A Multiscale Landscape Approach to Predicting Bird and Moth Rarity Hotspots in a Threatened Pitch Pine–Scrub Oak Community

Abstract

In the northeastern United States, pitch pine ( Pinus rigida Mill.)-scrub oak (Quercus ilicifoliaWang.) communities are increasingly threatened by development and fire suppression, and prioritization of these habitats for conservation is of critical importance. As a basis for local conservation planning in a pitch pine-scrub oak community in southeastern Massachusetts, we developed logistic-regression models based on multiscale landscape and patch variables to predict hotspots of rare and declining bird and moth species. We compared predicted moth distributions with observed species-occurrence records to validate the models. We then quantified the amount of overlap between hotspots to assess the utility of rare birds and moths as indicator taxa. Species representation in hotspots and the current level of hotspot protection were also assessed. Predictive models included variables at all measured scales and resulted in average correct classification rates (optimal cut point) of 85.6% and 89.2% for bird and moth models, respectively. The majority of moth occurrence records were within 100 m of predicted habitat. Only 13% of all bird hotspots and 10% of all moth hotspots overlapped, and only a few small patches in and around Myles Standish State Forest were predicted to be hotspots for both taxa. There was no correlation between the bird and moth species-richness maps across all levels of richness (r = −0.03, p = 0.62). Species representation in hotspots was high, but most hotspots had limited or no protection. Given the lack of correspondence between bird and moth hotspots, our results suggest that use of species-richness indicators for conservation planning may be ineffective at local scales. Based on these results, we suggest that local-level conservation planning in pitch pine-scrub oak communities be based on multitaxa, multiscale approaches.Resumen: En el noreste de Estados Unidos, las comunidades de pino (Pinus rigida Mill.)-encino (Quercus ilicifolia Wang.) están cada vez más amenazadas por el desarrollo y la supresión de fuego y la priorización de esos hábitats es de importancia crítica. Como una base para la planeación de conservación local de una comunidad de pino-encino en el sureste de Massachussets, desarrollamos modelos de regresión logística con base en variables a nivel de paisaje multiescala y de fragmento para predecir lasáreas críticas para especies de aves y polillas raras y en declinación. Para validar los modelos comparamos las distribuciones esperadas de polillas con registros observados de la ocurrencia de especies. Posteriormente cuantificamos el traslape entré areas críticas para evaluar la utilidad de aves y polillas raras como taxa indicadores. Los modelos predictivos incluyeron variables en todas las escalas consideradas y resultaron en tasas promedio de clasificación correcta (punto de corteóptimo) de 85.6% y 89.2% para modelos de aves y polillas, respectivamente. La mayoría de los registros de ocurrencia de polillas estuvo dentro de 100 m del hábitat predicho. Sólo hubo traslape en 13%...

Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain)

“…We hypothesize that, as in previous studies of other species (e.g. Thompson and McGarigal 2002, Grand 2004, Wasserman et al 2012), a predictive model for brown bear occurrence based on the influence of habitat variables acting at multiple optimal scales (1) would generally offer greater specificity, sensitivity, classification accuracy, and predictive power than a model constructed at a single scale and (2) may provide new and important insights into the species-habitat association that would not be apparent if scale is not explicitly optimized in habitat modeling.…”

“…Thompson and McGarigal 2002, Grand et al 2004, Wasserman et al 2012. We predicted relative habitat suitability based on each environmental variable at each scale using the maximum entropy algorithm Maxent (Phillips et al 2006).…”

“…For each variable we selected the scale producing the single variable model with the best performance (highest AUC value) and excluded all other scales for further analysis (e.g. Grand et al 2004, Wasserman et al 2012.…”

“…This emphasizes the importance of evaluating species habitat selection at a range of spatial scales in order to reveal the true grain at which the animals respond to the landscape (Kotliar and Wiens 1990). Recent studies focusing on different species (Thompson and McGarigal 2002, Grand et al 2004, Shirk et al 2010, Wasserman et al 2012 have shown that species often select different habitat variables at widely divergent scales and demonstrated that multiscale approaches to habitat modeling yielded much improved predictions of species occurrence. These studies revealed the power of the bivariate scaling method of systematically varying the scale of analysis for the habitat variables to identify the dominant scales for each ecological process.…”

Multi-scale habitat selection modeling: introduction to the special issue

“…Indeed, how environmental structure at various scales affects the distribution, abundance and fitness of organisms has been a major focus of ecology since its inception. Moreover, there is growing consensus that it is critical not only to identify the operative scale(s) for the expression of these ecological relationships, but that multi-scale approaches to habitat modeling will yield stronger and more reliable inferences (e.g., Grand et al 2004;Holland et al 2004;Wasserman et al 2012;Weaver et al 2012;Shirk et al 2014;Zeller et al 2014). Given longstanding awareness of the importance of scale in species-environment relationships (Wiens 1976) and growing recognition of the advantages of multi-scale approaches for elucidating these relationships (Mayor et al 2009), why do so few studies use multi-scale approaches to identify the relevant operative scale(s)?…”