As geographic range estimates for the IUCN Red List guide conservation actions, accuracy and ecological realism are crucial. IUCN’s extent of occurrence (EOO) is the general region including the species’ range, while area of occupancy (AOO) is the subset of EOO occupied by the species. Data‐poor species with incomplete sampling present particular difficulties, but species distribution models (SDMs) can be used to predict suitable areas. Nevertheless, SDMs typically employ abiotic variables (i.e., climate) and do not explicitly account for biotic interactions that can impose range constraints. We sought to improve range estimates for data‐poor, parapatric species by masking out areas under inferred competitive exclusion. We did so for two South American spiny pocket mice:
- NSF-PAR ID:
- 10290521
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Heteromys australis (Least Concern) andHeteromys teleus (Vulnerable due to especially poor sampling), whose ranges appear restricted by competition. For both species, we estimated EOO using SDMs and AOO with four approaches: occupied grid cells, abiotic SDM prediction, and this prediction masked by approximations of the areas occupied by each species’ congener. We made the masks using support vector machines (SVMs) fit with two data types: occurrence coordinates alone; and coordinates along with SDM predictions of suitability. Given the uncertainty in calculating AOO for low‐data species, we made estimates for the lower and upper bounds for AOO, but only make recommendations forH. teleus as its full known range was considered. The SVM approaches (especially the second one) had lower classification error and made more ecologically realistic delineations of the contact zone. ForH. teleus , the lower AOO bound (a strongly biased underestimate) corresponded to Endangered (occupied grid cells), while the upper bounds (other approaches) led to Near Threatened. As we currently lack data to determine the species’ true occupancy within the post‐processed SDM prediction, we recommend that an updated listing forH. teleus include these bounds for AOO. This study advances methods for estimating the upper bound of AOO and highlights the need for better ways to produce unbiased estimates of lower bounds. More generally, the SVM approaches for post‐processing SDM predictions hold promise for improving range estimates for other uses in biogeography and conservation. -
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Abstract Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision‐makers prepare for reintroductions, range shifts, reductions and/or expansions by predicting habitat suitability across landscapes. Yet, range‐expanding or ‐shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an
integrated SDM database (iSDMdb ).The
iSDMdb is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user‐friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces and management priorities. To illustrate its utility, we used the endangered New Zealand sea lionPhocarctos hookeri . We consulted with wildlife rangers, decision‐makers and sea lion experts to supplement SDM predictions with additional, more realistic and applicable information for management.Almost half the data fields included in this database resulted from engaging with these end‐users during our study. The SDM found 395 predicted sites. However, the
iSDMdb 's additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range‐expanding or ‐shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The
iSDMdb addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers and decision‐makers. We exemplify how to build aniSDMdb using open‐source tools, and how to make diverse, complex assessments more accessible for end‐users. -
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Abstract Species distribution models (SDMs) that rely on regional‐scale environmental variables will play a key role in forecasting species occurrence in the face of climate change. However, in the Anthropocene, a number of local‐scale anthropogenic variables, including wildfire history, land‐use change, invasive species, and ecological restoration practices can override regional‐scale variables to drive patterns of species distribution. Incorporating these human‐induced factors into SDMs remains a major research challenge, in part because spatial variability in these factors occurs at fine scales, rendering prediction over regional extents problematic. Here, we used big sagebrush (
Artemisia tridentata Nutt.) as a model species to explore whether including human‐induced factors improves the fit of the SDM. We applied a Bayesian hurdle spatial approach using 21,753 data points of field‐sampled vegetation obtained from the LANDFIRE program to model sagebrush occurrence and cover by incorporating fire history metrics and restoration treatments from 1980 to 2015 throughout the Great Basin of North America. Models including fire attributes and restoration treatments performed better than those including only climate and topographic variables. Number of fires and fire occurrence had the strongest relative effects on big sagebrush occurrence and cover, respectively. The models predicted that the probability of big sagebrush occurrence decreases by 1.2% (95% CI: −6.9%, 0.6%) when one fire occurs and cover decreases by 44.7% (95% CI: −47.9%, −41.3%) if at least one fire occurred over the 36 year period of record. Restoration practices increased the probability of big sagebrush occurrence but had minimal effect on cover. Our results demonstrate the potential value of including disturbance and land management along with climate in models to predict species distributions. As an increasing number of datasets representing land‐use history become available, we anticipate that our modeling framework will have broad relevance across a range of biomes and species. -
null (Ed.)Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters.more » « less
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Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).
Location Eastern USA.
Time period 2017–2018.
Taxa studied Trees.
Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.
Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.
Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41598-021-96047-7
