode, and stand basal area 14 . At ( 4 ) landscape scale, we used a 3 ha buffer for analysis with nest-trees at the center. We choose a 3 ha buffer because other studies reported home ranges of 3 ha for Brown-headed nuthatch ( Sitta pusilla ) 71 and Eurasian nuthatch ( Sitta europaea ) 72 ; species of comparable size and habits to White-throated treerunner. At this scale, we measured the nearest distance to a forest edge and forest area 14 (Table 1 ). We determined forest cover area through remote sensing combining Remap 73 and QGIS 3.6 Noosa 74 . Remap is an online mapping platform that allowed us to classify land cover. At an approximate 120.000 ha buffer area containing all 130 plots (65 corresponding to White-throated treerunner nests and 65 to control trees, i.e. random trees without nests of the study species), we established a set of training points through photointerpretation. This set of training points feeds an algorithm that based on a set of biophysical, spectral, and climatic predictors classifies the entire area in the different classes of land cover types that were defined with the training set points. The image of reference corresponded to a 2014–2017 Landsat composite image. The resulting classified image was downloaded and corrected with QGIS, determining the forest cover at 3 ha plots for every nest. Forest edge was established when there was a > 50 m distance between forests, as Eurasian nuthatch shows a maximum distance of 50 m in open spaces 75 . Table 1 Spatial scales (cavity, nest-tree, forest-stand and landscape) assessed to explore habitat preference and fitness of White-throated treerunner ( Pygarrhychas albogularis ). Spatial scale Variable Description Cavity Fresh 1: Cavity excavated during the observation year; 0: Cavity excavated in previous seasons. Entrance diameter Horizontal diameter entrance of the excavated cavities (cm) DCH Diameter at cavity height of nest trees (cm) Height Height above the ground of the excavated cavities on tree (m) Aspect Cardinal orientations of the excavated cavities in degrees (0° − 360°) Cavity volume Internal cavity volume calculated as volume of cylinder (cm 3 ) Branch order Order of tree branches where excavated cavity was located. 1: main trunk; 2 secondary branch; 3: tertiary branch; and so forth. Subdecay class Degree of decomposition of the tree branch where excavated cavity was located Nest-tree Tree species Tree species where nests were found. Tree DBH Diameter at breast height of nest trees Decay class Degree of decomposition of the nest trees. Decay classes assigned were 1 (live healthy tree); 2 (live unhealthy tree); 3 (recently dead tree); and 4 (long dead tree; modified from Thomas et al. 1979; Edworthy et al. 2012). Forest-stand Tree density Density of total trees in one hectare Average DBH Mean DBH of total trees in forest-stand Standard deviation DBH Standard deviation of DBH in total trees on forest-stand Decay class mode Mode of degree decomposition of the total trees in forest-stand Basal area Basal area of forest-stand (cm 2 /ha) Landscape Forest edge Nearest distance to forest edge from to nest tree (m). Forest area Forest area in circular buffer of 3 ha with nest tree in the center (m 1 ) Statistical Analysis Breeding habitat preferences We used a stratified case-control sampling design 76 to examine habitat attributes, across spatial scales, associated with nest-site preferences and fitness. We had 65 nests of White-throated treerunner, and thus randomly selected 65 control sites from a large data base we have collected throughout the years (2008–2018). For the random selection of control sites, we excluded plots were treerunner nests were found and we selected control vegetation plots and trees from the same forest site and season of its paired treerunner nest. To examine habitat preferences we used generalized linear mixed effect models (R package lme4 v1.1-31) 77 with site and season as random effects and White-throated treerunner nest presence ( 1 ) or absence (0) as the response. We assessed possible preferences at three spatial scales (nest-tree scale, forest-stand scale, and landscape scale. Table 1 ). The modelling algorithm was considered keeping all random effect variables as a basis, and manually adding fixed effect variables one at a time from smaller to larger scales. Eighteen models were built, and the best model was selected through Akaike criterion and variable significance. Fitness To investigate whether habitat attributes of preferred reproduction sites are linked to fitness we looked at two different aspects: nesting success and number of fledglings. For nesting success we estimated daily nest survival rate (DSR) using the logistic exposure method 78 with generalized linear mixed effect models, including site and season as random effects. The response variable was either 1 (nest survived between nest visits) or 0 (nest did not survive). For the number of fledglings produced, we used linear mixed effect models, with number of fledglings as the response variable and included site and season as random effects. As for the fixed effects, we used the same three spatial scales used in the habitat preference assessment, but also added cavity-scale covariables in the analysis (Table 1 ). This was done only for fitness and not habitat selection because, as an excavator, cavity attributes are generated by the excavation process; thus, those attributes are not “preferred” or “avoided”. We fitted all possible combinations of covariables, excluding interactions. For most covariables we looked at their linear effect only. However, for Aspect, we looked at its linear and quadratic effect. We then ranked the models by AICc and selected the model with the lowest value. We assessed parameter importance in the final model by determining whether or not their 95% confidence interval (CI) included zero 79 . We estimated overall nest survival rate using Mayfield-derived daily failure rates 80 . Before fitting nest survival models we investigated a potential effect of researcher on DSR derived from frequent nest visitations. We created a continuous variable of cumulative nest visitations, and assessed its effect on DSR using logistic exposure method, as described above. Before fitting any model, we checked for outliers with Cook's distance (D), and for correlation among covariates to assess multicollinearity (r > 0.75). We replaced missing values with the mean of the variable and standardized all continuous variables to a mean of zero with one unit of standard deviation 81 . We assessed the goodness of fit of the final models with \({\chi }^{2}\) tests, rejecting the model if p < 0.05. All analysis were performed in R 4.2.1 82 . Declarations Acknowledgements We thank the numerous field assistants and students who worked in the field on the Chilean Nestweb project over the years. We acknowledge the logistic support from the Chilean Forestry Service (CONAF). We thank J. Laker (Kodkod: Lugar de Encuentros), M. Venegas and R. Sanhueza (Guías-Cañi), R. Timmerman, M. Sabugal, C. Délano, A. Dittborn, Lahuen Foundation and Kawellucó Private Sanctuary, who kindly allowed us to work in their properties. Author contributions. TAA, JTI and KM: Conceptualization, Methodology, Writing, Supervision, Project administration, Funding acquisition. FN, ZV, AV, RJ and ERP: Methodology, Investigation, Data Curation, Writing. RR: Reviewing, Funding acquisition. Funding We thank the financial support from ANID/FONDECYT de Inicio N° 11230504 and N° 11160932, the Rufford Small Grants Foundation (14397-2), the Chilean Ministry of the Environment (FPA Projects 09-083-08, 09-078-2010, 9-I-009-12), The Peregrine Fund, Environment Canada, Idea Wild and Francois Vuilleumier Fund for Research on Neotropical Birds from the Neotropical Ornithological Society. We acknowledge the support from ANID PIA/BASAL FB0002, ANID/FONDAP/15110006 and the ANID – Millennium Science Initiative –CESIEP Code NCS13_004. Data Availability Statement. The data that support the findings of this study are available from the corresponding author upon reasonable request. Additional information (Competing interests Statement). 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Entrance diameter Horizontal diameter entrance of the excavated cavities (cm) DCH Diameter at cavity height of nest trees (cm) Height Height above the ground of the excavated cavities on tree (m) Aspect Cardinal orientations of the excavated cavities in degrees (0° - 360°) Cavity volume Internal cavity volume calculated as volume of cylinder (cm 3 ) Branch order Order of tree branches where excavated cavity was located. 1: main trunk; 2 secondary branch; 3: tertiary branch; and so forth. Subdecay class Degree of decomposition of the tree branch where excavated cavity was located Nest-tree Tree species Tree species where nests were found. Tree DBH Diameter at breast height of nest trees Decay class Degree of decomposition of the nest trees. Decay classes assigned were 1 (live healthy tree); 2 (live unhealthy tree); 3 (recently dead tree); and 4 (long dead tree; modified from Thomas et al. 1979; Edworthy et al. 2012). Forest-stand Tree density Density of total trees in one hectare Average DBH Mean DBH of total trees in forest-stand Standard deviation DBH Standard deviation of DBH in total trees on forest-stand Decay class mode Mode of degree decomposition of the total trees in forest-stand Basal area Basal area of forest-stand (cm 2 /ha) Landscape Forest edge Nearest distance to forest edge from to nest tree (m). Forest area Forest area in circular buffer of 3 ha with nest tree in the center (m 1 ) Table 2. Characteristics of nesting habitats (cavities and tree scales) and fitness (measured as nesting success and the number of fledglings) of White-throated treerunners ( Pygarrhychas albogularis ; n = 65). Values are presented as percentages or mean ± standard deviation (range). Variable White-throated treerunners’ nests Landscape scale Forest edge (m) 482.1 ± 807.7 (0 - 2851.8) Forest area (m 1 ) 25882.1 ± 4403.4 (9880 - 29499) Forest-stand scale Tree density (tree/ha) 771 ± 366 (25 - 1550) Average DBH (cm) 31.4 ± 7.7 (20.2 - 59.8) Standard deviation DBH (cm) 16.2 ± 11.2 (6.9 - 86) Decay class mode 2 ± 0.6 (1 - 4) Tree scale Tree species (%) Nothofagus obliqua : 61.5 Nothofagus dombeyi : 12.3 Nothofagus pumilio : 10.8 Persea lingue : 9.2 Eucryphia cordifolia : 6.1 Tree DBH (cm) 40.7 ± 20.3 (14.1 - 123.1) Decay class 3.2 ± 1.0 (1 – 4) Cavity scale Fresh (%) Fresh cavities: 89.2 Non-fresh cavities: 10.8 Entrance diameter (cm) 3.8 ± 0.6 (2.5 – 5) DCH (cm) 21.5 ± 11.5 (9 – 60) Height (m) 9.6 ± 4.1 (1.1 – 17.1) Aspect (°) 145.4 ± 106.5 (4 – 348) Cavity volumen (cm 3 ) 1,349.1 ± 755.2 (147.3 – 4,417.9) Branch order (%) Main trunk: 64.6 2 nd order branch: 30.8 3 rd order branch: 4.6 Substrate decay class 3.7 ± 0.7 (2 – 4) Fitness Clutch size (# eggs) 3.2 ± 0.8 (1 – 5) Nesting success (%) Successful: 75.03 (CI = 62.77 – 89.62) unsuccessful: 24.97 Number of fledglings (# chicks) 3.0 ± 1.2 (1 – 5) Table 3. Model rankings (for White-throated treerunners ( Pygarrhychas albogularis ) habitat preference, number of fledglings, and nest success in relation to the four spatial scales assessed in its nesting sites in south temperate forests, Chile. Season and site were random terms in all models. Bold indicates best-supported models. a Number of parameters estimated. b Difference in AICc values between each model and the lowest AICc model (we show the list of models until the first one with ∆AIC > 2). c AICc model weight. d Log likelihood. Model K a AICc ∆AIC b W i c LL d (a) Habitat preference DBH + Decay + Basal area + Forest area 9 113.7 0.00 0.34 -47.8 DBH + Decay + Basal area + Forest area + SD DBH 10 117.26 2.06 0.12 -47.67 (b) Nesting success Entrance diameter + forest edge + height 6 95.1 0.00 0.07 -41.44 Entrance diameter + height 5 95.4 0.26 0.06 -42.61 Forest edge + height 5 95.9 0.81 0.05 -42.88 Height 4 96.1 0.98 0.05 -44.00 DBH + entrance diameter 5 96.4 1.27 0.04 -43.11 Entrance diameter + forest edge + height + tree density 7 96.7 1.77 0.03 -41.29 Entrance diameter + DBH + height 6 97.0 1.84 0.029 -42.36 Forest edge + height + tree density 6 97.0 1.92 0.03 -42.40 DBH 4 91.1 1.97 0.03 -44.49 DBH + Forest edge + height + entrance diameter 7 97.19 2.06 0.03 -41.43 (c) Number of fledglings DBH + decay class 7 102.2 0.00 0.35 -42.28 Intercept 4 103.5 1.28 0.19 -47.14 Decay class 6 103.6 1.47 0.17 -44.50 DBH 5 103.6 1.47 0.17 -45.91 ASP + ASP 2 + DBH + decay clas 9 104.3 2.08 0.12 -40.02 Additional Declarations No competing interests reported. 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Altamirano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYHACxgNAggfM/AAi2AmoByk9cACqhXEGiGQmUgsYMPMQo8WevfnB4Q8122TM288efGzbZsfA30zIFp5jBgcOHLvNI3MmL9k4ty2ZQeIwIS0SCUAtbLd5JBhyzKRz2w4wGBD0i0T6hwMH/gG18L8xk7YkTkuOwYGDbUAtEkBbGInScuZMwYGzfSAtb4wNe84l8xD0C3t7+8YHFd9u20vw5xg++FFmJ8ff3kBAD4a1JKofBaNgFIyCUYANAADMHD46NwTOaQAAAABJRU5ErkJggg==","orcid":"","institution":"National Audubon Society","correspondingAuthor":true,"prefix":"","firstName":"Tomás","middleName":"A.","lastName":"Altamirano","suffix":""},{"id":283576892,"identity":"0693baf3-5967-456b-9320-2662044848f9","order_by":1,"name":"Fernando J. Novoa","email":"","orcid":"","institution":"ECOS (Ecosystem - Complexity - Society) Co-Laboratory, Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"J.","lastName":"Novoa","suffix":""},{"id":283576893,"identity":"62a86c91-585c-4a97-b563-1c0f67f3914c","order_by":2,"name":"Zoltan Von Von Bernath","email":"","orcid":"","institution":"ECOS (Ecosystem - Complexity - Society) Co-Laboratory, Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Zoltan","middleName":"Von","lastName":"Von Bernath","suffix":""},{"id":283576894,"identity":"a923d237-fd79-445e-b24b-da3d57656196","order_by":3,"name":"Alejandra Vermehren","email":"","orcid":"","institution":"ECOS (Ecosystem - Complexity - Society) Co-Laboratory, Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Alejandra","middleName":"","lastName":"Vermehren","suffix":""},{"id":283576895,"identity":"ed7ca276-7d04-46f0-a311-7bca9e8af1e1","order_by":4,"name":"Kathy Martin","email":"","orcid":"","institution":"University of British Columbia","correspondingAuthor":false,"prefix":"","firstName":"Kathy","middleName":"","lastName":"Martin","suffix":""},{"id":283576896,"identity":"760e4a4b-a087-4fb0-a924-be5b0ad93d87","order_by":5,"name":"Rocío Jara","email":"","orcid":"","institution":"ECOS (Ecosystem - Complexity - Society) Co-Laboratory, Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"Rocío","middleName":"","lastName":"Jara","suffix":""},{"id":283576897,"identity":"7a42f77e-a94f-44f9-81af-1ae757cef564","order_by":6,"name":"Edwin Rockwell-Price","email":"","orcid":"","institution":"Green Godwit Consulting LLC","correspondingAuthor":false,"prefix":"","firstName":"Edwin","middleName":"","lastName":"Rockwell-Price","suffix":""},{"id":283576898,"identity":"bf28dc62-64b6-456f-915c-8b01563b9e5d","order_by":7,"name":"Ricardo Rozzi","email":"","orcid":"","institution":"Universidad de Magallanes","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Rozzi","suffix":""},{"id":283576899,"identity":"c160a0df-2c05-49ba-b6e3-096bfb2816c8","order_by":8,"name":"José Tomás Ibarra","email":"","orcid":"","institution":"ECOS (Ecosystem - Complexity - Society) Co-Laboratory, Pontificia Universidad Católica de Chile","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Tomás","lastName":"Ibarra","suffix":""}],"badges":[],"createdAt":"2024-01-31 16:01:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3914394/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3914394/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93594-1","type":"published","date":"2025-10-06T15:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53461873,"identity":"8971a93c-6dbd-4b34-8f2e-7b22c90a9125","added_by":"auto","created_at":"2024-03-26 09:24:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1311574,"visible":true,"origin":"","legend":"\u003cp\u003eStudy species White-throated treerunners (\u003cem\u003ePygarrhychas albogularis\u003c/em\u003e) (A) and the spatial scales assessed for breeding habitat preferences and fitness in temperate forests of South America: cavity scale (B), tree scale (C), forest-stand scale (D), and landscape scale (E).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3914394/v1/5fe4aa449ed38c1cc440b5c4.png"},{"id":53461531,"identity":"3154aede-3fa5-4244-86bd-0aae2f8aa262","added_by":"auto","created_at":"2024-03-26 09:16:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157304,"visible":true,"origin":"","legend":"\u003cp\u003eMost influential variables, at different spatial scales, on nesting habitat preferences of White-throated treerunners (\u003cem\u003ePygarrhichas albogularis\u003c/em\u003e) in temperate forest of South America: (A) Tree-decay (tree scale) and (B) forest area (landscape scale).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3914394/v1/189fe43da3975b413ea68210.png"},{"id":53461529,"identity":"268df2d9-ebd3-4dcf-9f04-102b5cdbd2c3","added_by":"auto","created_at":"2024-03-26 09:16:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122609,"visible":true,"origin":"","legend":"\u003cp\u003eMost influential variables (significant and marginally significant), at different spatial scales, on fitness (daily survival rate and number of fledglings) of White-throated treerunner\u003cem\u003e \u003c/em\u003e(\u003cem\u003ePygarrhichas albogularis\u003c/em\u003e) in temperate forest, South America: (A) Height (cavity scale), (B) Diameter at breast heigh (tree scale), and (C) decay class (tree scale).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3914394/v1/fc513081dd50602d71d5c16b.png"},{"id":93419780,"identity":"5dd60d3e-b9f4-4b8a-be57-55ce8df1a185","added_by":"auto","created_at":"2025-10-13 16:07:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3208607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3914394/v1/888d6fba-e3b6-4611-9cb2-16a0e68afe08.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eFrom Tree-cavity to Landscape: Habitat Preferences and Fitness Operates Across Scales for an Old Relict Species of Southern South-america\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAnimals select their habitats from available resources in a way that should maximize fitness \u003csup\u003e1\u003c/sup\u003e. Thus, it is expected that habitat attributes associated with habitat selection will also be those most strongly linked to fitness \u003csup\u003e2\u003c/sup\u003e. Habitat preferences (i.e. the final pattern of habitat used with respect to its availability) will generally be adaptive, under the pressures of natural selection, if a species obtains maximum fitness \u003csup\u003e3,4\u003c/sup\u003e. However, there may be a mismatch between habitat preferences and fitness by factors such as temporal changes in habitat features after territory establishment \u003csup\u003e5\u003c/sup\u003e and rapid disturbance of reproductive habitats \u003csup\u003e6,7\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHabitat attributes influence fitness via the costs (e.g. predation risk) and benefits (e.g. food availability) of habitat preferences\u003csup\u003e8\u003c/sup\u003e. There is evidence that avian habitat preferences are scale-dependent and hierarchical phenomena \u003csup\u003e9\u003c/sup\u003e. Scales at which habitat selection may occur range from microsites selected for nesting or foraging to the entire geographic range of a species \u003csup\u003e10\u003c/sup\u003e. Thus, differences between nesting and available sites reported for excavators (i.e. species that excavate their nesting cavities in trees) may be because there are multiple scales operating in nesting site selection processes, from fine to coarse scales \u003csup\u003e11,12\u003c/sup\u003e. Excavators should assess, for example, the tradeoff between a secure nesting substrate for excavation and the distance to a foraging area \u003csup\u003e13\u003c/sup\u003e. Multiscale studies allow identifying important scales concerning individual perception of their habitats, otherwise difficult to detect when knowledge of the ecology of the study species is limited \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe White-throated treerunner (\u003cem\u003ePygarrhichas albogularis\u003c/em\u003e King 1831), is a poorly known Furnariid endemic to South American temperate and Mediterranean ecosystems, mainly found in central and southern regions of Chile and Argentina \u003csup\u003e15\u003c/sup\u003e. This species is considered a \u0026ldquo;living fossil\u0026rdquo; or \u0026ldquo;old relict\u0026rdquo; as it is the only living species of the genus \u003cem\u003ePygarrhichas\u003c/em\u003e \u003csup\u003e16,17\u003c/sup\u003e. White-throated treerunner is one of the four species of tree cavity excavators in these ecosystems \u003csup\u003e18\u003c/sup\u003e. As an excavator, this species relies on habitats with presence of trees suitable for excavation \u003csup\u003e19\u003c/sup\u003e. Although this species has been suggested as a key habitat facilitator for several avian and mammalian cavity-nesting species in southern South America \u003csup\u003e20,21\u003c/sup\u003e, there is only one study on the ecology of treerunners and focused on foraging use of tree species \u003csup\u003e22\u003c/sup\u003e. There are occasional community level studies that included treerunners as part of an avian assemblage \u003csup\u003e23\u0026ndash;32\u003c/sup\u003e. Thus, this research advances the knowledge exploring nesting habitat preference and fitness of treerunners in the centre of its distribution.\u003c/p\u003e \u003cp\u003eIn this study we examine the nesting tree preference of treerunners and whether their preferred habitat attributes are linked to fitness through a two-step modelling approach. First, we assess habitat preferences analyzing the link between habitat attributes and preferences of nesting sites at three spatial scales: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) nest-tree scale, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) forest-stand scale, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) landscape scale. Second, we assess whether habitat attributes of preferred reproduction sites are linked to fitness (represented as nesting success and number of fledglings) at the same three spatial scales, adding cavity characteristics as a fourth smaller spatial scale in the analysis. We hypothesize that (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) preferred reproductive habitat attributes differ to those of surrounding available habitat and that (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) habitat attributes at each spatial scale are linked to improved fitness, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) there is a match between attributes of habitat preferred and fitness. This study delivers better understanding of forests attributes that must be maintained to ensure habitat for this and other coexisting cavity nester species in south temperate forests \u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 2010 and 2018, we located and monitored 65 White-throated treerunner\u003cem\u003e\u0026rsquo;\u003c/em\u003e nests (273 to 1,342 m of elevation), 84.6% of which were excavated in \u003cem\u003eNothofagus\u003c/em\u003e trees (Table 2). In average, treerunners used mid-size trees for nesting (DBH = 40.6 cm) within a broad range of trees (DBH range: 14.1 - 123.1 cm). Old dead trees with advanced decay (55%) and live unhealthy trees (35%) contained the great majority of its nests, with only 8% and 2% for recently dead and live healthy trees, respectively (Table 2). When we look at the specific substrate decay, 80% of the nests where in old dead substrates, and only 14% and 6% in live unhealthy and recently dead substrates, respectively. Fifty-eight nests (89.2%) were in freshly excavated cavities. White-throated treerunner laid an average of 3 eggs (range: 1 \u0026ndash; 5 eggs). The overall nest survival was 75.03% (95% CI = 62.77 \u0026ndash; 89.62%; n = 61 including only nests with known outcome). Regarding unsuccessful nests (n = 10), 6 failed because of predation, 3 were abandoned, and 1 nest failed because eggs were not viable (Table 2). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eBreeding habitat preferences across spatial scales\u003c/h2\u003e\n\u003cp\u003eVariation in the use of breeding habitat was best predicted by a model that included two tree-nest scale variables (DBH and tree decay), one forest-stand scale variable (basal area), and one landscape scale variable (forest area; Table 3). However, basal area was not significant (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e4.64 \u0026plusmn; 3.04, p \u0026gt; 0.05). The probability that a given habitat is used for nesting increased with larger trees, advanced tree decay, and\u0026nbsp;forest area. Tree decay was the strongest predictor of nest presence, followed by forest area, and DBH. Treerunners avoided healthy living trees (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e-10.10 \u0026plusmn; 2.86, p \u0026lt; 0.01), and the probability of nesting in a tree increased positively with each increasing decay class (Fig. 2A): unhealthy living trees (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e3.53 \u0026plusmn; 1.26, p \u0026lt; 0.01), recently dead trees (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e3.76 \u0026plusmn; 1.46, p = 0.01), and old dead trees (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e7.35 \u0026plusmn; 1.64, p \u0026lt; 0.01). Even when both forest area and DBH are positively associated with preferred breeding habitats, GLMMs showed that the extension of forest area (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e4.09 \u0026plusmn; 2.11, p = 0.05, marginally significant; Fig. 2B) influenced 82 times more than DBH (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e0.04 \u0026plusmn; 0.02, p = 0.01).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eFitness outputs\u003c/h2\u003e\n\u003cp\u003eFor nesting success, there were two spatial scales included in the best models: cavity scale (entrance diameter and height) and landscape scale (forest edge, Table 3). Entrance diameter and forest edge had a positive effect on nesting success (daily survival rate). Nests with bigger entrances and further from the forest edge were more successful. Height was positively and marginally associated with nesting success (\u003cem\u003eb =\u0026nbsp;\u003c/em\u003e1.093 95% CI = 0.241 \u0026ndash; 2.837, p = 0.05; Fig. 3A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the number of fledglings produced, the final model only had parameters at the tree scale (DBH and decay class). DBH was negatively associated with number of fledglings (\u003cem\u003eb\u003c/em\u003e = -0.263; CI = -0.508 \u0026ndash; -0.021; Table 3; Fig. 3B). Decay class had an important effect on number of fledglings, being positively associated with unhealthy living trees (\u003cem\u003eb\u003c/em\u003e = 3.180) and with recently dead trees (\u003cem\u003eb\u003c/em\u003e = 0.798), while long dead trees were negatively associated with the number of fledglings (\u003cem\u003eb\u003c/em\u003e = -0.403) (Fig. 3C).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe found that both habitat preferences for nesting and nesting success measures for White-throated treerunner operate across multiple spatial scales. Similar to previous studies using multiscale approaches \u003csup\u003e7,13,33\u003c/sup\u003e, at least one variable at each scale influenced nest site preferences and/or fitness (number of fledglings and nesting success) for our study species. Both tree and landscape scales were the most important variables influencing nesting habitat preferences, while cavity, forest-stand, and landscape scale variables were the most important in our assessment of nesting success (daily survival rate and number of fledglings). White-throated treerunner showed preferences for areas with a relatively high forest cover and that their nesting success increased in forests with a relatively low density of trees. This may suggest that old-growth forests, with extensive areas of forest with relatively less, but larger, trees compared to second-growth forests \u003csup\u003e34\u003c/sup\u003e, contain the best complement of multiscale variables for this species.\u003c/p\u003e \u003cp\u003eWhite-throated treerunner tends to excavate new cavities (89%) every nesting season, with the remaining 11% consisting of reused cavities, all of which were excavated by this species during previous years. Thus, we have determined that this is a primary cavity-nester (i.e. obligate excavators; mainly excavates their own cavities, but occasionally use existing excavated cavities; \u003csup\u003e35\u003c/sup\u003e. Breeding pairs might use existing cavities depending on food and/or nest predation risk (e.g. in case their breeding attempt failed in their fresh cavity). Similarly to Red-naped Sapsuckers (\u003cem\u003eSphyrapicus nuchalis\u003c/em\u003e), which mostly excavate their own cavities, but about 11\u0026ndash;12% reuse existing cavities, mainly cavities that were excavated in previous years \u003csup\u003e36\u003c/sup\u003e. White-throated treerunner are considered to have similar breeding behavior to nuthatches in North America, but Red-Breasted nuthatch (\u003cem\u003eSitta canadensis\u003c/em\u003e) tend to be facultative excavators (i.e. excavate about 50% of their nests, excavation rates vary annually; Aitken et al. 2002, Norris \u0026amp; Martin 2012) \u003csup\u003e38,39\u003c/sup\u003e. Thus, based on our study, treerunners are more extensively primary cavity excavators like Red-naped Sapsuckers or Downy Woodpeckers (\u003cem\u003eDryobates pubescens\u003c/em\u003e) rather than nuthatches that are facultative excavators \u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhite-throated treerunner nesting habitat preferences were mainly associated with variables acting at both tree and landscape scales, preferring relatively larger trees in advanced decay classes present in forested areas. This partially agrees with the results found for functionally similar species in the northern Hemisphere, such as Red-naped Sapsucker (\u003cem\u003eSphyrapicus nuchalis\u003c/em\u003e) \u003csup\u003e13\u003c/sup\u003e. The latter species preferred nesting habitats associated with landscape scale attributes, similar to treerunners, but only tree scale attributes were linked to fitness \u003csup\u003e13\u003c/sup\u003e. This suggests a hierarchical process of habitat selection in White-throated treerunner \u003csup\u003e40\u003c/sup\u003e, in which several factors, at different spatial scales, influence the decision of choosing a given nest-site \u003csup\u003e7\u003c/sup\u003e. For example, the main trophic resource for treerunners are forest-dwelling arthropods in adult and larvae states \u003csup\u003e41,42\u003c/sup\u003e, which are probably more abundant in multi-stratified forested landscapes with presence of all tree decay classes \u003csup\u003e43\u003c/sup\u003e. On the other hand, this species is choosing variables at finer scale for nesting too. Like other cavity-nesting species in the study area \u003csup\u003e29\u003c/sup\u003e, tree scale attributes are the most influential factors for treerunners. This finding is similar to results reported for several primary cavity nesters such as woodpeckers in temperate forests of North America\u003csup\u003e19\u003c/sup\u003e and in tropical Atlantic forests of South America \u003csup\u003e44\u003c/sup\u003e, where excavators strongly select live unhealthy and standing dead trees as nesting sites.\u003c/p\u003e \u003cp\u003eWhite-throated treerunner showed high values of nesting success, with 75% of its nests being successful. This high percentage strongly contrasts with the overall nesting success of avian communities in tropical forests of Brasil (42%)\u003csup\u003e45\u003c/sup\u003e and Dominican Republic (34%) \u003csup\u003e46\u003c/sup\u003e, but similar to other cavity nesters and excavators such as Red-Breasted nuthatch (84%) \u003csup\u003e47\u003c/sup\u003e, and Red-naped Sapsuckers (91%) \u003csup\u003e13\u003c/sup\u003e. We found that the entrance diameter of cavities was positively associated with nesting success, a counterintuitive finding, which might be associated with adult ability to successfully breed; and thus, stronger adults might be able to protect the nest and excavate larger cavities. Furthermore, cavity-nest predator assemblage in our study area is diverse but composed by relatively large animals, with \u003cem\u003eLeopardus guigna\u003c/em\u003e, \u003cem\u003eDromiciops gliroides, Milvago chimango, Caracara plancus, Glaucidium nana\u003c/em\u003e, and \u003cem\u003eRattus rattus\u003c/em\u003e being the main predators \u003csup\u003e48\u003c/sup\u003e. Thus, even when predation was the main cause of nest failure, small differences in entrance diameter may not increase predation risk. On the other hand, and because the nesting substrate that treerunners choose for excavation is usually soft, excavate cavities higher in the forest might reduce the predation risk by terrestrial predators. Nesting success increased with the distance from the forest edge, suggesting that the selection of breeding habitats (selecting sites with high forest cover) is adaptive, and that decision is being translated in more breeding success \u003csup\u003e13\u003c/sup\u003e. Second-growth forests contain a larger number of trees and more chance to be close to edge compared to old-growth forests, as well as less options to nests higher in the canopy, suggesting that the first may not be ideal habitats to breed for White-throated treerunners.\u003c/p\u003e \u003cp\u003eThe number of fledglings of White-throated treerunners was associated only with tree scale attributes (i.e. DBH and decay). The latter is partially similar to the productivity of Red-naped Sapsuckers in the United States \u003csup\u003e13\u003c/sup\u003e, where cavity and tree scales were the most important attributes associated with the number of fledglings. Breeding outputs were positively related to southeastern orientation of cavity entrance for Red-naped Sapsuckers, while we did not find any relation between cavity aspect and fitness for treerunners. Our finding showing a lack of association between cavity orientation and fitness matches with recently published results for South American excavators, in which there is no general pattern of cavity orientation \u003csup\u003e49\u003c/sup\u003e. Instead, and unlike nesting success, the number of fledglings was associated with tree scale, being negatively associated with tree DBH. This can be responding to elevation, due to larger trees are present mainly at high elevations, where shorter breeding seasons can be translated in smaller clutch sizes \u003csup\u003e50\u003c/sup\u003e. On the other hand, we found that unhealthy live trees and recently dead trees increased the number of fledglings, while long dead trees negatively affected the number of fledglings. This might be also responding to adult ability to generate more breeding outputs; perhaps stronger adults select stronger substrate to excavate, leaving softer trees and brunches to the weaker pairs.\u003c/p\u003e \u003cp\u003eWithin forest biodiversity, cavity-nesters have globally become seriously threatened by deforestation \u003csup\u003e51\u003c/sup\u003e and conventional silviculture \u003csup\u003e44,52,53\u003c/sup\u003e, in which old and large cavity-bearing trees are often removed. This might also be the case for White-throated treerunners, and also for other cavity-nester species that nest in large decaying and standing dead trees in south temperate forests \u003csup\u003e29\u003c/sup\u003e. Despite their importance, these important large decaying and standing dead trees are not protected by current forestry laws in Chile \u003csup\u003e54\u003c/sup\u003e. Here, temperate forests are threatened by fragmentation, degradation, and deforestation \u003csup\u003e55,56\u003c/sup\u003e, as nearly 70% of its original extent has been lost \u003csup\u003e57\u003c/sup\u003e. Our reported relationship between habitat preferences and fitness could be crucial information to conserve White-throated treerunners and multiple coexisting species, as this excavator is known to play an important role structuring forest-dependent communities by providing cavities for other small-size vertebrates, including birds, marsupials, and bats \u003csup\u003e21,58\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and focal species\u003c/h2\u003e \u003cp\u003eWe studied White-throated treerunners in Andean temperate forests of La Araucan\u0026iacute;a Region, Chile (39\u0026deg;16\u0026rsquo;S, 71\u0026deg;48\u0026rsquo;W, see Altamirano et al. 2017a for a full description of the study area). This area presents a mean daily temperature of 6.0\u0026deg;C and an average annual precipitation\u0026thinsp;\u0026gt;\u0026thinsp;2.000 mm distributed throughout the year \u003csup\u003e21,59\u003c/sup\u003e. We surveyed 20 forest stands (20\u0026ndash;40 ha each), corresponding to nine second-growth forest stands between 40 to 80 years old subjected to selective logging, and 11 old-growth forest stands over 200 years old. Broadleaf species dominated second-growth forests stands include \u003cem\u003eNothofagus obliqua\u003c/em\u003e, \u003cem\u003eNothofagus dombeyi\u003c/em\u003e, and \u003cem\u003eLaurelia sempervirens\u003c/em\u003e, while old-growth stands were mixed conifer-broadleaf forests dominated by broadleaf species such as \u003cem\u003eLaureliopsis philippiana\u003c/em\u003e and \u003cem\u003eNothofagus dombeyi\u003c/em\u003e associated with the conifer \u003cem\u003eSaxegothaea conspicua\u003c/em\u003e al lower elevations (500\u0026ndash;900 m). At higher elevations (900-1,600 m) of old-growth stands include \u003cem\u003eNothofagus pumilio\u003c/em\u003e (broadleaf) and \u003cem\u003eAraucaria araucana\u003c/em\u003e (conifer). The understory of second- and old-growth forests were dominated by bamboo species (\u003cem\u003eChusquea\u003c/em\u003e spp.), \u003cem\u003eAzara\u003c/em\u003e spp., \u003cem\u003eBerberis\u003c/em\u003e spp., and tree saplings.\u003c/p\u003e \u003cp\u003eIn Chile, White-throated treerunner is distributed between Santa In\u0026eacute;s hill (32\u0026deg; 9'31.51\"S; 71\u0026deg;29'32.76\"O) and the Cape Horn Archipelago (55\u0026deg;58`59.61\u0026rdquo; S; 67\u0026deg;16\u0026acute;00.69\u0026rdquo; O, Mart\u0026iacute;nez \u0026amp; Gonz\u0026aacute;lez 2004). Because of its size, morphology, and habits, White-throated treerunner has been compared to \u0026ldquo;Nuthatches\u0026rdquo; (Sittidae, genus \u003cem\u003eSitta\u003c/em\u003e) from North America, Europe, and Asia \u003csup\u003e60,61\u003c/sup\u003e. White-throated treerunner is strictly arboreal, does not fly long distances, and similar to nuthatches moves from tree to tree, climbing them vertically with its legs and tail \u003csup\u003e62,63\u003c/sup\u003e. It actively feeds on larvae, adult insects \u003csup\u003e15,64\u003c/sup\u003e and other arthropods \u003csup\u003e65\u003c/sup\u003e, by removing small pieces of bark along tree trunks and branches \u003csup\u003e15,62\u003c/sup\u003e. At early stages of the reproductive season, naturalist records indicate that treerunner excavates its nesting-cavities on highly decayed or burned trees \u003csup\u003e63,66\u003c/sup\u003e, and standing dead trees of small diameter at breast high (15 cm) \u003csup\u003e66\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNest searching and monitoring\u003c/h3\u003e\n\u003cp\u003eDuring eight breeding seasons (October to February), between 2010 and 2018, we searched (6 h per day, 6 days per week) for occupied cavities of White-throated treerunner in each of the 20 forest stands. To find and monitor nests, we employed the protocol described in Martin and Geupel (1993), observing adult behavior such as repeated visits to the same tree, long periods out of sight after knowing its position on a tree or sudden flight out of a tree-cavity \u003csup\u003e68\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNests in cavities lower than 2 m height were checked directly using a Ridgid camera, while a wireless monitoring system \u003csup\u003e69\u003c/sup\u003e mounted on a 15 m long telescopic pole \u003csup\u003e29,44,68\u003c/sup\u003e was used for cavities above 2 m height. A nest was considered active after we confirmed it contained at least one egg or nestling. Then, we obtained geographic coordinates for each nest using a handheld GPS with \u0026plusmn;\u0026thinsp;10 m accuracy. Unique code was given to every nest, cavity, and tree containing a nest for monitoring (every 3 to 4 days) until fate to determine number of hatched eggs and fledglings, signs of depredation, or cavity availability for further nesting attempts.\u003c/p\u003e\n\u003ch3\u003eHabitat sampling across spatial scales\u003c/h3\u003e\n\u003cp\u003eFollowing Sadoti and Vierling (2010), we used a paired used-availability study design to infer habitat preference at four spatial scales (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) cavity-scale, we measured entrance diameter (cm), diameter at cavity height (DCH, m), cavity height above the ground (m), aspect (\u0026deg;), internal cavity volume (cm\u003csup\u003e3\u003c/sup\u003e), branch order (order of tree branches where excavated cavity was located; 1: main trunk; 2 secondary branch; 3: tertiary branch; and so forth), and substrate decay class (degree of decomposition of the specific substrate where a given nest was located, associated with the branch order). At (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) nest-tree scale, we recorded tree species, diameter at breast height (DBH), and decay class of nest-trees (1: live healthy tree; 2: live unhealthy tree; 3: recently dead tree; 4: old dead tree; and 5: naturally fallen tree; for details see \u003csup\u003e29\u003c/sup\u003e). At (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) forest-stand scale, we established vegetation plots of 11,2 m radius (0,04 ha), with the nest-tree at the center of the plot and recorded both DBH and decay classes for every tree with DBH\u0026thinsp;\u0026gt;\u0026thinsp;12.5. These data allowed us to calculate habitat attributes including tree density, mean DBH and standard deviation, decay class mode, and stand basal area \u003csup\u003e14\u003c/sup\u003e. At (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) landscape scale, we used a 3 ha buffer for analysis with nest-trees at the center. We choose a 3 ha buffer because other studies reported home ranges of 3 ha for Brown-headed nuthatch (\u003cem\u003eSitta pusilla\u003c/em\u003e)\u003csup\u003e71\u003c/sup\u003eand Eurasian nuthatch (\u003cem\u003eSitta europaea\u003c/em\u003e) \u003csup\u003e72\u003c/sup\u003e; species of comparable size and habits to White-throated treerunner. At this scale, we measured the nearest distance to a forest edge and forest area \u003csup\u003e14\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We determined forest cover area through remote sensing combining Remap \u003csup\u003e73\u003c/sup\u003e and QGIS 3.6 Noosa \u003csup\u003e74\u003c/sup\u003e. Remap is an online mapping platform that allowed us to classify land cover. At an approximate 120.000 ha buffer area containing all 130 plots (65 corresponding to White-throated treerunner nests and 65 to control trees, i.e. random trees without nests of the study species), we established a set of training points through photointerpretation. This set of training points feeds an algorithm that based on a set of biophysical, spectral, and climatic predictors classifies the entire area in the different classes of land cover types that were defined with the training set points. The image of reference corresponded to a 2014\u0026ndash;2017 Landsat composite image. The resulting classified image was downloaded and corrected with QGIS, determining the forest cover at 3 ha plots for every nest. Forest edge was established when there was a\u0026thinsp;\u0026gt;\u0026thinsp;50 m distance between forests, as Eurasian nuthatch shows a maximum distance of 50 m in open spaces \u003csup\u003e75\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial scales (cavity, nest-tree, forest-stand and landscape) assessed to explore habitat preference and fitness of White-throated treerunner (\u003cem\u003ePygarrhychas albogularis\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatial scale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eCavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFresh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1: Cavity excavated during the observation year; 0: Cavity excavated in previous seasons.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntrance diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHorizontal diameter entrance of the excavated cavities (cm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiameter at cavity height of nest trees (cm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeight above the ground of the excavated cavities on tree (m)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardinal orientations of the excavated cavities in degrees (0\u0026deg; \u0026minus;\u0026thinsp;360\u0026deg;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCavity volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInternal cavity volume calculated as volume of cylinder (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBranch order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrder of tree branches where excavated cavity was located. 1: main trunk; 2 secondary branch; 3: tertiary branch; and so forth.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubdecay class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of decomposition of the tree branch where excavated cavity was located\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNest-tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree species\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTree species where nests were found.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree DBH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiameter at breast height of nest trees\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecay class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree of decomposition of the nest trees. Decay classes assigned were 1 (live healthy tree); 2 (live unhealthy tree); 3 (recently dead tree); and 4 (long dead tree; modified from Thomas et al. 1979; Edworthy et al. 2012).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eForest-stand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTree density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDensity of total trees in one hectare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage DBH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean DBH of total trees in forest-stand\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard deviation DBH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation of DBH in total trees on forest-stand\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecay class mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMode of degree decomposition of the total trees in forest-stand\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBasal area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBasal area of forest-stand (cm\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLandscape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest edge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNearest distance to forest edge from to nest tree (m).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest area in circular buffer of 3 ha with nest tree in the center (m\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003eBreeding habitat preferences\u003c/h2\u003e \u003cp\u003eWe used a stratified case-control sampling design \u003csup\u003e76\u003c/sup\u003e to examine habitat attributes, across spatial scales, associated with nest-site preferences and fitness. We had 65 nests of White-throated treerunner, and thus randomly selected 65 control sites from a large data base we have collected throughout the years (2008\u0026ndash;2018). For the random selection of control sites, we excluded plots were treerunner nests were found and we selected control vegetation plots and trees from the same forest site and season of its paired treerunner nest.\u003c/p\u003e \u003cp\u003eTo examine habitat preferences we used generalized linear mixed effect models (R package lme4 v1.1-31)\u003csup\u003e77\u003c/sup\u003e with site and season as random effects and White-throated treerunner nest presence (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) or absence (0) as the response. We assessed possible preferences at three spatial scales (nest-tree scale, forest-stand scale, and landscape scale. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The modelling algorithm was considered keeping all random effect variables as a basis, and manually adding fixed effect variables one at a time from smaller to larger scales. Eighteen models were built, and the best model was selected through Akaike criterion and variable significance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFitness\u003c/h2\u003e \u003cp\u003eTo investigate whether habitat attributes of preferred reproduction sites are linked to fitness we looked at two different aspects: nesting success and number of fledglings. For nesting success we estimated daily nest survival rate (DSR) using the logistic exposure method \u003csup\u003e78\u003c/sup\u003e with generalized linear mixed effect models, including site and season as random effects. The response variable was either 1 (nest survived between nest visits) or 0 (nest did not survive). For the number of fledglings produced, we used linear mixed effect models, with number of fledglings as the response variable and included site and season as random effects. As for the fixed effects, we used the same three spatial scales used in the habitat preference assessment, but also added cavity-scale covariables in the analysis (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This was done only for fitness and not habitat selection because, as an excavator, cavity attributes are generated by the excavation process; thus, those attributes are not \u0026ldquo;preferred\u0026rdquo; or \u0026ldquo;avoided\u0026rdquo;. We fitted all possible combinations of covariables, excluding interactions. For most covariables we looked at their linear effect only. However, for Aspect, we looked at its linear and quadratic effect. We then ranked the models by AICc and selected the model with the lowest value. We assessed parameter importance in the final model by determining whether or not their 95% confidence interval (CI) included zero \u003csup\u003e79\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe estimated overall nest survival rate using Mayfield-derived daily failure rates \u003csup\u003e80\u003c/sup\u003e. Before fitting nest survival models we investigated a potential effect of researcher on DSR derived from frequent nest visitations. We created a continuous variable of cumulative nest visitations, and assessed its effect on DSR using logistic exposure method, as described above. Before fitting any model, we checked for outliers with Cook's distance (D), and for correlation among covariates to assess multicollinearity (r\u0026thinsp;\u0026gt;\u0026thinsp;0.75). We replaced missing values with the mean of the variable and standardized all continuous variables to a mean of zero with one unit of standard deviation \u003csup\u003e81\u003c/sup\u003e. We assessed the goodness of fit of the final models with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\chi }^{2}\\)\u003c/span\u003e\u003c/span\u003e tests, rejecting the model if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analysis were performed in R 4.2.1 \u003csup\u003e82\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the numerous field assistants and students who worked in the field on the Chilean Nestweb project over the years. We acknowledge the logistic support from the Chilean Forestry Service (CONAF). We thank J. Laker (Kodkod: Lugar de Encuentros), M. Venegas and R. Sanhueza (Gu\u0026iacute;as-Ca\u0026ntilde;i), R. Timmerman, M. Sabugal, C. D\u0026eacute;lano, A. Dittborn, Lahuen Foundation and Kawelluc\u0026oacute; Private Sanctuary, who kindly allowed us to work in their properties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eTAA, JTI and KM:\u0026nbsp;Conceptualization, Methodology, Writing, Supervision, Project administration, Funding acquisition. FN, ZV, AV, RJ and ERP: Methodology, Investigation, Data Curation, Writing. RR: Reviewing, Funding acquisition.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the financial support from ANID/FONDECYT de Inicio N\u0026deg; 11230504 and N\u0026deg; 11160932, the Rufford Small Grants Foundation (14397-2), the Chilean Ministry of the Environment (FPA Projects 09-083-08, 09-078-2010, 9-I-009-12), The Peregrine Fund, Environment Canada, Idea Wild and Francois Vuilleumier Fund for Research on Neotropical Birds from the Neotropical Ornithological Society. We acknowledge the support from ANID PIA/BASAL FB0002, ANID/FONDAP/15110006 and the ANID \u0026ndash; Millennium Science Initiative \u0026ndash;CESIEP Code NCS13_004.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement.\u003c/strong\u003e The data that support the findings of this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information (Competing interests Statement).\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests. This research was carried out under a Chilean permit to develop science in protected areas (N\u0026deg; 018/2023), also it did not involve either animal captures or the collection of any plan material.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGaillard, J. 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(Platt Establecimientos Gr\u0026aacute;ficos S.A, Buenos Aires, 1946).\u003c/li\u003e\n\u003cli\u003eAltamirano, T. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eH\u0026aacute;bitos de Nidificaci\u0026oacute;n de Las Aves Del Bosque Templado Andino de Chile\u003c/em\u003e. \u003cem\u003eFondo de Protecci\u0026oacute;n Ambiental, Ministerio de Medio Ambiente.\u003c/em\u003e vol. 1 (2012).\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez, D. E. \u0026amp; Gonz\u0026aacute;lez, G. E. \u003cem\u003eAves de Chile: Gu\u0026iacute;a de Campo y Breve Historia\u003c/em\u003e. (2017).\u003c/li\u003e\n\u003cli\u003eAltamirano, T. A. \u003cem\u003eet al.\u003c/em\u003e \u003cem\u003eH\u0026aacute;bitos de Nidificaci\u0026oacute;n de Las Aves Del Bosque Templado Andino de Chile\u003c/em\u003e. (Santiago, Chile, 2012).\u003c/li\u003e\n\u003cli\u003eMartin, T. E. \u0026amp; Geupel, G. R. Nest-Monitoring Plots : Methods for Locating Nests and Monitoring Success ( M\u0026eacute;todos para localizar nidos y monitorear el \u0026eacute;xito de estos ) Published by : Wiley on behalf of Association of Field Ornithologists Stable URL : http://www.jstor.org/stable/451386. \u003cem\u003eJ Field Ornithol\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 507\u0026ndash;519 (1993).\u003c/li\u003e\n\u003cli\u003eMartin, K., Aitken, K. E. H. \u0026amp; Wiebe, K. L. Nest sites and nest webs for cavity-nesting communities in interior British Columbia, Canada: nest characteristics and niche partitioning. \u003cem\u003eCondor\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 5\u0026ndash;19 (2004).\u003c/li\u003e\n\u003cli\u003eHuebner, D. P. \u0026amp; Hurteau, S. R. An economical wireless cavity-nest viewer. \u003cem\u003eJ Field Ornithol\u003c/em\u003e \u003cstrong\u003e78\u003c/strong\u003e, 87\u0026ndash;92 (2007).\u003c/li\u003e\n\u003cli\u003eSadoti, G. \u0026amp; Vierling, K. T. Nonideal habitat selection by a north american cavity excavator: Pecking up the wrong tree? \u003cem\u003eCan J Zool\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 527\u0026ndash;535 (2010).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Halloran, K. A. \u0026amp; Conner, R. N. Habitat Used by Brown-headed Nuthatches. \u003cem\u003eBulletin of the Texas Ornithological Society\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 7\u0026ndash;13 (1987).\u003c/li\u003e\n\u003cli\u003eBellamy, P. E. \u003cem\u003eet al.\u003c/em\u003e The influences of habitat, landscape structure and climate on local distribution patterns of the nuthatch (Sitta europaea L.). \u003cem\u003eOecologia\u003c/em\u003e \u003cstrong\u003e115\u003c/strong\u003e, 127\u0026ndash;136 (1998).\u003c/li\u003e\n\u003cli\u003eMurray, N. J., Keith, D. A., Simpson, D., Wilshire, J. H. \u0026amp; Lucas, R. M. Remap: An online remote sensing application for land cover classification and monitoring. \u003cem\u003eMethods Ecol Evol\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 2019\u0026ndash;2027 (2018).\u003c/li\u003e\n\u003cli\u003eQGIS Development Team. QGIS Geographic Information System. Preprint at (2019).\u003c/li\u003e\n\u003cli\u003eMatthysen, E., Adriaensen, F., Dhondt, A. A. \u0026amp; Dhondt, A. A. Dispersal distances of nuthatches, Sitta europaea, in a highly fragmented forest habitat. \u003cem\u003eOikos\u003c/em\u003e \u003cstrong\u003e72\u003c/strong\u003e, 375 (1995).\u003c/li\u003e\n\u003cli\u003eKeating, K. I. M. A. \u0026amp; Cherry, S. USE AND INTERPRETATION OF LOGISTIC REGRESSION IN HABITAT-SELECTION STUDIES. \u003cem\u003eJournal of Wildlife Management\u003c/em\u003e \u003cstrong\u003e68\u003c/strong\u003e, 774\u0026ndash;789 (2004).\u003c/li\u003e\n\u003cli\u003eBates, D., Maechler, M., Bolker, B. \u0026amp; Walker, S. Fitting linear mixed-effects models using lme4. \u003cem\u003eJ Stat Softw\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, 1\u0026ndash;48 (2015).\u003c/li\u003e\n\u003cli\u003eShaffer, T. L. A unified approach to analyzing nest success. \u003cem\u003eAuk\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 526\u0026ndash;540 (2004).\u003c/li\u003e\n\u003cli\u003eTabachnick, B. G. \u0026amp; Fidell, L. S. \u003cem\u003eUsing Multivariate Statistics\u003c/em\u003e. (Allyn \u0026amp; Bacon, Boston, Massachusetts., 2001).\u003c/li\u003e\n\u003cli\u003eMayfield, H. Suggestions for calculating nest success. \u003cem\u003eWilson Bull\u003c/em\u003e 456\u0026ndash;466 (1975).\u003c/li\u003e\n\u003cli\u003eSchielzeth, H. Simple means to improve the interpretability of regression coefficients. \u003cem\u003eMethods Ecol Evol\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 103\u0026ndash;113 (2010).\u003c/li\u003e\n\u003cli\u003eR Development Core Team. RStudio: Integrated Development for R. Preprint at (2019).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Spatial scales (cavity, nest-tree, forest-stand and landscape) assessed to explore habitat preference and fitness of White-throated treerunner (\u003cem\u003ePygarrhychas albogularis\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.130637636080872%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpatial scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.94245723172628%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.130637636080872%\" rowspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eCavity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"top\"\u003e\n \u003cp\u003eFresh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.94245723172628%\" valign=\"top\"\u003e\n \u003cp\u003e1: Cavity excavated during the observation year; 0: Cavity excavated in previous seasons.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eEntrance diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eHorizontal diameter entrance of the excavated cavities (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eDCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eDiameter at cavity height of nest trees (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eHeight\u0026nbsp;above the ground\u0026nbsp;of the excavated cavities on tree (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eAspect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eCardinal orientations of the excavated cavities in degrees (0\u0026deg; - 360\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eCavity volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eInternal cavity volume calculated as volume of cylinder (cm\u003csup\u003e3\u003c/sup\u003e)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eBranch order\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eOrder of tree branches where excavated cavity was located. 1: main trunk; 2 secondary branch; 3: tertiary branch; and so forth.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eSubdecay class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eDegree of decomposition of the tree branch where excavated cavity was located\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.130637636080872%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eNest-tree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"top\"\u003e\n \u003cp\u003eTree species\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.94245723172628%\" valign=\"top\"\u003e\n \u003cp\u003eTree species where nests were found.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eTree DBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eDiameter at breast height of nest trees\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eDecay class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eDegree of decomposition of the nest trees.\u0026nbsp;Decay classes assigned were 1 (live healthy tree); 2 (live unhealthy tree); 3 (recently dead tree); and 4 (long dead tree; modified from Thomas\u0026nbsp;et al. 1979; Edworthy\u0026nbsp;et al.\u0026nbsp;2012).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.130637636080872%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003eForest-stand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"top\"\u003e\n \u003cp\u003eTree density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.94245723172628%\" valign=\"top\"\u003e\n \u003cp\u003eDensity of total trees in one hectare\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eAverage DBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eMean DBH of total trees in forest-stand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eStandard deviation DBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eStandard deviation of DBH in total trees on forest-stand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eDecay class mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eMode of degree decomposition of the total trees in forest-stand\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eBasal area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eBasal area of forest-stand (cm\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.130637636080872%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eLandscape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.926905132192847%\" valign=\"top\"\u003e\n \u003cp\u003eForest edge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.94245723172628%\" valign=\"top\"\u003e\n \u003cp\u003eNearest distance to forest edge from to nest tree (m).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.92035398230089%\" valign=\"top\"\u003e\n \u003cp\u003eForest area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.07964601769912%\" valign=\"top\"\u003e\n \u003cp\u003eForest area in circular buffer of 3 ha with nest tree in the center (m\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Characteristics of nesting habitats (cavities and tree scales) and fitness (measured as nesting success and the number of fledglings) of\u0026nbsp;White-throated treerunners\u0026nbsp;(\u003cem\u003ePygarrhychas albogularis\u003c/em\u003e; n = 65). Values are presented as percentages or mean \u0026plusmn; standard deviation (range).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhite-throated treerunners\u0026rsquo; nests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eLandscape scale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eForest edge\u0026nbsp;(m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e482.1\u0026nbsp;\u0026plusmn; 807.7 (0 - 2851.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eForest area\u0026nbsp;(m\u003csup\u003e1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e25882.1\u0026nbsp;\u0026plusmn; 4403.4 (9880 - 29499)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eForest-stand scale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eTree density (tree/ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e771\u0026nbsp;\u0026plusmn; 366 (25 - 1550)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eAverage DBH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e31.4\u0026nbsp;\u0026plusmn; 7.7 (20.2 - 59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eStandard deviation DBH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e16.2\u0026nbsp;\u0026plusmn; 11.2 (6.9 - 86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eDecay class mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e2\u0026nbsp;\u0026plusmn; 0.6 (1 - 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTree scale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eTree species (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eNothofagus obliqua\u003c/em\u003e: 61.5\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNothofagus dombeyi\u003c/em\u003e: 12.3\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eNothofagus pumilio\u003c/em\u003e: 10.8\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePersea lingue\u003c/em\u003e: 9.2\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eEucryphia cordifolia\u003c/em\u003e: 6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eTree DBH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e40.7\u0026nbsp;\u0026plusmn;\u0026nbsp;20.3\u0026nbsp;(14.1 - 123.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eDecay class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e3.2\u0026nbsp;\u0026plusmn; 1.0 (1 \u0026ndash; 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eCavity scale\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eFresh (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003eFresh cavities: 89.2\u003c/p\u003e\n \u003cp\u003eNon-fresh cavities: 10.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eEntrance diameter (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e3.8\u0026nbsp;\u0026plusmn;\u0026nbsp;0.6 (2.5 \u0026ndash; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eDCH (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e21.5\u0026nbsp;\u0026plusmn;\u0026nbsp;11.5 (9 \u0026ndash; 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eHeight (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e9.6\u0026nbsp;\u0026plusmn;\u0026nbsp;4.1 (1.1 \u0026ndash; 17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eAspect (\u0026deg;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e145.4 \u0026plusmn;\u0026nbsp;106.5\u0026nbsp;(4 \u0026ndash; 348)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eCavity volumen (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;1,349.1 \u0026plusmn; 755.2 (147.3 \u0026ndash; 4,417.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eBranch order (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003eMain trunk: 64.6\u003c/p\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e order branch: 30.8\u003c/p\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e order branch: 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eSubstrate decay class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e3.7\u0026nbsp;\u0026plusmn; 0.7 (2 \u0026ndash; 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFitness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eClutch size (# eggs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e3.2\u0026nbsp;\u0026plusmn;\u0026nbsp;0.8 (1 \u0026ndash; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eNesting success (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003eSuccessful: 75.03 (CI = 62.77 \u0026ndash; 89.62)\u003c/p\u003e\n \u003cp\u003eunsuccessful: 24.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"44.086021505376344%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of fledglings (# chicks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"55.913978494623656%\" valign=\"top\"\u003e\n \u003cp\u003e3.0\u0026nbsp;\u0026plusmn; 1.2 (1 \u0026ndash; 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3. Model rankings (for White-throated treerunners (\u003cem\u003ePygarrhychas albogularis\u003c/em\u003e) habitat preference, number of fledglings, and nest success in relation to the four spatial scales assessed in its nesting sites in south temperate forests, Chile. Season and site were random terms in all models. Bold indicates best-supported models. \u003csup\u003ea\u0026nbsp;\u003c/sup\u003eNumber of parameters estimated. \u003csup\u003eb\u0026nbsp;\u003c/sup\u003eDifference in AICc values between each model and the lowest AICc model (we show the list of models until the first one with ∆AIC \u0026gt; 2). \u003csup\u003ec\u0026nbsp;\u003c/sup\u003eAICc model weight. \u003csup\u003ed\u003c/sup\u003e Log likelihood.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eK\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAICc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e∆AIC\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eW\u003csub\u003ei\u003c/sub\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLL\u003csup\u003ed\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e(a) Habitat preference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBH + Decay + Basal area + Forest area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e113.7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-47.8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eDBH + Decay + Basal area + Forest area + SD DBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e117.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-47.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(b) Nesting success\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntrance diameter +\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;forest edge + height\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.07 \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-41.44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eEntrance diameter + height \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e95.4 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e0.26 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.06 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-42.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eForest edge + height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e95.9 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e0.81 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-42.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e96.1 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.05 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-44.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eDBH + entrance diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e96.4 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.27 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.04 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-43.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eEntrance diameter +\u0026nbsp;forest edge + height + tree density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e96.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-41.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eEntrance diameter + DBH + height\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-42.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eForest edge + height + tree density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-42.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eDBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e91.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-44.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eDBH + Forest edge + height + entrance diameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e97.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-41.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(c) Number of fledglings\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDBH + decay class\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e102.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e-42.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e103.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.19 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-47.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eDecay class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e103.6 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.47 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.17 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-44.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eDBH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e103.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-45.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.33868378812199%\" valign=\"top\"\u003e\n \u003cp\u003eASP + ASP\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e+ DBH + decay clas\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.93900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.088282504012842%\" valign=\"top\"\u003e\n \u003cp\u003e104.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\" valign=\"top\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.593900481540931%\" valign=\"top\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.804173354735152%\" valign=\"top\"\u003e\n \u003cp\u003e-40.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3914394/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3914394/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnimals select their habitats from available resources in a way that should maximize fitness, and thus habitat preferences are generally predicted to be adaptive. However, there may be a mismatch between habitat preferences and fitness due to factors such as limited availability or disturbance of breeding habitats. In this study, we examine whether preferred nesting habitat attributes are linked to fitness (nesting success and number of fledglings) of White-throated treerunner (\u003cem\u003ePygarrhichas albogularis\u003c/em\u003e), an obligate excavator and tree cavity nester across four spatial scales: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) cavity, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) nest-tree, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) forest-stand, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) landscape. During eight breeding seasons (October to February), between 2010 and 2018, we found and monitored 65 treerunner nests in Andean Temperate Forests, Chile. Our results show a multiscale response for both habitat preferences and fitness: both nest-tree and landscape scales were the most influential variables for nesting habitat preferences, while all habitat scales influenced fitness. The probability that a given habitat is used for nesting increased with larger trees, advanced tree decay classes, and forest cover. Nesting success was positively related with cavity entrance diameter, height, and distance from the forest edge. On the other hand, the number of fledglings decrease with larger trees and increase with decay class. Our findings suggest a match between habitat preferences and fitness. Finally, treerunners prefer areas with a relatively high forest cover and their nesting success increased with relatively lower tree density, suggesting that old-growth forests comprise the best integration of multiscale habitat attributes for this species.\u003c/p\u003e","manuscriptTitle":"From Tree-cavity to Landscape: Habitat Preferences and Fitness Operates Across Scales for an Old Relict Species of Southern South-america","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 09:16:30","doi":"10.21203/rs.3.rs-3914394/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-09T05:00:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-08T19:33:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-02T18:11:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d93464a2-1d7a-446c-9e70-53195cd163df","date":"2024-03-28T02:53:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38148c7d-fee3-44b4-b446-d09992e3acbd_SNPRID","date":"2024-03-27T21:09:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-27T17:50:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-27T17:47:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-22T05:53:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-22T05:50:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-31T15:54:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edb6ac79-080a-4eb0-8661-0ddea0c5c4a3","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29893077,"name":"Biological sciences/Ecology/Biodiversity"},{"id":29893078,"name":"Biological sciences/Ecology/Conservation"},{"id":29893079,"name":"Biological sciences/Ecology/Forest ecology"}],"tags":[],"updatedAt":"2025-10-13T16:02:53+00:00","versionOfRecord":{"articleIdentity":"rs-3914394","link":"https://doi.org/10.1038/s41598-025-93594-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-10-06 15:58:21","publishedOnDateReadable":"October 6th, 2025"},"versionCreatedAt":"2024-03-26 09:16:30","video":"","vorDoi":"10.1038/s41598-025-93594-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93594-1","workflowStages":[]},"version":"v1","identity":"rs-3914394","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3914394","identity":"rs-3914394","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}