Landscape Ecol (2019) 34:503–519 https://doi.org/10.1007/s10980-019-00788-w (0123456789().,-volV)(0123456789().,-volV) RESEARCH ARTICLE Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas Ho Yi Wan . Samuel A. Cushman . Joseph L. Ganey Received: 22 May 2018 / Accepted: 18 February 2019 / Published online: 4 March 2019 Ó This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019 Abstract converted the models into landscape resistance sur- Context Habitat loss and fragmentation are the most faces and used simulations to model connectivity pressing threats to biodiversity, yet assessing their corridors for the species, and created composite impacts across broad landscapes is challenging. habitat and connectivity models by averaging the Information on habitat suitability is sometimes avail- local and non-local models. able in the form of a resource selection function model Results While the local and the non-local models developed from a different geographical area, but its both performed well, the local model performed best applicability is unknown until tested. in the part of the study area where it was built, but Objectives We used the Mexican spotted owl as a performed worse in areas that are beyond the extent of case study to demonstrate how models developed from the data used to train it. The composite habitat model different geographic areas affect our predictions for improved performances over both models in most habitat suitability, landscape resistance, and connec- cases. tivity. We identified the most suitable habitats and Conclusions With rigorous testing, multi-scale habi- core areas for dispersal and movement for the species. tat selection models built on empirical data from other Methods We applied two multi-scale habitat selec- geographical areas can be useful. Averaging predic- tion models—a local model and a non-local model— tions of multiple models can improve performance, to a broad study area in northern Arizona. We but the effectiveness is subject to the performance of the reference models. Electronic supplementary material The online version of Keywords Connectivity Á Corridor Á Endangered this article (https://doi.org/10.1007/s10980-019-00788-w) species Á Fragmentation Á Habitat loss Á Habitat contains supplementary material, which is available to autho- rized users. selection Á Landscape resistance Á Mexican spotted owl Á Resource selection function Á Scale H. Y. Wan (&) School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ 86011, USA e-mail: [email protected] Introduction S. A. Cushman Á J. L. Ganey Species extinction is accelerating exponentially as a Rocky Mountain Research Station, USDA Forest Service, 2500 S. Pine Knoll, Flagstaff, AZ 86001, USA consequence of intensified anthropogenic activities 123 504 Landscape Ecol (2019) 34:503–519 (Barnosky et al. 2011; Ceballos et al. 2015; De Vos loss and fragmentation from past timber management et al. 2015). Habitat loss and fragmentation are activities and increasing uncharacteristically large and considered the most pressing threats to biodiversity severe fires (U.S. Department of the Interior 2012). and the leading causes of species extinction (Fahrig Landscape-scale restoration and fuels reduction treat- 2003; Pimm et al. 2014; Newbold et al. 2015). Climate ments that aim to reduce risk of large and high-severity change compounds these threats, exerting further wildfires and promote forest health and resilience have stress on species and exacerbating habitat and biodi- been planned and implemented, with some treatment versity loss (Thomas et al. 2004; Bellard et al. 2012). areas coinciding with habitats used by the Mexican Fragmentation diminishes connectivity and dispersal, spotted owl (U.S. Department of Agriculture 2014). leading to reduced gene flow, smaller effective Some treatments may aid in reducing habitat loss from population size, and increased genetic drift and wildfires in the long run (Ager et al. 2007; Waltz et al. inbreeding, all of which accelerate local species 2014; Roccaforte et al. 2015; Jones et al. 2016; Chiono extinction (Frankham 1996; Young and Clarke 2000; et al. 2017; Ziegler et al. 2017). Conversely, because Stockwell et al. 2003). One of the greatest challenges Mexican spotted owls typically nest in areas with high to assessing the impacts of habitat loss and fragmen- canopy cover, some treatments may degrade owl tation on populations and their connectivity is how to habitat in the short run (Meiman et al. 2003; Seamans reliably predict and model these effects across broad and Gutie´rrez 2007; Odion et al. 2014; Tempel et al. landscapes. Often the only information available is in 2014; Bond 2016; Stephens et al. 2016). Identifying the form of a model developed from another geo- important movement areas and linkages between core graphical area or time period. However, extrapolating habitats provides critical information for designing research findings and model predictions beyond the treatment plans that can sustain the imperiled species spatial or temporal scope of inference suggested by a while meeting management objectives in various study is generally scientifically indefensible and may locations. lead to incorrect findings and misdirected manage- In this study, we compared habitat suitability and ment decisions (Burnham and Anderson 1998; Cush- landscape resistance predictions produced for a com- man et al. 2013b). mon region by two different modeling studies con- The validity of a model is uncertain when it is ducted on the Mexican spotted owl in different parts of applied to a different landscape, because the accuracy its range. Both studies used multi-scale resource and error rates relevant to the original landscape do not selection functions to predict and map habitat suit- necessarily apply to a new landscape, especially when ability. We applied both of these models to a wide area the two landscapes differ in ecological traits, such as in the range of the Mexican spotted owl and used biophysical characteristics, climate, and disturbance individual-based simulations and resistant kernel regimes (Cushman et al. 2013b). If several high- connectivity modeling to predict landscape connec- performance models are available from different tivity for the Mexican spotted owl based on each of geographic areas, it is important to take into account these two habitat models, and evaluated the similar- all of them. One rigorous approach to this is to conduct ities and differences in their predictions. We also a meta-analysis, which is an analytical process that tested different dispersal distance thresholds in the combines the habitat attribute values for all of the models to deal with uncertainty regarding the dispersal models and creates a generalized model (Hedges and ability of the Mexican spotted owl. In addition, we Olkin 1985; Gurevitch and Hedges 1993; Gates 2002). created a composite model based on the two resource Another valuable approach is to quantitatively com- selection functions to see if it would improve the pare the predictions of the models developed in accuracy of predictions. different places when applied in the same location We had three goals: (1) to evaluate how models (Wan et al. 2017). This enables the researcher to developed from different geographic areas affected formally describe the differences in their predictions. our predictions for habitat suitability, landscape The Mexican spotted owl (Strix occidentalis resistance, and connectivity; (2) to identify the most lucida) is a listed Threatened species under the suitable habitats for the Mexican spotted owl by Endangered Species Act (U.S. Department of the comparing predictions from different habitat selection Interior 1993). The main threat to the owl is habitat models; and (3) to identify core areas important for 123 Landscape Ecol (2019) 34:503–519 505 dispersal and movement of the Mexican spotted owl Methods by comparing predictions from different models. We had several a priori hypotheses: First, we expected that Study area the habitat model developed locally would outperform the habitat model developed for a non-local region. The study spanned the area along and south of the Second, we expected that the local model would Mogollon Rim in Arizona (latitude 32.6–35.4°N, perform better in the part of the study area where it was longitude 108.6–112.1°W; Fig. 1). It covered an area built than in parts of the study area that were beyond of approximately 9.3 million ha and encompassed the the extent of the data used to train it. Third, we full extent of the Apache-Sitgreaves, Coconino, and expected that the model predictions would differ more Tonto National Forests. The boundary of the study for predictions of habitat suitability than predictions of area was defined by the farthest latitude and longtitude connectivity, since connectivity spatially smooths of these forests, extended by a 5000-m buffer. local habitat differences. Elevation ranged from 300 to 3846 m. Mean annual precipitation ranged from * 50 mm to [ 5800 mm. A diverse array of vegetation occurred along the elevational climatic gradient (McClaran and Brady 1994). The lowest elevation consisted of desert and semi-desert ecosystems that were populated by arid shrub and grassland species including big sagebrush (Artemisia tridentata). Above the desert and semi-