Use of LiDAR in the conservation management of the endangered red squirrel (<i>Sciurus vulgaris L.</i>)

Flaherty, Silvia; Lurz, Peter W. W.

doi:10.1117/1.jrs.8.083592

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…; Flaherty et al. ). A matter of primary interest is the relative importance of ecological parameters that are the causal, driving factors for a species distribution (Guisan and Zimmermann ).…”

Section: Introductionmentioning

confidence: 99%

“…During the last two decades, species distribution models (SDMs) and habitat suitability maps have become an increasingly important tool to understand the ecology and occurrence of species and to support their conservation (Guisan and Thuiller 2005;Guisan et al 2013). SDMs are used to assess the effects of environmental and land use changes on species richness and distribution (Zaniewski et al 2002;Jeschke and Strayer 2008;Braunisch et al 2014;Vogeler et al 2014;Breiner et al 2015), to investigate specific habitat requirements of species (Vierling et al 2011;Farrell et al 2013;Zellweger et al 2013) or as basis for priority site selection in species conservation management Flaherty et al 2014). A matter of primary interest is the relative importance of ecological parameters that are the causal, driving factors for a species distribution (Guisan and Zimmermann 2000).…”

Section: Introductionmentioning

confidence: 99%

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Remotely sensed forest habitat structures improve regional species conservation

Rechsteiner

Zellweger

Gerber³

et al. 2017

Remote Sens Ecol Conserv

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Recent studies show that light detection and ranging (LiDAR) derived habitat variables significantly increase the performance and accuracy of species distribution models (SDMs). In particular, the structure of complex habitats such as forest can be accurately parametrized by an area-wide, LiDAR-based vegetation profile. However, evidence of specific applications of such models in real-world conservation management still remains sparse. Here, we developed a resource selection SDM for hazel grouse (Bonasa bonasia L.) in a Swiss nature park with the aim to map habitat suitability and to inform the park management about habitat improvement measures. We used remote sensing, particularly LiDAR to derive ecologically relevant forest vegetation characteristics at the local scale and used them as predictors in an ensemble SDM approach. The predicted habitat suitability was mainly affected by local, fine grained vegetation structure. Average vegetation height, shrub density and canopy height variation contributed most to the habitat quality for hazel grouse. This clearly shows how LiDAR provides the means to develop ecologically interpretable predictor variables of forest habitat structure and that these predictors can be used to reliably map local-scale habitat quality, indicated by high model performance scores (median AUC of 0.918). This improves spatial conservation planning, and at the same time, provides meaningful information to derive habitat improvement measures that can be implemented in the field by foresters. Hazel grouse occurrence in the park is restricted to a few highly suitable, disjunct habitat patches. Therefore, conservation management should increase the connectivity of suitable habitat with the aim to stimulate an increase and better exchange of individuals in the regional hazel grouse population. Habitat improvements can be achieved by forestry measures that regularly integrate early successional forest stages into production forests. They should contain stands with a shrub density of around 30% as well as heterogeneous stands in terms of vegetation height.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remotely sensed forest habitat structures improve regional species conservation

Rechsteiner

Zellweger

Gerber³

et al. 2017

Remote Sens Ecol Conserv

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“…This makes use of lidar data as an explanatory tool to increase understanding (and quantification) of resource selection by species of known distribution [13]. Such studies have most frequently focussed on bird species [14][15][16][17], but with a growing number examining mammals, such as the bald-faced saki monkey (Pithecia irrorata) [18], Pacific fisher (Martes pennanti) [19], moose (Alces alces) [20], and red squirrel (Sciurus vulgaris) [21].…”

Section: Introductionmentioning

confidence: 99%

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

Hill

Hinsley

2015

Remote Sensing

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Structure is a fundamental physical element of habitat, particularly in woodlands, and hence there has been considerable recent uptake of airborne lidar data in forest ecology studies. This paper investigates the significance of lidar data characteristics when modelling organism-habitat relationships, taking a single species case study in a mature woodland ecosystem. We re-investigate work on great tit (Parus major) habitat, focussing on bird breeding data from 1997 and 2001 (years with contrasting weather conditions and a demonstrated relationship between breeding success and forest structure). We use a time series of three lidar data acquisitions across a 12-year period (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). The lidar data characteristics assessed include time-lag with field data (up to 15 years), spatial sampling density (average post spacing in the range of 1 pulse per 0.14 m 2 -17.77 m 2 ), approach to processing (raster or point cloud), and the complexity of derived structure metrics (with a total of 33 metrics assessed, each generated separately using all returns and only first returns). Ordinary least squares regression analysis was employed to investigate relationships between great tit mean nestling body mass, calculated per brood, and the various canopy structure measures from all lidar datasets. For the 2001 bird breeding data, the relationship between mean nestling body mass and mean canopy height for a sample area around each nest was robust to the extent that it could be detected strongly and with a OPEN ACCESS Remote Sens. 2015, 7 3447 high level of statistical significance, with relatively little impact of lidar data characteristics. In 1997, all relationships between lidar structure metrics and mean nestling body mass were weaker than in 2001 and more sensitive to lidar data characteristics, and in almost all cases they were opposite in trend. However, whilst the optimum habitat structure differed between the two study years, the lidar-derived metrics that best characterised this structure were consistent: canopy height percentiles and mean overstorey canopy height (calculated using all returns or only first returns) and the standard deviation of canopy height (calculated using all returns). Overall, our results suggest that for relatively stable woodland habitats, ecologists should not feel prohibited in using lidar data to explore or monitor organism-habitat relationships because of perceived data quality issues, as long as the questions investigated, the scale of analysis, and the interpretation of findings are appropriate for the data available.

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“…This approach can make use of the latest digital opportunities like open-source code and LIDAR data (light detection and ranging): a powerful system using light to produce new-dimensional spatial data over land cover and height ( Ohse et al (2009) for underlying GIS model concepts). LIDAR has proven to perform well in ecological modelling for squirrels, especially since it allows for the display of habitat heterogeneity and structural features ( Flaherty, Lurz & Patenaude, 2014 ).…”

Section: Introductionmentioning

confidence: 99%

High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR

Robold

Huettmann

2021

PeerJ

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American red squirrels (Tamiasciurus hudsonicus) are small mammals that are abundantly distributed throughout North America. Urbanization in the Anthropocene is now a global process, and squirrels live in affected landscapes. This leads to squirrels adjusting to human developments. Not much is known about the distribution of squirrels and squirrel middens near humans, especially not in the subarctic and sub-urbanized regions. Although this species is hunted, there are no real publicly available distribution and abundance estimates nor management plans and bag limits for squirrels in Alaska or in the United States known by us, except the endangered Mt. Graham squirrel. In general, insufficient squirrel conservation research is carried out; they are underrepresented in research and its literature. To further the science-based management for such species, this study aims to generate the first digital open access workflow as a generic research template for small mammal work including the latest machine learning of open source and high-resolution LIDAR data in an Open Source Geographic Information System (QGIS) and ArcGIS. Machine learning has proven to be less modeler biased and improve accuracy of the analysis outcome, therefore it is the preferred approach. This template is designed to be rapid, simple, robust, generic and effective for being used by a global audience. As a unique showcase, here a squirrel midden survey was carried out for two years (2016 and 2017). These squirrel middens were detected in a research area of 45,5 hectares (0,455 km2) in downtown Fairbanks, interior boreal forest of Alaska, U.S. Transect distances were geo-referenced with a GPS and adjusted to the visual conditions to count all squirrel middens within the survey area. Different layers of proximity to humans and habitat characteristics were assembled using aerial imagery and LIDAR data (3D data needed for an arboreal species like the red squirrels) consisting of a 3 × 3 m resolution. The layer data was used to train a predictive distribution model for red squirrel middens with machine learning. The model showed the relative index of occurrence (RIO) in a map and identified canopy height, distance to trails, canopy density and the distance to a lake, together, as the strongest predictors for squirrel midden distribution whereas open landscape and disturbed areas are avoided. It is concluded that squirrels select for high and dense forests for middens while avoiding human disturbance. This study is able to present a machine learning template to easily and rapidly produce an accurate abundance prediction which can be used for management implications.

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Use of LiDAR in the conservation management of the endangered red squirrel (Sciurus vulgaris L.)

Cited by 10 publications

References 32 publications

Remotely sensed forest habitat structures improve regional species conservation

Remotely sensed forest habitat structures improve regional species conservation

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR

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