Pattern Oriented Modeling (POM) for Indirect Estimation of Helper & Floater Dispersal Behavior on Camp Lejeune Individual-based, spatially-explicit population models (SEPMs): mechanistic models capable of predicting dynamics of non-equilibrium populations Landscape Equivalency Analysis (LEA) (Bruggeman et al 2005) Generally-applicable landscape-scale accounting system “Jeopardy standard” evaluated based on changes in Probability of Regional Extinction due to trade “Take standard” evaluated based on expected changes in abundance To incorporate fragmentation effects, credits valued based on a trades ability to move the balance between recruitment and migration closer to levels observed in a “baseline” landscape - Baseline represents the habitat allocation in which rates of recruitment and migration are balanced to minimize genetic drift and inbreeding while maintaining genetic variance at two spatial scales “Landscape-equivalent patches” make equal contributions to abundance and genetic variance within and among breeding groups without increasing the Probability of Regional Extinction Doug Bruggeman, T. Wiegand, & M. L. Jones Dept. of Fisheries & Wildlife, Michigan State University. UFZ Dept. Ecological Modelling, Leipzig, Germany When Do We Know Enough to Re-Allocate Listed Species’ Habitat ? Background Installations are often required to re-allocate listed species habitat to achieve military readiness. However, we are uncertain how changing landscape patterns (i.e., habitat area & connectivity) affects population viability. For example, some species require dispersal and/or variation in rates of recruitment among breeding groups to achieve regional, or landscape, persistence. Yet, we will never be completely certain how changing landscape patterns over time affects population viability. We recognize three main challenges posed by habitat trading policies and propose three solutions. accepted models Step 1. Identify range of uncertain parameter values Step 2. Assemble parameter values at random generating ~100,000 SEPMs Step 3. Test the ability of the 100,000 different model parameterizations to recreate patterns observed in nature For connectivity, prediction error estimated using Mantel’s correlation coefficient between observed and expected patterns. Parameter error should decrease as we demand smaller prediction errors (stronger filter) Step 4. Remove parameterizations with high model prediction error (filtering) 719 models An SEPM for the Red-cockaded Woodpecker (RCW) was constructed to simulate the cooperative breeding system (Letcher et al. 1998) Observed pattern of female connectivity on Camp Lejeune (orange = 1 of bird shared between territories; blue = 0) Integration of Landscape Equivalency Analysis & Pattern Oriented Modeling for the Red-cockaded Woodpecker on Camp Lejeune Uncertain Dispersal Parameters Range considered Probability of male & female floater survival0.8 – 1.0 Floater dispersal steps per season km Perceptual distance of helpers and floaters km d = probability of turning 0°, 45°, 90°, 135° m1 to m8 = permeability of 8 land cover classes to dispersal B = strength of directional vs. forest-based dispersal0 – 1.0 We are still uncertain how males and females disperse to find breeding vacancies. Therefore, parameter values that simulate these behaviors were varied across 100,000 SEPMs. We then tested the ability of each model parameterization to recreate patterns observed in nature. Five patterns were derived from Dr. J. Walters long term bird banding study (i.e., number of potential breeding groups over time, number of floaters over time, group size, connectivity, and pairwise minimum genetic distance). These observed patterns were used to estimate a prediction error for each of the 100,000 SEPMs. Prediction errors representing the degree of agreement between observed and expected patterns were estimated using squared deviation and correlation coefficients. Here describe the four basic steps of POM, but simplicity we report prediction errors for only one pattern, connectivity, or the number of birds shared among territories. 8 models Results. Our ability to reduce uncertainty varied across the dispersal parameter values considered. We were able to remove 92% of possible parameter values for male helper perceptual distance and 80% of possible parameter values for both female floater perceptual distance and steps taken per season. Therefore, these parameters are critical for reproducing patterns observed on Camp Lejeune. In other words, a narrow range of parameter values are needed to minimize prediction error. In contrast, only about 22% of possible parameter values for permeability of land cover classes were removed during POM. Therefore, these parameter values are not critical for reproducing patterns observed on Camp Lejeune. However, RCW territories are so aggregated on-base and perceptual distance is great enough that it is not surprising that permeability of land cover is not critical. This remaining uncertainty should be reflected in decision making, so we used all 8 remaining SEPMs to evaluate habitat trades. For the SEED project, we used LEA to evaluate hypothetical habitat trading scenarios for Camp Lejeune. LEA estimates tradable credits as “Landscape Service Years” (LSYs), which is a time integrated estimate of the proportional change in ecological service due to a change in landscape pattern. LSYs are calculated by comparing services (i.e., abundance and genetic diversity) expected under different landscape configurations. Due to time constraints we completed our analysis at a small spatial extent and defined a relative baseline landscape as that meeting Camp Lejeune’s recovery plan. The withdrawal landscape reflected loss of RCW habitat on-base. The mitigation landscape reflected habitat restoration on Encroachment Partnering parcels. Baseline landscape Withdrawal landscape Mitigation landscape Take 12 clusters starting 2007 Simulate recovery starting 2005 Add 9 Clusters Starting 2035 Landscape Service Years (LSYs) Median values from 8 SEPMs Debit LSY d Credit LSY c Credits remaining PBGs Hs – genetic diversity within groups Dst – genetic diversity among groups “Jeopardy standard” evaluated based on probability of extinction across Camp Lejeune, which was zero across all landscapes and dispersal models. “Take standard” evaluated by comparing Potential Breeding Groups (PBGs) over time in the withdrawal and mitigation landscape to the installation’s recovery goal (173 active groups). Trades evaluated with all 8 SEPMs remaining after POM Trading did not violate take standard. All 8 SEPMs predicted that Camp Lejeune’s recovery plan will exceed 173 PBGs despite the loss of habitat (i.e., debit values were negative because PBGs in withdrawal > PBGs at recovery). Further, all 8 SEPMs predicted that the 9 clusters off-base will be occupied generating positive credit values. Fragmentation effects were evaluated by comparing genetic diversity within breeding groups (Hs) and genetic divergence among breeding groups (Dst) in the baseline, withdrawal, and mitigation landscapes. A small debit in LSYs for Hs resulted from habitat loss, meaning that rates of inbreeding and genetic drift increased only a little relative to the baseline landscape. Restoration of habitat off-base resulted in a small, negative credit. This means that average rates of inbreeding and genetic drift increased in the mitigation landscape due to the restoration of peripheral habitat. A small debit in LSYs for Dst resulted from habitat loss, meaning that habitat loss increased genetic differences among clusters. Restoration of habitat off-base further increased genetic differences among clusters creating a negative credit. Therefore, these clusters do not contribute as much to migration as those lost on-base. In summary, habitat loss on base is not expected to appreciably decrease population viability. This project provided only a preliminary analyses because the spatial extent was small. However, the LEA results are in agreement with expectations from RCW behavior. Specifically, LEA indicated that peripheral clusters are not as productive nor do they contribute as much to migration as clusters found on-base. All 8 SEPMs were in agreement regarding the expected changes in population processes due to the trade. Therefore, we know enough to value trades at this spatial scale. Had models disagreed on the value of trades, it would indicate that empirical data on dispersal behaviors should be collected before the most cost-effective trade could be determined. Pattern-Oriented Modeling (Grimm et al 2005) Patterns observed in nature provide high information content (Grimm et al 1996) Contrasting the ability of different parameter values to reproduce observed patterns can be used to estimate parameter values indirectly Challenge: SEPMs often contain uncertain parameter values Challenge: Predict population patterns in dynamic landscapes Challenge: To achieve regional persistence, spatially subdivided populations require migration and variation in rates of recruitment over space How can we determine if habitat patches traded make equivalent contributions to rates of recruitment and migration? Camp Lejeune