Dataset Information

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

ABSTRACT: LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses (PCA). We then analyzed wood mouse's habitat preferences using three different models based on: (i) field PCs predictors, (ii) LiDAR PCs predictors; and (iii) both set of predictors in a combined model, including a variance partitioning analysis. Elevation was also included as a predictor in the three models. Our results indicate that LiDAR derived variables were better predictors than field-based variables. The model combining both data sets slightly improved the predictive power of the model. Field derived variables indicated that wood mouse was positively influenced by the gradient of increasing shrub cover and negatively affected by elevation. Regarding LiDAR data, two LiDAR PCs, i.e. gradients in canopy openness and complexity in forest vertical structure positively influenced wood mouse, although elevation interacted negatively with the complexity in vertical structure, indicating wood mouse's preferences for plots with lower elevations but with complex forest vertical structure. The combined model was similar to the LiDAR-based model and included the gradient of shrub cover measured in the field. Variance partitioning showed that LiDAR-based variables, together with elevation, were the most important predictors and that part of the variation explained by shrub cover was shared. LiDAR derived variables were good surrogates of environmental characteristics explaining habitat preferences by the wood mouse. Our LiDAR metrics represented structural features of the forest patch, such as the presence and cover of shrubs, as well as other characteristics likely including time since perturbation, food availability and predation risk. Our results suggest that LiDAR is a promising technology for further exploring habitat preferences by small mammal communities.

SUBMITTER: Jaime-Gonzalez C

PROVIDER: S-EPMC5542549 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

Jaime-González Carlos C Acebes Pablo P Mateos Ana A Mezquida Eduardo T ET

PloS one 20170803 8

LiDAR technology has firmly contributed to strengthen the knowledge of habitat structure-wildlife relationships, though there is an evident bias towards flying vertebrates. To bridge this gap, we investigated and compared the performance of LiDAR and field data to model habitat preferences of wood mouse (Apodemus sylvaticus) in a Mediterranean high mountain pine forest (Pinus sylvestris). We recorded nine field and 13 LiDAR variables that were summarized by means of Principal Component Analyses ...[more]

PMID: 28771566

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Project description:BACKGROUND:Monitoring and managing carbon stocks in forested ecosystems requires accurate and repeatable quantification of the spatial distribution of wood volume at landscape to regional scales. Grid-based forest inventory networks have provided valuable records of forest structure and dynamics at individual plot scales, but in isolation they may not represent the carbon dynamics of heterogeneous landscapes encompassing diverse land-management strategies and site conditions. Airborne LiDAR has greatly enhanced forest structural characterisation and, in conjunction with field-based inventories, it provides avenues for monitoring carbon over broader spatial scales. Here we aim to enhance the integration of airborne LiDAR surveying with field-based inventories by exploring the effect of inventory plot size and number on the relationship between field-estimated and LiDAR-predicted wood volume in deciduous broad-leafed forest in central Germany. RESULTS:Estimation of wood volume from airborne LiDAR was most robust (R2 = 0.92, RMSE = 50.57 m3 ha-1 ~14.13 Mg C ha-1) when trained and tested with 1 ha experimental plot data (n = 50). Predictions based on a more extensive (n = 1100) plot network with considerably smaller (0.05 ha) plots were inferior (R2 = 0.68, RMSE = 101.01 ~28.09 Mg C ha-1). Differences between the 1 and 0.05 ha volume models from LiDAR were negligible however at the scale of individual land-management units. Sample size permutation tests showed that increasing the number of inventory plots above 350 for the 0.05 ha plots returned no improvement in R2 and RMSE variability of the LiDAR-predicted wood volume model. CONCLUSIONS:Our results from this study confirm the utility of LiDAR for estimating wood volume in deciduous broad-leafed forest, but highlight the challenges associated with field plot size and number in establishing robust relationships between airborne LiDAR and field derived wood volume. We are moving into a forest management era where field-inventory and airborne LiDAR are inextricably linked, and we encourage field inventory campaigns to strive for increased plot size and give greater attention to precise stem geolocation for better integration with remote sensing strategies.

Project description:Avian malaria and related haemosporidian parasites (genera Haemoproteus, Plasmodium, and Leucocytozoon) affect bird demography, species range limits, and community structure, yet they remain unsurveyed in most bird communities and populations. We conducted a community-level survey of these vector-transmitted parasites in New Mexico, USA, to describe their diversity, abundance, and host associations. We focused on the breeding-bird community in the transition zone between piñon-juniper woodland and ponderosa pine forests (elevational range: 2,150-2,460 m). We screened 186 birds representing 49 species using both standard PCR and microscopy techniques to detect infections of all three avian haemosporidian genera. We detected infections in 68 out of 186 birds (36.6%), the highest proportion of which were infected with Haemoproteus (20.9%), followed by Leucocytozoon (13.4%), then Plasmodium (8.0%). We sequenced mtDNA for 77 infections representing 43 haplotypes (25 Haemoproteus, 12 Leucocytozoon, 6 Plasmodium). When compared to all previously known haplotypes in the MalAvi and GenBank databases, 63% (27) of the haplotypes we recovered were novel. We found evidence for host specificity at the avian clade and species level, but this specificity was variable among parasite genera, in that Haemoproteus and Leucocytozoon were each restricted to three avian groups (out of six), while Plasmodium occurred in all groups except non-passerines. We found striking variation in infection rate among host species, with nearly universal infection among vireos and no infection among nuthatches. Using rarefaction and extrapolation, we estimated the total avian haemosporidian diversity to be 70 haplotypes (95% CI [43-98]); thus, we may have already sampled ∼60% of the diversity of avian haemosporidians in New Mexico pine forests. It is possible that future studies will find higher diversity in microhabitats or host species that are under-sampled or unsampled in the present study. Fortunately, this study is fully extendable via voucher specimens, frozen tissues, blood smears, parasite images, and documentation provided in open-access databases (MalAvi, GenBank, and ARCTOS).

Dataset Information

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

Publications

Bridging gaps: On the performance of airborne LiDAR to model wood mouse-habitat structure relationships in pine forests.

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