Geology topics

William DeLuca

Publications and source records attributed to William DeLuca.

6 recordsLinked to original sources

USGS2024

Boom and bust: The effects of masting on seed predator range dynamics and trophic cascades

Aim Spatiotemporal variation in resource availability is a strong driver of animal distributions. In the northern hardwood and boreal forests of the northeastern United States, tree mast events provide resource pulses that drive the population dynamics of small mammals, including the American red squirrel ( Tamiasciurus hudsonicus ), a primary songbird nest predator. This study sought to determine whether mast availability ameliorates their abiotic limits, enabling red squirrel elevational distributions to temporarily expand and negatively impact high-elevation songbirds. Location Northeastern United States. Methods We used two independent datasets to evaluate our hypotheses. First, we fit a dynamic occupancy model using data from camera trap surveys to evaluate red squirrel distributional responses to pulses in the tree mast. We also assessed population responses using systematic auditory surveys analysed with an open-population binomial mixture model. Further, we used modelled red squirrel abundance in nest-survival models to evaluate whether their abundance is correlated with the daily nest survival of three songbird species. Results The tree mast provided a critical resource pulse that resulted in a two-fold increase in the annual elevational distribution of red squirrels. The elevational distribution of red squirrels ranged from a minimum of ~450 m (range: 663–1145 m asl) following two consecutive years without a masting event to a maximum of over 1000 m (range: 443–1545 m asl) after a large mast event. The daily nest survival of three songbird species tended to decline with an increase in the abundance of red squirrels. Main Conclusions Tree mast is a central biological phenomenon in many temperate and boreal forests. This study reveals how this resource pulse results in range changes in a small mammal that is both a seed and bird predator, as well as prey for many carnivores. Thus, understanding this phenomenon can inform the conservation and management of northern forests, including breeding songbirds.

Connecticut, Maine, Massachusetts, New Hampshire, ↗

USGS2022

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter datasets versus pseudo-absence locations during migratory periods, and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and post-breeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations, when compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings, and thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach for integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.

Ecological Applications↗

USGS2020

Examining the mechanisms of species responses to climate change: Are there biological thresholds?

Climate-change-driven shifts in distribution and abundance have been documented in many species. However, in order to better predict species responses, managers are seeking to understand the mechanisms that are driving these changes, including any thresholds that might soon be crossed. Leveraging the research that has already been supported by the Northeast Climate Adaptation Science Center and its partners, this project used the latest modeling techniques combined with robust field data to examine the impact of specific climate variables, land use change, and species interactions on the future distribution and abundance of species of conservation concern. Moreover, this project documented biological thresholds related to climate variability and change for critical species in the Northeastern and Midwestern U.S. Specifically, our objectives were to identify the primary drivers (climate change vs. urban growth) of species distribution changes in the Northeast; examine the nature of species landscape capability change over time to identify potential thresholds; determine how changing temperatures and snowpack characteristics will drive species interactions; analyze the sensitivity of tree and bird responses to the magnitude, variability, periodicity, and seasonality of temperature and precipitation under climate change in the eastern U.S.; and identify how discrete climate triggers such as extreme events will correlate with known biological thresholds. Major outcomes included 1) refining the understanding of the mechanisms that drive projected changes in the distribution of vulnerable populations; and 2) improving how these results are conveyed to stakeholders by identifying understandable responses in the form of thresholds.

Connecticut, Delaware, Illinois, Indiana, Iowa, Ke↗

USGS2017

Landscape capability models as a tool to predict fine-scale forest bird occupancy and abundance

Context Species-specific models of landscape capability (LC) can inform landscape conservation design. Landscape capability is “the ability of the landscape to provide the environment […] and the local resources […] needed for survival and reproduction […] in sufficient quantity, quality and accessibility to meet the life history requirements of individuals and local populations.” Landscape capability incorporates species’ life histories, ecologies, and distributions to model habitat for current and future landscapes and climates as a proactive strategy for conservation planning. Objectives We tested the ability of a set of LC models to explain variation in point occupancy and abundance for seven bird species representative of spruce-fir, mixed conifer-hardwood, and riparian and wooded wetland macrohabitats. Methods We compiled point count data sets used for biological inventory, species monitoring, and field studies across the northeastern United States to create an independent validation data set. Our validation explicitly accounted for underestimation in validation data using joint distance and time removal sampling. Results Blackpoll warbler ( Setophaga striata ), wood thrush ( Hylocichla mustelina ), and Louisiana ( Parkesia motacilla ) and northern waterthrush ( P. noveboracensis ) models were validated as predicting variation in abundance, although this varied from not biologically meaningful (1%) to strongly meaningful (59%). We verified all seven species models [including ovenbird ( Seiurus aurocapilla ), blackburnian ( Setophaga fusca ) and cerulean warbler ( Setophaga cerulea )], as all were positively related to occupancy data. Conclusions LC models represent a useful tool for conservation planning owing to their predictive ability over a regional extent. As improved remote-sensed data become available, LC layers are updated, which will improve predictions.

Landscape Ecology↗

USGS2017

Landscape capability predicts upland game bird abundance and occurrence

Landscape capability (LC) models are a spatial tool with potential applications in conservation planning. We used survey data to validate LC models as predictors of occurrence and abundance at broad and fine scales for American woodcock ( Scolopax minor ) and ruffed grouse ( Bonasa umbellus ). Landscape capability models were reliable predictors of occurrence but were less indicative of relative abundance at route (11.5–14.6 km) and point scales (0.5–1 km). As predictors of occurrence, LC models had high sensitivity (0.71–0.93) and were accurate (0.71–0.88) and precise (0.88 and 0.92 for woodcock and grouse, respectively). Models did not predict point-scale abundance independent of the ability to predict occurrence of either species. The LC models are useful predictors of patterns of occurrences in the northeastern United States, but they have limited utility as predictors of fine-scale or route-specific abundances.

Journal of Wildlife Management↗

USGS2015

Biological responses to climate impacts with a focus on Regional Species of Greatest Conservation Need (RSGCN)

This chapter reviews the responses to climate change on the 367 Regional Species of Greatest Conservation Need (RSGCN) identified by the Northeast Fish and Wildlife Diversity Technical Committee (NEFWDTC), technical experts from states’ natural resource agencies (Appendix 3.1). These species were chosen based on their conservation status, listing in SWAPs, and the percentage of their range that occurs in the Northeast. The objectives of this chapter are to: summarize how regional biodiversity has already responded and is expected to respond to climate change; summarize information on specific RSGCN species responses to climate change to date and anticipated under future scenarios; characterize the greatest uncertainties about how biodiversity and RSGCN species will respond to climate change in the future; and highlight where other factors are expected to exacerbate the effects of climate change. This information was obtained through a systematic review of the peer-reviewed literature, primarily using the ISI Web of Knowledge to search for papers on each species related to “climate”, “temperature”, or “precipitation”. Although we undoubtedly missed some sources, the following allows us to review some of the ways climate change will affect regional species of conservation concern

Connecticut, Delaware, Massachusetts, Maryland, Ma↗