Estimating Temporal Trend in the Presence of Spatial Complexity: A Bayesian Hierarchical Model for a Wetland Plant Population Undergoing Restoration

Monitoring programs that evaluate restoration and inform adaptive management are important for addressing environmental degradation. These efforts may be well served by spatially explicit hierarchical approaches to modeling because of unavoidable spatial structure inherited from past land use patterns and other factors. We developed Bayesian hierarchical models to estimate trends from annual density counts observed in a spatially structured wetland forb (Camassia quamash [camas]) population following the cessation of grazing and mowing on the study area, and in a separate reference population of camas. The restoration site was bisected by roads and drainage ditches, resulting in distinct subpopulations (“zones”) with different land use histories. We modeled this spatial structure by fitting zone-specific intercepts and slopes. We allowed spatial covariance parameters in the model to vary by zone, as in stratified kriging, accommodating anisotropy and improving computation and biological interpretation. Trend estimates provided evidence of a positive effect of passive restoration, and the strength of evidence was influenced by the amount of spatial structure in the model. Allowing trends to vary among zones and accounting for topographic heterogeneity increased precision of trend estimates. Accounting for spatial autocorrelation shifted parameter coefficients in ways that varied among zones depending on strength of statistical shrinkage, autocorrelation and topographic heterogeneity—a phenomenon not widely described. Spatially explicit estimates of trend from hierarchical models will generally be more useful to land managers than pooled regional estimates and provide more realistic assessments of uncertainty. The ability to grapple with historical contingency is an appealing benefit of this approach

Lidar-derived canopy architecture predicts brown creeper occupancy of two western coniferous forests

In western conifer-dominated forests where the abundance of old-growth stands is decreasing, species such as the Brown Creeper (Certhia americana) may be useful as indicator species for monitoring the health of old-growth systems because they are strongly associated with habitat characteristics associated with old growth and are especially sensitive to forest management. Light detection and ranging (lidar) is useful for acquiring fine-resolution, three-dimensional data on vegetation structure across broad areas. We evaluated Brown Creeper occupancy of forested landscapes by using lidar-derived canopy metrics in two coniferous forests in Idaho. Density of the upper canopy was the most important variable for predicting Brown Creeper occupancy, although mean height and height variability were also included in the top models. The upper canopy was twice as dense and the mean height was almost 50% higher at occupied than at unoccupied sites. Previous studies have found indicators of canopy density to be important factors for Brown Creeper habitat; however, this represents the first time that lidar data have been used to examine this relationship empirically through the mapping of the upper canopy density that cannot be continuously quantified by field-based methods or passive remote sensing. Our model’s performance was classified as “good” by multiple criteria. We were able to map probabilities of Brown Creeper occupancy in ~50 000 ha of forest, probabilities that can be used at the local, forest-stand, and landscape scales, and illustrate the potential utility of lidar-derived data for studies of avian distributions in forested landscapes.En los bosques dominados por coníferas del oeste, donde está disminuyendo la abundancia de rodales maduros, las especies como Certhia americana pueden ser útiles como especies indicadoras para monitorear la salud de los sistemas maduros debido a que están fuertemente asociadas con las características del hábitat vinculadas con el bosque maduro y son especialmente sensibles al manejo del bosque. El sistema de detección y alcance de luz (denominado lidar, un acrónimo del inglés “light detection and ranging”) es útil para adquirir datos tridimensionales de alta resolución de la estructura de la vegetación a través de grandes áreas. Evaluamos la ocupación de C. americana de paisajes boscosos usando métricas del dosel derivadas de lidar en dos bosques de coníferas en Idaho. La densidad del dosel alto fue la variable más importante para predecir la ocupación de C. americana, aunque la altura media y la variabilidad de la altura también fueron incluidas en los mejores modelos. El dosel alto fue dos veces más denso y la altura media fue casi 50% más alta en los sitios ocupados que en los sitios desocupados. Estudios previos han encontrado que los indicadores de densidad del dosel son factores importantes del hábitat de C. americana; sin embargo, esto representa la primera vez que datos de lidar han sido usados para examinar esta relación de modo empírico a través del mapeo de la densidad del dosel alto, de un modo continuo que no puede ser cuantificado por métodos basados en trabajo de campo o muestreo remoto pasivo. El desempeño de nuestro modelo fue clasificado como “bueno” por múltiples criterios. Fuimos capaces de mapear las probabilidades de ocupación de C. americana en ~50 000 ha de bosque, probabilidades que pueden ser usadas a las escalas local, de rodal de bosque y de paisaje, y que ilustran la utilidad potencial de los datos derivados de lidar para estudios de distribución de aves en paisajes boscosos.Keywords: Brown Creeper, Certhia americana, mapping, habitat, forest, occupancy, lida

Climate Change, Woodpeckers, and Forests: Current Trends and Future Modeling Needs

The structure and composition of forest ecosystems are expected to shift with climate‐induced changes in precipitation, temperature, fire, carbon mitigation strategies, and biological disturbance. These factors are likely to have biodiversity implications. However, climate‐driven forest ecosystem models used to predict changes to forest structure and composition are not coupled to models used to predict changes to biodiversity. We proposed integrating woodpecker response (biodiversity indicator) with forest ecosystem models. Woodpeckers are a good indicator species of forest ecosystem dynamics, because they are ecologically constrained by landscape‐scale forest components, such as composition, structure, disturbance regimes, and management activities. In addition, they are correlated with forest avifauna community diversity. In this study, we explore integrating woodpecker and forest ecosystem climate models. We review climate–woodpecker models and compare the predicted responses to observed climate‐induced changes. We identify inconsistencies between observed and predicted responses, explore the modeling causes, and identify the models pertinent to integration that address the inconsistencies. We found that predictions in the short term are not in agreement with observed trends for 7 of 15 evaluated species. Because niche constraints associated with woodpeckers are a result of complex interactions between climate, vegetation, and disturbance, we hypothesize that the lack of adequate representation of these processes in the current broad‐scale climate–woodpecker models results in model–data mismatch. As a first step toward improvement, we suggest a conceptual model of climate–woodpecker–forest modeling for integration. The integration model provides climate‐driven forest ecosystem modeling with a measure of biodiversity while retaining the feedback between climate and vegetation in woodpecker climate change modeling

How much does the time lag between wildlife field-data collection and LiDAR-data acquisition matter for studies of animal distributions? A case study using bird communities

Vegetation structure quantified by light detection and ranging (LiDAR) can improve understanding of wildlife occupancy and species-richness patterns. However, there is often a time lag between the collection of LiDAR data and wildlife data. We investigated whether a time lag between the LiDAR acquisition and field-data acquisition affected mapped wildlife distributions ranging from an individual species distribution to total avian species richness in a conifer forest. We collected bird and LiDAR data in 2009 across a 20,000 ha forest in northern Idaho. Using the 2009 LiDAR data, we modelled the probability of occurrence for the brown creeper (Certhia americana). Using the same 2009 LiDAR data, we additionally modelled total avian species richness and richness of three different bird nesting guilds (ground/understory, mid/upper canopy and cavity). We mapped brown creeper occupancy probability and species richness using the 2009 models, and then compared these maps with maps based on the same models applied to a 2003-LiDAR dataset. A prior study identified areas harvested between 2003 and 2009. There was on average a 5% absolute decrease in mapped probabilities of brown creeper occurrence in non-harvest areas between 2003 and 2009. Species richness changed by less than one species in all cases within non-harvest areas between the 2003 and 2009 maps. Although these comparisons were statistically significant at the p < 0.0001 level, it is likely that the high number of map cells (~480,000) influenced this result. Similar patterns between our 2003 and 2009 maps in non-harvest areas for this suite of avian responses suggests that a 6-year difference between field-data collection and LiDAR-data collection has a minimal effect on mapped avian patterns in an undisturbed coniferous forest. However, because this is one case study in one ecosystem, additional work examining the effect of temporal lags between LiDAR and field-data collection on mapping wildlife distributions is warranted in additional ecosystems

Evaluating GEDI data fusions for continuous characterizations of forest wildlife habitat

Continuous characterizations of forest structure are critical for modeling wildlife habitat as well as for assessing trade-offs with additional ecosystem services. To overcome the spatial and temporal limitations of airborne lidar data for studying wide-ranging animals and for monitoring wildlife habitat through time, novel sampling data sources, including the space-borne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument, may be incorporated within data fusion frameworks to scale up satellite-based estimates of forest structure across continuous spatial extents. The objectives of this study were to: 1) investigate the value and limitations of satellite data sources for generating GEDI-fusion models and 30 m resolution predictive maps of eight forest structure measures across six western U.S. states (Colorado, Wyoming, Idaho, Oregon, Washington, and Montana); 2) evaluate the suitability of GEDI as a reference data source and assess any spatiotemporal biases of GEDI-fusion maps using samples of airborne lidar data; and 3) examine differences in GEDI-fusion products for inclusion within wildlife habitat models for three keystone woodpecker species with varying forest structure needs. We focused on two fusion models, one that combined Landsat, Sentinel-1 Synthetic Aperture Radar, disturbance, topographic, and bioclimatic predictor information (combined model), and one that was restricted to Landsat, topographic, and bioclimatic predictors (Landsat/topo/bio model). Model performance varied across the eight GEDI structure measures although all representing moderate to high predictive performance (model testing R2 values ranging from 0.36 to 0.76). Results were similar between fusion models, as well as for map validations for years of model creation (2019–2020) and hindcasted years (2016–2018). Within our wildlife case studies, modeling encounter rates of the three woodpecker species using GEDI-fusion inputs yielded AUC values ranging from 0.76–0.87 with observed relationships that followed our ecological understanding of the species. While our results show promise for the use of remote sensing data fusions for scaling up GEDI structure metrics of value for habitat modeling and other applications across broad continuous extents, further assessments are needed to test their performance within habitat modeling for additional species of conservation interest as well as biodiversity assessments

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies

Habitat Selection of Lewis\u27 Woodpeckers in Southeastern Colorado

Volume: 109Start Page: 121End Page: 13

Incorporating remotely sensed tree canopy cover data into broad scale assessments of wildlife habitat distribution and conservation

Remote sensing provides critical information for broad scale assessments of wildlife habitat distribution and conservation. However, such efforts have been typically unable to incorporate information about vegetation structure, a variable important for explaining the distribution of many wildlife species. We evaluated the consequences of incorporating remotely sensed information about horizontal vegetation structure into current assessments of wildlife habitat distribution and conservation. For this, we integrated the new NLCD tree canopy cover product into the US GAP Analysis database, using avian species and the finished Idaho GAP Analysis as a case study. We found: (1) a 15-68% decrease in the extent of the predicted habitat for avian species associated with specific tree canopy conditions, (2) a marked decrease in the species richness values predicted at the Landsat pixel scale, but not at coarser scales, (3) a modified distribution of biodiversity hotspots, and (4) surprising results in conservation assessment: despite the strong changes in the species predicted habitats, their distribution in relation to the reserves network remained the same. This study highlights the value of area wide vegetation structure data for refined biodiversity and conservation analyses. We discuss further opportunities and limitations for the use of the NLCD data in wildlife habitat studies

Relationships among Vegetation Structure, Canopy Composition, and Avian Richness Patterns across an Aspen-Conifer Forest Gradient

Ecologists have a long-term interest in understanding the relative influence of vegetation composition and vegetation structure on avian diversity. LiDAR remote sensing is useful in studying local patterns of avian diversity because it characterizes fine-scale vegetation structure across broad extents. We used LiDAR, aerial and satellite imagery, and avian field data to investigate the relative influence of vegetation structure and canopy composition on avian richness across an aspen-conifer forest gradient. Aspen enhances forest avian biodiversity but has been declining across western North America. We conducted bird surveys between 2013 and 2014 in plots with a range of aspen-conifer canopy composition. We found aspen to support higher avian richness than conifer, especially among cavity nesters. In contrast to other studies, we found weak relationships between vegetation structure and avian richness. Although primary cavity excavator richness was negatively influenced by canopy density, canopy composition was the most important variable influencing total richness and nesting guild richness. This study adds to the body of literature utilizing LiDAR-derived metrics to better understand local patterns of avian diversity, and provides perspectives on how avian communities might respond to conifer encroachment into aspen