Predicting abundance of desert riparian birds: validation and calibration of the Effective Area Model.

Reliable prediction of the effects of landscape change on species abundance is critical to land managers who must make frequent, rapid decisions with long-term consequences. However, due to inherent temporal and spatial variability in ecological systems, previous attempts to predict species abundance in novel locations and/or time frames have been largely unsuccessful. The Effective Area Model (EAM) uses change in habitat composition and geometry coupled with response of animals to habitat edges to predict change in species abundance at a landscape scale. Our research goals were to validate EAM abundance predictions in new locations and to develop a calibration framework that enables absolute abundance predictions in novel regions or time frames. For model validation, we compared the EAM to a null model excluding edge effects in terms of accurate prediction of species abundance. The EAM outperformed the null model for 83.3% of species (N=12) for which it was possible to discern a difference when considering 50 validation sites. Likewise, the EAM outperformed the null model when considering subsets of validation sites categorized on the basis of four variables (isolation, presence of water, region, and focal habitat). Additionally, we explored a framework for producing calibrated models to decrease prediction error given inherent temporal and spatial variability in abundance. We calibrated the EAM to new locations using linear regression between observed and predicted abundance with and without additional habitat covariates. We found that model adjustments for unexplained variability in time and space, as well as variability that can be explained by incorporating additional covariates, improved EAM predictions. Calibrated EAM abundance estimates with additional site-level variables explained a significant amount of variability (P < 0.05) in observed abundance for 17 of 20 species, with R2 values >25% for 12 species, >48% for six species, and >60% for four species when considering all predictive models. The calibration framework described in this paper can be used to predict absolute abundance in sites different from those in which data were collected if the target population of sites to which one would like to statistically infer is sampled in a probabilistic way.     

A Framework to Evaluate Ecological and Social Outcomes of Collaborative Management: Lessons from Implementation with a Northern Arizona Collaborative Group

As collaborative groups gain popularity as an alternative means for addressing conflict over management of public lands, the need for methods to evaluate their effectiveness in achieving ecological and social goals increases. However, frameworks that examine both effectiveness of the collaborative process and its outcomes are poorly developed or altogether lacking. This paper presents and evaluates the utility of the holistic ecosystem health indicator (HEHI), a framework that integrates multiple ecological and socioeconomic criteria to evaluate management effectiveness of collaborative processes. Through the development and application of the HEHI to a collaborative in northern Arizona, the Diablo Trust, we present the opportunities and challenges in using this framework to evaluate the ecological and social outcomes of collaborative adaptive management. Baseline results from the first application of the HEHI are presented as an illustration of its potential as a co-adaptive management tool. We discuss lessons learned from the process of selecting indicators and potential issues to their long-term implementation. Finally, we provide recommendations for applying this framework to monitoring and adaptive management in the context of collaborative management.     

Techniques and Guidelines for Monitoring Neotropical Butterflies

Long-term monitoring of selected species can identify changes in biological diversity, permitting the timely adjustment of management activities to reverse or avoid undesired trends. This paper addresses several related issues bearing on the development of inexpensive and easily implemented monitoring programs for tropical butterflies. First, we discuss the use of butterflies as ecological indicators. Next, we present field evaluations of butterfly sampling techniques, indicating that: (1) light-gap size greatly affects sampling results in forests and should be of critical concern in site selection and sampling design; (2) baited traps and visual censuses provide complementary data on butterfly abundances; (3) monitoring a subset of locally common butterfly species can provide data for comparing community composition and relative abundance of species in areas where species inventories are incomplete. Drawing on these results, we develop guidelines for designing monitoring programs. These address the formulation of explicit questions to be addressed through monitoring and the selection of appropriate study sites, study species, sampling techniques, and sampling frequency. A protocol for the ongoing butterfly monitoring program that emerged from these studies is appended. The techniques and guidelines presented here are intended to serve as an adaptable model for biologists designing monitoring projects to help guide applied conservation efforts in the tropics.     

Efficient species-level monitoring at the landscape scale

Monitoring the population trends of multiple animal species at a landscape scale is prohibitively expensive. However, advances in survey design, statistical methods, and the ability to estimate species presence on the basis of detection-nondetection data have greatly increased the feasibility of species-level monitoring. For example, recent advances in monitoring make use of detection-nondetection data that are relatively inexpensive to acquire, historical survey data, and new techniques in genetic evaluation. The ability to use indirect measures of presence for some species greatly increases monitoring efficiency and reduces survey costs. After adjusting for false absences, the proportion of sample units in a landscape where a species is detected (occupancy) is a logical state variable to monitor. Occupancy monitoring can be based on real-time observation of a species at a survey site or on evidence that the species was at the survey location sometime in the recent past. Temporal and spatial patterns in occupancy data are related to changes in animal abundance and provide insights into the probability of a species' persistence. However, even with the efficiencies gained when occupancy is the monitored state variable, the task of species-level monitoring remains daunting due to the large number of species. We propose that a small number of species be monitored on the basis of specific management objectives, their functional role in an ecosystem, their sensitivity to environmental changes likely to occur in the area, or their conservation importance.