Complementarity-based reserve selection algorithms efficiently prioritize sites for biodiversity conservation, but they are data-intensive and most regions lack accurate distribution maps for the majority of species. We explored implications of basing conservation planning decisions on incomplete and biased data using occurrence records of the plant family Proteaceae in South Africa. Treating this high-quality database as 'complete', we introduced three realistic sampling biases characteristic of biodiversity databases: a detectability sampling bias and two forms of roads sampling bias. We then compared reserve networks constructed using complete, biased, and randomly sampled data. All forms of biased sampling performed worse than both the complete data set and equal-effort random sampling. Biased sampling failed to detect a median of 1-5% of species, and resulted in reserve networks that were 9-17% larger than those designed with complete data. Spatial congruence and the correlation of irreplaceability scores between reserve networks selected with biased and complete data were low. Thus, reserve networks based on biased data require more area to protect fewer species and identify different locations than those selected with randomly sampled or complete data.

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%...

Despite lessons from terrestrial systems, conservation efforts in marine systems continue to focus on identifying priority sites for protection based on high species richness inferred from range maps. Range maps oversimplify spatial variability in animal distributions by assuming uniform distribution within range and de facto giving equal weight to critical and marginal habitats. We used Marxan ver. 2.43 to compare species richness-based systematic reserve network solutions using information about marine mammal range and relative abundance. At a global scale, reserve network solutions were strongly sensitive to model inputs and assumptions. Solutions based on different input data overlapped by a third at most, with agreement as low as 10% in some cases. At a regional scale, species richness was inversely related to density, such that species richness hotspots excluded highest-density areas for all species. Based on these findings, we caution that species-richness estimates derived from range maps and used as input in conservation planning exercises may inadvertently lead to protection of largely marginal habitat.

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Context. Conservation planning is increasingly using "coarse filters" based on the idea of conserving "nature's stage". One such approach is based on ecosystems and the concept of ecological integrity, although myriad ways exist to measure ecological integrity.Objectives. To describe our ecosystem-based index of ecological integrity (IEI) and its derivative 5 index of ecological impact (ecoImpact), and illustrate their applications for conservation assessment and planning in the northeastern United States.Methods. We characterized the biophysical setting of the landscape at the 30 m cell resolution using a parsimonious suite of settings variables. Based on these settings variables and mapped ecosystems, we computed a suite of anthropogenic stressor metrics reflecting intactness (i.e., 10 freedom from anthropogenic stressors) and resiliency metrics (i.e., connectivity to similar neighboring ecological settings), quantile-rescaled them by ecosystem and geographic extent, and combined them in a weighted linear model to create IEI. We used the change in IEI over time under a land use scenario to compute ecoImpact.Results. We illustrated the calculation of IEI and ecoImpact to compare the ecological integrity 15 consequences of a 70-year projection of urban growth to an alternative scenario involving securing a network of conservation core areas (reserves) from future development.Conclusions. IEI and ecoImpact offer an effective way to assess ecological integrity across the landscape and examine the potential ecological consequences of alternative land use and land cover scenarios to inform conservation decision making. 20