Jonathon J. Valente

Site occupancy models are widely used to estimate species distributions from presence–nondetection data, yet for mobile animals, the true proportion of sites occupied varies in time. Thus, true occupancy and estimates generated from different protocols are affected by temporal characteristics of survey design. Here we tested whether we could standardize occupancy estimates across different sampling protocols with a post hoc statistical test. Using an individual‐based movement model of Wood Thrush ( Hylocichla mustelina ), we simulated point count surveys across 3000 populations under 90 spatially and temporally variable sampling protocols. For each simulation, we fit a standard occupancy model, then used minute‐level detections to truncate surveys and generate occupancy estimates across increasing survey lengths. We fit asymptotic models to these series to estimate intercepts (instantaneous occupancy) and asymptotes (longer‐term occupancy) and evaluated how well they approximated true instantaneous, daily and seasonal occupancy. We were unable to recover instantaneous or seasonal occupancy from any approach. Both standard occupancy models and asymptotic regression (the top‐performing asymptotic model) most closely approximated daily occupancy; although asymptotic regression produced slightly less biased estimates, it showed substantially greater variability, resulting in greater overall error. Our results show that post hoc analytical approaches cannot, at present, reliably standardize occupancy estimates across heterogeneous survey designs. Instead, ensuring biologically informative and comparable estimates will depend on standardized survey protocols. Practical implication : Monitoring programs using presence–nondetection data should prioritize consistent survey protocols when estimating occupancy for mobile species, with a focus on daily occupancy as the most attainable and useful metric. Although the highest accuracy and precision were achieved under the most intensive survey protocols, for point counts of Wood Thrush and similar species, four consecutive 15–20 min surveys provide near‐optimal precision and can be completed in a single visit, making them well suited for estimating relative occupancy under logistical constraints. Three 20‐min surveys spaced ~24 h apart closely approximate daily occupancy while maintaining reasonable precision. Developing similar protocols for other species and survey methods will be critical to ensuring comparable occupancy estimates across space and time.

Headwater wetlands in the southeast United States are a ubiquitous feature in the Coastal Plain but difficult to map because of their transitional nature. These wetlands are important for regional drainage and there is a need to better document their occurrence on the landscape. To improve mapping and detection, we evaluated the Wetland Intrinsic Potential (WIP) tool due to its use of multi-scale topographic indices, hydrologic proxies, and random forest procedures that contribute to ’cryptic’ wetland detection. Our resulting WIP model identified metrics that most accurately depicted the wetland-upland matrix within the Bushy Creek – Dyas Creek watershed, near Bay Minette, Alabama, USA. An initial model was trained and validated with an out of bag (OOB) accuracy assessment on a spatial subset of the watershed to predict wetland presence, absence, and extent. Our model resulted in an OOB accuracy rate of 96.0%, with wetland omission and commission errors of 7.0% and 2.5%, respectively. The model was then applied to the remaining extent of the watershed for a secondary validation assessment. Overall accuracy for the secondary validation dataset was 92.3%, with wetland omission and commission errors of 14.0% and 4.5%, respectively. Thus, WIP reliably discerned wetlands from uplands, with an AUC of 0.98 and kappa coefficient of 0.83. Maps generated with WIP had 89.1% agreement with National Wetland Inventory maps, although they increased wetland area by as much as 8.7%. Our findings demonstrate the applicability and limitations of this method for wetland mapping and supports future models which explore land use/cover and hydrogeomorphic relationships with wetlands.

Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging‐driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood. We combine two decades of Landsat‐derived habitat metrics with 29,000 surveys of the marbled murrelet ( Brachyramphus marmoratus )—an iconic Pacific Northwest old‐forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape‐scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate‐driven ocean conditions. Comparing alternative landscape‐scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set‐aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change. Synthesis and applications: We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest‐dependent species amid rising timber demand.

Presence-absence surveys are frequently used to monitor populations of rare and elusive species. Such data may also be used as a proxy for breeding activity, but links between presence-absence data and higher-order processes must be validated to determine their reliability. The Marbled Murrelet (Brachyramphus marmoratus) is a threatened seabird that nests in older-aged forests along the Pacific Coast. Its nests are exceptionally difficult to find, so we tested whether presence-absence surveys can help identify nesting sites. Between 2018 and 2022 we located 17 trees containing active murrelet nests in the Oregon Coast Range (USA) and 38 trees that purportedly contained no active nests (26 in occupied murrelet stands and 12 in unoccupied stands). Observers surveyed within 200 m of focal trees using standard presence-absence surveys, and we modeled the effects of site status (active nest or control) and distance from the focal tree on probability of recording murrelets. We never detected murrelets in unoccupied control sites. We found some evidence that the probability of recording presence was higher at active nesting sites (0.81, 95% CI: 0.71, 0.88) than at occupied control sites (0.71, 95% CI: 0.64, 0.78) although a null model had similar support. The probability of recording murrelet breeding behaviors in nesting and occupied control sites was 0.20 (95% CI: 0.14, 0.27) regardless of distance to a known active nest. These results suggest that presence-absence surveys may be useful for identifying plausible murrelet nesting habitat, but they are ineffective for identifying active nesting sites. Moreover, we estimated that 20 repeated surveys at a point in space are required to reasonably conclude there are no active nesting sites within 200 m. These findings serve as an important reminder of the limitations that can come with relying on presence-absence data alone to identify breeding sites.

Occupancy models estimate distributions of imperfectly detected species, but violations of the closure assumption can bias results. However, researchers working with mobile animals may find it impossible to eliminate such violations. Here, we tested the hypothesis that occupancy models fit to realistic sampling data can generate unbiased occupancy estimates for an itinerant Wood Thrush (Hylocichla mustelina) population. In 2013 and 2014, we tracked movements of 41 breeding Wood Thrush males. We modelled territory shift probabilities using logistic exposure models and within‐territory movements using continuous‐time stochastic process models. We then constructed an individual‐based model, simulated (1000 iterations) spatiotemporal locations for individuals and simulated sampling these populations using 162 different point count protocols with variable spatial (sampling radius and point placement method), and temporal (survey length, between‐survey intervals and number of surveys) characteristics. We compared occupancy estimates with true values of instantaneous, daily and seasonal occupancy from the simulations. We parameterized continuous time stochastic process models based on movements within 34 unique territories and estimated a daily territory shift probability of 0.0099 (95% CI: 0.0060, 0.0152). Simulated data indicated that estimates of occupancy ranged from 0.18 (0.06, 1.00) to 0.80 (0.71, 0.89) depending on protocol characteristics. Occupancy estimates increased with increasing survey radius, survey length and between‐survey interval. Protocols using shorter surveys and between‐survey intervals were good estimators for instantaneous occupancy (low bias and mean‐squared error) but poor estimators for daily and seasonal occupancy; longer surveys and intervals generated unbiased estimators of daily occupancy but underestimated seasonal occupancy. Logistic regression models that ignored imperfect detection outperformed occupancy models for estimating instantaneous occupancy but not daily or seasonal occupancy. For mobile animals, occupancy of sampling sites changes in space and time. Consequently, the spatial and temporal aspects of a sampling protocol have strong, but predictable, effects on occupancy model parameter estimates. Our results demonstrate that how these factors interact is critical for designing surveys that produce occupancy estimates representative of the biological process of interest to a researcher.

Understanding how habitat fragmentation affects individual species is complicated by challenges associated with quantifying species‐specific habitat and spatial variability in fragmentation effects within a species’ range. We aggregated a 29‐year breeding survey data set for the endangered marbled murrelet ( Brachyramphus marmoratus ) from >42,000 forest sites throughout the Pacific Northwest (Oregon, Washington, and northern California) of the United States. We built a species distribution model (SDM) in which occupied sites were linked with Landsat imagery to quantify murrelet‐specific habitat and then used occupancy models to test the hypotheses that fragmentation negatively affects murrelet breeding distribution and that these effects are amplified with distance from the marine foraging habitat toward the edge of the species’ nesting range. Murrelet habitat declined in the Pacific Northwest by 20% since 1988, whereas the proportion of habitat comprising edges increased by 17%, indicating increased fragmentation. Furthermore, fragmentation of murrelet habitat at landscape scales (within 2 km of survey stations) negatively affected occupancy of potential breeding sites, and these effects were amplified near the range edge. On the coast, the odds of occupancy decreased by 37% (95% confidence interval [CI] –54 to 12) for each 10% increase in edge habitat (i.e., fragmentation), but at the range edge (88 km inland) these odds decreased by 99% (95% CI 98 to 99). Conversely, odds of murrelet occupancy increased by 31% (95% CI 14 to 52) for each 10% increase in local edge habitat (within 100 m of survey stations). Avoidance of fragmentation at broad scales but use of locally fragmented habitat with reduced quality may help explain the lack of murrelet population recovery. Further, our results emphasize that fragmentation effects can be nuanced, scale dependent, and geographically variable. Awareness of these nuances is critical for developing landscape‐level conservation strategies for species experiencing broad‐scale habitat loss and fragmentation.

Conspecific presence can indicate the location or quality of resources, and animals settling near conspecifics often gain fitness benefits. This can result in adaptive conspecific attraction during breeding habitat selection as demonstrated in numerous terrestrial, territorial birds. There is growing interest in using simulated conspecific social cues (e.g., decoys, broadcasted vocalizations) to manage bird distributions, yet it remains unclear when this approach is likely to succeed. We reviewed published studies to evaluate whether the strength of conspecific attraction in terrestrial birds is mediated by characteristics of species (life history traits), simulated cues (e.g., timing and duration), sites (e.g., quality), and how conspecific attraction was measured. We identified 31 experiments that simulated social cues and compared conspecific settlement between treatment and control sites. We then used phylogenetically controlled meta-regression to assess the impacts of 19 moderators on settlement. Nearly all species included in these experiments were migratory passerines, and social cues generally had a strong, positive influence on their settlement decisions, as the odds of site occupancy were 3.12× (95% CI: 0.81–11.69) greater in treatment sites relative to control sites. Within this group, conspecific attraction was evolutionarily conserved with ≥25.5% (95% CI: 5.1%–65.4%) of the variance in treatment effects explained by phylogenetic relatedness. However, we found no evidence that any covariates influenced the response to social cues, and we posit this stems from limited research specifically designed to identify the mechanisms mediating conspecific attraction. We therefore developed a research agenda that provides a framework for testing mechanistic hypotheses regarding how cue characteristics, species traits, and spatial contexts may mediate attraction to conspecifics. Evaluating these hypotheses will greatly advance the field by helping managers understand when, where, and why simulating social cues can be used to enhance populations of species that are of conservation concern.