Combining multiple sources of data to inform conservation of Lesser ...

Volume 135, 2018, pp. 228–239 DOI: 10.1642/AUK-17-113.1

RESEARCH ARTICLE

Combining multiple sources of data to inform conservation of Lesser Prairie-Chicken populations Beth E. Ross,1,2* David A. Haukos,3 Christian A. Hagen,4 and James Pitman5 1

U.S. Geological Survey, South Carolina Cooperative Fish and Wildlife Research Unit, Clemson, South Carolina, USA Kansas State University, Department of Biology, Manhattan, Kansas, USA 3 U.S. Geological Survey, Kansas Cooperative Fish and Wildlife Research Unit, Manhattan, Kansas, USA 4 Oregon State University, Bend, Oregon, USA 5 Western Association of Fish and Wildlife Agencies, Emporia, Kansas, USA * Corresponding author: [email protected] 2

Submitted June 26, 2017; Accepted November 7, 2017; Published February 7, 2018

ABSTRACT Conservation of small populations is often based on limited data from spatially and temporally restricted studies, resulting in management actions based on an incomplete assessment of the population drivers. If fluctuations in abundance are related to changes in weather, proper management is especially important, because extreme weather events could disproportionately affect population abundance. Conservation assessments, especially for vulnerable populations, are aided by a knowledge of how extreme events influence population status and trends. Although important for conservation efforts, data may be limited for small or vulnerable populations. Integrated population models maximize information from various sources of data to yield population estimates that fully incorporate uncertainty from multiple data sources while allowing for the explicit incorporation of environmental covariates of interest. Our goal was to assess the relative influence of population drivers for the Lesser Prairie-Chicken (Tympanuchus pallidicinctus) in the core of its range, western and southern Kansas, USA. We used data from roadside lek count surveys, nest monitoring surveys, and survival data from telemetry monitoring combined with climate (Palmer drought severity index) data in an integrated population model. Our results indicate that variability in population growth rate was most influenced by variability in juvenile survival. The Palmer drought severity index had no measurable direct effects on adult survival or mean number of offspring per female; however, there were declines in population growth rate following severe drought. Because declines in population growth rate occurred at a broad spatial scale, declines in response to drought were likely due to decreases in chick and juvenile survival rather than emigration outside of the study area. Overall, our model highlights the importance of accounting for environmental and demographic sources of variability, and provides a thorough method for simultaneously evaluating population demography in response to long-term climate effects. Keywords: climate change, drought, integrated population models, Lesser Prairie-Chicken, Tympanuchus pallidicinctus ´ de las poblaciones de ´ Combinando multiples fuentes de datos para planificar la conservacion Tympanuchus pallidicinctus RESUMEN ´ de pequenas ˜ poblaciones esta usualmente basada en datos limitados de estudios restringidos en La conservacion ´ incompleta de los t´erminos espaciales y temporales, lo que deriva en acciones de manejo basadas en una evaluacion ´ Si las fluctuaciones en abundancia esta´n relacionadas con el clima, un correcto manejo controladores de la poblacion. es particularmente importante, ya que los eventos clima´ticos extremos podr´ıan afectar desproporcionadamente la ´ especialmente para las poblaciones vulnerables, se ven abundancia poblacional. Las evaluaciones de conservacion, ´ apoyadas por el conocimiento de como los eventos extremos influencian el estatus y la tendencia poblacional. A pesar ´ e´ stos pueden ser escasos de la importancia que los datos puedan tener para apoyar los esfuerzos de conservacion, ´ de varias ˜ o vulnerables. Los modelos poblacionales integrados maximizan la informacion para poblaciones pequenas ´ fuentes de datos para generar estimaciones poblacionales que incorporan la incertidumbre de multiples fuentes de ´ explicita de covariables ambientales de interes. ´ Nuestro objetivo fue datos mientras que permiten la incorporacion ´ evaluar la influencia relativa de los controladores poblacionales para Tympanuchus pallidicinctus en el nucleo de su rango, el oeste y el sur de Kansas. Usamos datos de muestreos de ensambles de cortejo censados al costado de las rutas, monitoreo de nidos y datos de supervivencia obtenidos con telemetr´ıa combinados con datos clima´ticos (´Indice de Severidad de Sequ´ıa de Palmer) en un modelo poblacional integrado. Nuestros resultados indican que la variabilidad en la tasa de crecimiento poblacional estuvo mayormente influenciada por la variabilidad en la

Q 2018 American Ornithological Society. ISSN 0004-8038, electronic ISSN 1938-4254 Direct all requests to reproduce journal content to the AOS Publications Office at [email protected]

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

IPMs for Lesser Prairie-Chicken conservation

229

supervivencia de los juveniles. El ´Indice de Severidad de Sequ´ıa de Palmer no tuvo efectos directos medibles en la ´ supervivencia del adulto y en el numero medio de hijos por hembra; sin embargo, hubo disminuciones en la tasa de crecimiento poblacional luego de sequ´ıas severas. Debido a que las disminuciones en la tasa de crecimiento poblacional ocurrieron a una escala espacial amplia, las disminuciones en respuesta a la sequ´ıa se debieron ´ hacia probablemente a descensos en la supervivencia de los polluelos y de los juveniles, ma´s que a la emigracion afuera del a´rea de estudio. En general, nuestro modelo destaca la importancia de considerar las fuentes de variabilidad ´ ambiental y demogra´fica, y brinda un metodo robusto para evaluar simulta´neamente la demograf´ıa poblacional en respuesta a los efectos clima´ticos de largo plazo.

Palabras clave: cambio clima´tico, modelos poblacionales integrados, sequ´ıa, Tympanuchus pallidicinctus INTRODUCTION Conservation of small populations is often based on limited information due to monitoring challenges (Thompson 2004). However, species with small populations are often of high priority for conservation, and management decisions are still made despite limited data. Additionally, for populations of conservation concern, it is especially important to understand the role of demographic stochasticity in population fluctuations, given that a few stochastic events could disproportionately affect population abundance. Without understanding the role of environmental and population density effects (e.g., density dependence or Allee effects), researchers and managers could waste valuable time and resources applying ineffective management actions (Willi et al. 2006). Integrated population models (IPMs) are especially useful for understanding populations of conservation concern because they draw information from multiple data sources, leveraging information from limited datasets (Besbeas et al. 2002, Oppel et al. 2014). By combining longterm count data with detailed datasets of survival and reproduction, researchers and managers can be better informed about population demographics and growth rates (Besbeas et al. 2002, Schaub et al. 2012, Tempel et al. 2014). Modeling direct changes of life-history characteristics in this framework allows researchers and managers to explicitly understand how environmental changes affect populations while still making use of information from data related to abundance (Johnson et al. 2010, Chandler and Clark 2014, Oppel et al. 2014). IPMs not only have potential to provide information on the relative effects of environmental change on population parameters, but can also potentially provide information on future population growth rates based on forecasted changes in the environment. Understanding the effect of environmental change on populations is especially important for the flora and fauna in the Great Plains region of North America. Increased climate variability (IPCC 2013), increased drought (Cook et al. 2015), and conversion of grassland to cropland (Sohl et al. 2012) are forecast for the Great Plains, which might result in compounding negative effects on flora and fauna in the region. A species of conservation concern in the

Great Plains region that is already being affected by environmental change is the Lesser Prairie-Chicken (Tympanuchus pallidicinctus). Both climate (Grisham et al. 2013, 2016, Ross et al. 2016b) and land-use change (Fuhlendorf et al. 2002, Ross et al. 2016a) affect this species in certain parts of its range by interacting to reduce its resilience in areas with less grassland cover (Ross et al. 2016a). While overall resilience is affected by the interaction of drought and land use, it remains unknown through which stage of the life cycle these impacts are realized. Because Lesser Prairie-Chickens are relatively short lived, parameters related to reproduction and juvenile survival likely affect the species more than adult survival (Hagen et al. 2009). The Lesser Prairie-Chicken is a grassland obligate species, so decreases in grassland habitat will likely have negative effects on future population growth rates, especially when combined with increased climate variability (Grisham et al. 2013). Lesser Prairie-Chickens experience large population fluctuations (i.e. demographic stochasticity; Garton et al. 2016), but the biotic or abiotic effects that cause these fluctuations at the population level and the relative contribution of these fluctuations to overall changes in the long-term population growth rate are unknown. In addition to environmental drivers such as climate and land-cover change, the Lesser Prairie-Chicken may be experiencing effects of density dependence in certain portions of its range through decreased carrying capacity (Garton et al. 2016). The species has been expanding its range northward in western Kansas, USA, but the mechanism for this range expansion is unknown (OylerMcCance et al. 2016). Range expansion could be due to changes in climate or land cover in the region, particularly through expansion of Conservation Reserve Program lands (Sullins 2017), and potentially driven by dispersal caused by increases in population abundance in the local region near the range boundary (Earl et al. 2016). To address complex drivers of Lesser Prairie-Chicken population changes, we used an IPM with data from lek counts, nest surveys, and telemetry. The purpose of our study was to maximize the use of temporally sparse data to estimate population parameters of interest and quantify the effects of climate, particularly drought, on the population growth rate from 1997 to 2015. Additionally,

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

230 IPMs for Lesser Prairie-Chicken conservation

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

FIGURE 1. Study area in Kansas, USA, 1997–2015. Gray region outlines the current range of the Lesser Prairie-Chicken within Kansas (inset). Thick black lines indicate lek count transects.

we quantified the contribution of demographic stochasticity to changes in the population growth rate and determined what portion of the life cycle causes these natural fluctuations. METHODS Study Area The Lesser Prairie-Chicken in Kansas consists of 3 populations or ecoregions in northwestern (Short-Grass Prairie/Conservation Reserve Program Mosaic Ecoregion), southwestern (Sand Sagebrush Prairie Ecoregion), and south-central Kansas (Mixed-Grass Prairie Ecoregion; Figure 1; McDonald et al. 2014, Oyler-McCance et al. 2016). The 3 populations have limited gene flow, though some individuals from the Sand Sagebrush and MixedGrass Prairie ecoregions have dispersed into the ShortGrass Prairie Ecoregion (Oyler-McCance et al. 2016). Land cover in northwestern Kansas consists of row-crop agriculture, native grassland, and former cropland enrolled in the U.S. Department of Agriculture Conservation

Reserve Program. Southwestern Kansas is dominated by sand sagebrush (Artemisia filifolia) prairie and primarily consists of grassland intermixed with cropland. Research in south-central Kansas took place within the Red Hills region, which primarily consists of mixed-grass prairie with pockets of row-crop agriculture in bottomlands. Typical cropland in Kansas consists of winter wheat, grain sorghum, alfalfa, soybeans, and limited areas of corn (both irrigated and dryland; Spencer 2014, Robinson 2015). Data Sources We used lek count surveys for Lesser Prairie-Chickens in Kansas from 1997 to 2014 in an N-mixture model to account for changes in detection probability (Ross et al. 2016b). In 1997, surveys consisted of 10 transects in 10 counties and increased to 17 transects in 15 counties covering ~520 km2 by 2014. To conduct a survey, observers drove a 16-km transect and stopped each 1.6 km for 3 min auditory surveys to identify leks. Surveys generally started between 0500 and 0700 hours. After completing the driving route, the observer then returned

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

FIGURE 2. Directed acyclic graph of the integrated population model for Lesser Prairie-Chickens in Kansas, USA, 1997–2015. This diagram of population dynamics incorporates population models for reproduction (q) and survival (SASY) and state-space models for leks to estimate abundance (NTotal). The variance– covariance matrix is represented as R, D represents data on fates of marked females, M represents data on the number of nests, and J represents the total number of chicks produced. Reproduction and survival estimates are modeled with covariates for the Palmer drought severity index (PDSI).

to each lek, flushed the birds, and counted all Lesser Prairie-Chickens on the lek. Observers generally conducted surveys twice each season between March 20 and April 20. Lekking males are most likely to be detected with this survey methodology, because transient males and females are likely not a large proportion of the observed birds. Mixed-species leks and leks with hybrids between Lesser Prairie-Chickens and Greater Prairie-Chickens (T. cupido) may occur on 3 routes in northwestern Kansas (Bain and Farley 2002). Species are difficult to distinguish in flush counts of these mixed leks, and Greater Prairie-Chickens may have been included in counts of Lesser PrairieChickens. Our estimates of lek abundance were not intended to estimate actual population abundance of Lesser Prairie-Chickens, but rather to provide an estimate of relative change for estimating trends in population growth rates (Dahlgren et al. 2016). Female Lesser Prairie-Chickens were collared for known-fate survival estimation from 1997 to 2002 and from 2013 to 2015 (n ¼ 308). During the spring and fall lekking periods of 1997–2002, we collared females with a necklace-style VHF radio transmitter (12 g, model 7PN; Advanced Telemetry Systems, Isanti, Minnesota, USA) with either 6 mo (1997–1999) or 12 mo (2000– 2002) battery life (Hagen et al. 2007). We randomly assigned females captured during 2013–2015 with either a 22 g, rump-mounted, solar-powered, GPS Platform Transmitting Terminal (PTT-100; Microwave Telemetry, Columbia, Maryland, USA) or a 15 g bib-style VHF transmitter with a battery life of ~790 days (A3960; Advanced Telemetry Systems). We located birds with VHF collars by triangulation using a handheld Yagi antenna and receiver 3–5 times wk1 until they died, they left the primary study area, or transmitter batteries were depleted. A fixed-wing aircraft with mounted telemetry equipment located missing VHF birds. Locations were

IPMs for Lesser Prairie-Chicken conservation

231

recorded every 2 hr with the GPS PTT transmitters and uploaded through the Argos satellite system every 3 days until the collared individuals died or transmitter batteries failed to recharge. We identified nest locations in 1997–2002 and 2013– 2015 by homing after a marked female was in the same location for 3 consecutive days (n ¼ 427). To determine nest fate, we revisited the nest only when the female left the nest for consecutive days. Nest fate was classified as successful (1 egg hatched), unsuccessful, or abandoned (left unattended for 3 consecutive days) and the total number of chicks was recorded. We pooled renesting attempts with first nesting attempts if the nest failed. Our environmental covariate consisted of a climatic index based on the Palmer drought severity index (PDSI; NCDC 1994), a relative measure of drought that responds quickly to changes in weather conditions. We were interested in drought during summer (June–July), which is negatively related to Lesser Prairie-Chicken abundance the following spring, or the lag effect of PDSI (Ross et al. 2016b). We calculated the average PDSI during summer for each year during 1997–2015 (NCDC 1994). Model Description An IPM (Besbeas et al. 2002, Johnson et al. 2010) combined information from these multiple datasets to assess processes driving population dynamics of the Lesser Prairie-Chicken (Figure 2). We used state-space models to describe population changes on Lesser Prairie-Chicken leks (de Valpine and Hastings 2002). For each site i on the transect route, sampled on occasion j, we summed the estimate for each year from our previous N-mixture model (Ross et al. 2016b) to obtain an overall estimate of our index to population abundance within the range of Lesser Prairie-Chickens in Kansas (McCaffery and Lukacs 2016). Our state-space model accounted for changes in relative abundance through time (Nt) as well as survival (S), mean reproduction (number of offspring per female, F), and sampling variability. The state process for second-year (SY) and after-second-year (ASY) males is then 2 NSY;t ~Poisson4ðNSY;t1 þ NASY;t1 ÞSJUV;t1

3

Ft1 5 2

NASY;t ~BinomialðNSY;t1 þ NASY;t1 ; SASY;t1 Þ

ð1Þ

ð2Þ

where SJUV and SASY are the survival estimates of juveniles (the period from hatch until the lek surveys in the subsequent breeding season) and ASY females (the annual period from the juvenile stage onward). We assume equal proportions of males and females in offspring, thus

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

232 IPMs for Lesser Prairie-Chicken conservation

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

dividing F by 2. While SJUV is estimated in the IPM framework, it is informed using priors from previous studies (see ‘‘Model Implementation’’; Pitman et al. 2006). However, we are unable to distinguish what proportion of the parameter that we refer to as ‘‘juvenile survival’’ consists of juveniles, immigrants, and other unmeasured variables (e.g., breeding propensity, renesting probability; Robinson et al. 2014), though our approach is broad enough in spatial scale that immigrants are unlikely (Plumb 2015, Robinson 2015). The observation component of the state-space model is yt ~NormalðNSY;t þ NASY;t ; r2t Þ

ð3Þ

where r2t is error associated with the estimate from the N-mixture model (Ross et al. 2016b). Note that the observation error as specified is not the same as detection probability; however, if the detection probability is stationary, as it was with the N-mixture model estimates (Ross et al. 2016b), this should not affect inference related to population dynamics or the estimation of demographic parameters. The IPM then links changes in abundance on leks over time through changes in demographic rates. We assume that the lek counts, which take place over a broad spatial scale, are representative of survival and reproduction at a finer spatial scale related to the observational level of our survival and fecundity data. Additionally, lek counts are based on male abundance while vital rates are based on marked females, therefore assuming that male and female abundance are highly correlated, and that both sexes are affected similarly by environmental conditions linked to survival and reproduction. Combining the likelihood of the process (LP) and observation (LO) models yields the combined likelihood for the state-space model: Lss ðyjN; SJUV ; SASY ; F; r2t Þ ¼ LO ðyjN; r2t Þ 3 LP ðNjSJUV ; SASY ; FÞ: ð4Þ We used abundance estimates from the model to derive the population growth rate (k t) for each time step where kt ¼

NASY;t þ NSY;t NASY;t1 þ NSY;t1

ð5Þ

For survival estimation, we were interested in quantifying how habitat type and changes in climate affected the mean and variance of annual survival of female Lesser Prairie-Chickens. We monitored females for survival and reproduction estimation, rather than males. The model therefore assumes equal survival probability for males and females throughout the study period. The model also assumes that transmitters had no effects on adult survival

or recruitment, and that VHF and GPS PTT marked individuals had similar survival rates (Plumb 2015). Because survival for females is likely lower, especially during the nesting season, this assumption likely underestimates true survival for males (Hagen et al. 2009). We modeled the annual survival rate using the inverse link function as logitðSASY;t Þ ¼ b0;ASY þ b1 ðPDSIt Þ þ eSASY ;t

ð6Þ

where b0,ASY is the intercept parameter, b1 is the regression coefficient for the value of PDSI at time t, and eSASY ;t is the random effect for time coming from a multivariate distribution with zero mean and e ~ MVN(0,R), where R is the variance–covariance matrix for demographic parameters in the model (see below). To develop the encounter history for survival estimation, we used a conditional Bernoulli model (Converse et al. 2013, Godar 2016). We held survival constant across the year, with variation based on year-to-year changes. Because our observations were limited to monthly occasions, we estimated monthly survival for each individual k as wk,t ¼ (SASY,t)1/12. We defined the encounter history as Xk;t jðXk;t1 Þ~Bernoulliðwk;t Þ

ð7Þ

Xk;t jðXk;t1 ¼ 0Þ ¼ 0:

ð8Þ

and

The likelihood for the annual survival model is therefore Ls(Xk,t j SASY, b, R). Our model for reproduction estimated the annual number of chicks (Jt). Our chick counts were specified as Jt ~ Poisson(Mt 3 Ft), where Mt is the number of surveyed broods and Ft is the number of chicks per female. Because nest success likely varies with weather conditions, we modeled annual reproduction as logðFt Þ ¼ c0 þ c1 ðPDSIt Þ þ eF;t

ð9Þ

where c0 is an intercept, c1 is the regression coefficient for PDSI, and eF,t is a random effect for time where e ~ MVN(0,R). The likelihood for the reproduction model was specified as LrF(J j M,F,c,R). We modeled juvenile survival and other unmeasured variables as logitðSJUV;t Þ ¼ b0;JUV þ eSJUV ;t

ð10Þ

where b0,JUV is the intercept and eSJUV ;t are the temporal residuals for juvenile survival. Because we did not have

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

IPMs for Lesser Prairie-Chicken conservation

sufficient data to estimate juvenile survival, we instead estimate this as a latent variable informed by count data and other vital-rate estimates. We estimate missing parameters (e.g., adult survival, mean reproduction) in years of missing data (2003–2012) through lek count data and prediction using PDSI, similar to forecasting population change based on a covariate (e.g., Oppel et al. 2014) but with additional data. If we assume that all our datasets are independent, then combining all our models yields the joint likelihood LIPM ðy; Dk;t ; JjN; SJUV ; SASY ; b; F; r2t ; c; RÞ ¼ LO ðyjN; r2t Þ 3 LP ðNjSJUV ; SASY ; FÞ 3 Ls ðXk;t jSASY ; b; RÞ 3 Lrp ðJjM; F; c; RÞ: ð11Þ Because datasets for nest success were based on females monitored for survival, it is likely they are not completely independent; however, violating this assumption has only minimal consequences for population estimates (Abadi et al. 2010). Estimating Temporal Variability In addition to estimating annual variation in demographic parameters, we were also interested in estimating correlations among demographic parameters. We therefore modeled female adult survival, juvenile survival, and mean reproductive output with temporal residuals coming from a multivariate normal distribution, e ~ MVN(0,R), where R is the variance–covariance matrix for the 3 parameters (Schaub et al. 2013). The temporal correlation between 2 parameters A and B can then be calculated as qffiffiffiffiffiffiffiffiffiffiffiffi ð12Þ rA;B ¼ rAB = r2A r2B

The prior for R was R ~ Wishart(I, 4) where I is a 3 3 3 identity matrix. Wishart priors can exert a strong influence here, tending to overestimate the variance and underestimate correlations (Alvarez et al. 2014). We used the correlation between annual demographic rates and annual population growth rates to quantify the relative contribution in temporal variability from each demographic parameter to variability in population growth rate (Schaub et al. 2013, Weegman et al. 2016). We computed the correlation coefficients and the probability that the correlation was positive for each posterior sample. Model Implementation We used Markov chain Monte Carlo and a Gibbs sampler in JAGS (Plummer 2012) with the package ‘‘runjags’’ (Denwood 2016) in R (R Core Team 2013) to obtain posterior distributions for the parameters (see Supple-

233

mental Material for JAGS code). We ran 3 chains for 1,100,000 iterations with a burn-in of 100,000 and thinning every 100 samples, saving 30,000 samples (10,000 from each chain). Trace plots of each parameter and GelmanRubin convergence statistics (Gelman et al. 2013) were used to determine convergence of chains. We implemented models with the priors N1,ASY ~ Poisson(2265), N1,SY ~ Poisson(1116), logit(b0,JUV ) ~ Beta(1.14, 8.41) (mean ¼ 0.12, SD ¼ 0.1), logit(b0,ASY) ~ Beta(24, 58) (mean ¼ 0.30, SD ¼ 0.05), c0 ~ Normal(0.78, 10), and bPDSI ~ Normal(0, 10). Previous results were used to inform the priors of juvenile survival, adult survival, and fecundity (Pitman et al. 2006, Hagen et al. 2009). The same data that generated our priors were used for the initial period of the study (1997–2002), similar to an empirical Bayesian framework; however, parameters for survival and fecundity were still informed by the count data within the IPM framework for the 1997–2002 period, estimated using different statistical models than the priors (e.g., Poisson regression with an effect of PDSI), and further estimated from data independent of the priors for the rest of the study (2002–2014). We assumed a ratio of 1:3 juveniles:adults based on a stable age distribution and estimates from our N-mixture model to assign priors for N1,ASY and N1,SY (Hagen et al. 2009). Power Analysis Although IPMs are increasingly used, few studies have examined the power to detect relationships among parameters in the modeling framework (but see Abadi et al. 2010, 2012). In particular, it was unknown how missing data might influence our results and inference. Our concern was that with several years of missing data in the middle of the study, data would be sparse enough that we would lack the power to detect significant environmental effects or correlations among vital-rate parameters (i.e. committing a type II error). To ensure proper inference from our model, we simulated 500 datasets based on our known sample sizes and estimates of adult survival, juvenile survival, and fecundity from the IPM (see above). We first simulated changes in adult survival, juvenile survival, and fecundity through time using mean estimates (b0,ASY, b0,JUV, c0), 3 levels of coefficient estimates with covariates (6 different scenarios where b1 ¼ 0.5, 1.0, or 2.0 and c1 ¼ 0; or b1 ¼ 0 and c1 ¼ 0.5, 1.0, or 2.0) and with estimates of the variance–covariance matrix from the IPM. We then simulated lek count data using our estimate of total abundance as ct ~ Poisson(NTotal ,t). Finally, we removed adult survival and reproduction data from the middle years of the study. To evaluate the power of our model to detect environmental effects, we ran our 500 simulated datasets for the 6 different covariate scenarios through the IPM and estimated the proportion of simulations where the 95% credible intervals (CIs) included a

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

234 IPMs for Lesser Prairie-Chicken conservation

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

FIGURE 3. Violin plots of population growth rates (k; median shown in solid line) of Lesser Prairie-Chickens in Kansas, USA, 1998– 2014, and estimates of population growth rate from a previous N-mixture model (Ross et al. 2016b, black circles). The probability that k is .1, P(k . 1), is indicated above the violin plot and below the violin plot for P(k , 1).

significant (i.e. different than zero) effect of PDSI for adult survival and reproduction, given our model estimates of effect sizes. RESULTS From 1998 to 2014, population growth rate over the study area in Kansas fluctuated above and below a stable population growth rate of 1, with an increasing population in 1998, 2004, and 2008 (k . 1); a decreasing population in 2000, 2003, 2007, and 2012 (k , 1); and stable populations otherwise (95% CIs of k included 1; Figure 3). Overall, k  1 in 13 of 17 yr (76.5%). Through accounting for uncertainty in the IPM, we estimated 95% CIs for kt during periods when only lek count data were available (2003–2011), though these estimates were largely informed through the survey data. Estimates of Lesser Prairie-Chicken adult survival were highly variable, with mean annual survival of 0.35 (95% CI: 0.25–0.44; Figure 4). Survival of adults was lowest in 2000, 2003, 2004, and 2014, and while some of these were years with severe drought, the probability of a positive effect of PDSI on adult survival was P(b1 . 0) ¼ 0.84 (b1 ¼ 0.50; 95% CI: 0.61 to 1.50; Figure 5). The mean reproduction (F) of Lesser Prairie-Chickens was relatively stable from 1998 to 2014, with no significant increases or decreases (F ¼ 2.90; 95% CI: 1.89–4.62; Figure 4). While the 95% CI for c1 included 0 (c1 ¼0.28; 95% CI: 0.69

to 0.09), the probability of a negative relationship between PDSI and nest success was P(c1 , 0) ¼ 0.92 (Figure 5). Mean annual juvenile survival for 1998–2014 was 0.41 (95% CI: 0.23–0.58; Figure 4). Juvenile survival was not correlated with adult survival (r ¼ 0.44; 95% CI: 0.48 to 0.91), and adult survival was not correlated with reproduction (r ¼ 0.57; 95% CI: 0.91 to 0.14), but juvenile survival was negatively correlated with reproduction (r ¼ 0.73; 95% CI: 0.94 to 0.24), indicating lower juvenile survival when reproduction was high and vice versa. The correlation between population growth rate and adult survival was not significant, though the probability that they were positively correlated was relatively high (r ¼ 0.255, 95% CI: 0.22 to 0.77, P(r . 0) ¼ 0.83; Figure 6). Population growth rate and juvenile survival were positively correlated and had the largest probability of correlation (r ¼ 0.43; 95% CI: 0.05 to 0.76, P(r . 0) ¼ 0.96; Figure 6). Population growth rate and reproduction were not correlated (r ¼ 0.0.07, 95% CI: 0.52 to 0.55, P(r . 0) ¼ 0.62; Figure 6), indicating that adult and juvenile survival contributed more to variation in population growth rate than reproduction. Power Analysis Our model had large power (.0.80) when using effect sizes of b1 ¼ 2 or c1 ¼ 1 or 2 (Figure 7). When using the mean estimates of b1 ¼ 0.50 and c1 ¼ 0.50, our model had reduced power (power of b1 ¼ 0.302, power of c1 ¼ 0.516),

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

IPMs for Lesser Prairie-Chicken conservation

235

FIGURE 4. Estimates of adult female survival (A), juvenile survival (B), and mean reproduction (C) of Lesser Prairie-Chickens in Kansas, USA, 1998–2014, with 95% credible intervals (shaded polygons). The variance associated with the estimates increased during the portion of the study without vital-rate data.

indicating that for our specific IPM framework, significant values of small effect sizes may be difficult to distinguish from nonsignificant effects. Alternatively, effect sizes larger than ~0.9 for reproduction and larger than ~1.5 for adult survival had large power to detect an effect. DISCUSSION Using an IPM, we quantified changes in Lesser PrairieChicken population growth rates by combining multiple limited sources of data. Previous research has indicated that Lesser Prairie-Chicken abundance responds negatively to extreme drought conditions (low values of PDSI; Ross et al. 2016b); however, the stage of the life cycle through which this decrease in abundance occurred was still unknown. Combining multiple sources of data allowed us to determine that while PDSI is linked to changes in population abundance, the mechanism of these effects in Kansas is likely not through changes in reproduction or in adult survival directly, but rather through changes in juvenile survival. Though the population growth rate declined during some years of the study, it does not appear that large contributions from reproduction drove those declines.

Lesser Prairie-Chickens historically exhibited a boomand-bust cycle in their population dynamics, as illustrated by our estimates of population growth rate from 1998 to 2014. The species may exhibit demographic lability to fluctuations in the environment, such as changes in PDSI, if the benefits of these ‘‘booms’’ outweigh the ‘‘busts’’ (Koons et al. 2009, Dahlgren et al. 2016). Given frequent fluctuations during 1998–2014 and in earlier years (Ross et al. 2016a), it may be that Lesser Prairie-Chickens are able to respond positively (i.e. increase in population abundance) to years with wet and cool conditions (Ross et al. 2016b). However, given the increase in drought frequency and intensity forecast to affect the Great Plains region, the diminishing opportunity to respond to these conditions with above-average precipitation may lessen the intensity of boom-and-bust cycles of Lesser Prairie-Chicken populations in the future. The negative correlation between juvenile survival and reproduction suggests negative density-dependent mortality of juveniles. Population-level analyses based on count data have identified density dependence in Lesser PrairieChickens but have not identified the mechanism for this effect (Garton et al. 2016). In addition to the negative correlation between juvenile survival and reproduction,

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

236 IPMs for Lesser Prairie-Chicken conservation

FIGURE 5. Violin plots for regression coefficient estimates of the effect of the Palmer drought severity index (PDSI) on Lesser Prairie-Chicken female adult survival (left) and reproduction (right) in Kansas, USA, 1998–2014. The probability of the coefficient estimate being different from zero (e.g., P(b1 . 0) ¼ 0.84) is indicated above and below the violin plot.

variation in adult and juvenile survival was correlated with population growth rate. The importance of chick and juvenile survival to contributions in population growth rate in relation to fecundity (i.e. higher elasticities) has previously been shown for Lesser Prairie-Chickens in Kansas (Hagen et al. 2009, Sullins 2017). While there is typically more variation found in traits with smaller elasticities (Sæther and Bakke 2000), this was not the case in our study. Rather, the variability in juvenile survival is

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

FIGURE 7. Power to detect an effect size different than zero for adult survival (circles) or reproduction (triangles) with 3 different levels of effect size: 0.5, 1.0, or 2.0. Sufficient power is shown with a horizontal line at power ¼ 0.80.

another line of evidence supporting the demographic lability of Lesser Prairie-Chickens, similar to other species of grouse (Dahlgren et al. 2016). Extreme drought during summer (PDSI , 3) and Lesser Prairie-Chicken abundance the following spring are related (Ross et al. 2016a, 2016b), and our estimates of

FIGURE 6. Correlation between Lesser Prairie-Chicken population growth rate (k) in Kansas, USA, 1998–2014, and (A) adult survival (SASY), (B) juvenile survival (SJUV), and (C) reproduction (q) from the integrated population model with the Palmer drought severity index. The posterior mean correlation coefficients and 95% credible intervals are included along with the P(r . 0).

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

population growth rate decreased after years with extreme droughts (2002, 2006, 2012, and 2013), supporting those previous results. Because adult survival and reproduction were not strongly correlated with summer PDSI, thus not driving this relationship with abundance and population growth rate in most years, it is likely that juvenile survival is the main life-history trait affected by annual variation in summer PDSI, except during extreme drought, when other population demographic rates may be more greatly affected. When we modeled extreme drought during summer as a binary covariate (extreme drought event or not) instead of summer PDSI as a continuous covariate in the IPM, we did not see any significant response between PDSI and adult survival or between PDSI and reproduction (data not shown). However, mean adult survival did decrease substantially in years of extreme drought (e.g., a decrease of 18%, from 49% in 2011 to 31% in 2012), indicating that during major population declines, adult survival is also likely affected. Because adult and juvenile survival are correlated with population growth rate, these declines likely have a disproportionate effect on the population, and additional years of survival monitoring might have allowed us to better assess this relationship. Moreover, if survival of adult males is more negatively affected by extreme drought than female survival, the relationship between adult survival and PDSI may have been underestimated; however, we do not have data to suggest a stronger negative effect of PDSI on males than on females. Juvenile survival is negatively affected by environmental conditions (Fields et al. 2006, Grisham et al. 2016), and juvenile survival (Pitman et al. 2006, Hagen et al. 2009) and fecundity (Sullins 2017) are important components of the Lesser Prairie-Chicken life cycle. Lesser Prairie-Chickens may be resilient enough to maintain a stable population growth rate during years with mild or moderate drought, but populations decline during extreme drought conditions (Ross et al. 2016b). Juvenile survival likely drives year-to-year variation in population growth rate; however, during these extreme droughts, other aspects of Lesser Prairie-Chicken demography are affected (e.g., adult survival and nest initiation; Grisham et al. 2014). If the frequency and intensity of droughts increase as forecast, juvenile survival could be negatively affected in years with extreme drought and there may be insufficient time between extreme drought events for the population to recover in years with favorable conditions (wet, cool springs; Ross et al. 2016b). Because our model did not explicitly separate first-year survival and emigration, low PDSI during the summer may have effects on emigration from leks or lek complexes as well. Low-quality habitat may cause young-of-the-year to leave the study area to seek better conditions. Little is known about the drivers of emigration and dispersal of

IPMs for Lesser Prairie-Chicken conservation

237

Lesser Prairie-Chickens, and future research is needed to specifically quantify whether either of these 2 populationlevel processes cause changes in abundance and population growth rates of Lesser Prairie-Chickens at a broad spatiotemporal scale. Lesser Prairie-Chickens have shown complex dispersal movements (Earl et al. 2016), particularly in the northern portion of their range, which could be influenced by habitat quality, habitat availability, and climate. In addition to providing insights into the ecology of Lesser Prairie-Chickens, our results illustrate the utility of combining long-term count data with intensive field studies to better estimate population changes and parameters. For example, we lacked data on adult survival and reproduction for 2004–2013, yet we were able to gain insights into population dynamics during this time from the long-term count data. Our power analysis indicated that while smaller effect sizes may be difficult to detect for adult survival and reproduction, large effect sizes should be detected, even with limited data. The implementation and maintenance of long-term count studies provide an important baseline for an IPM, and count data frequently have the largest influence on estimates of population growth rate in these models (Abadi et al. 2010). On the other hand, as our analysis illustrates, the additional information from intensive field studies complements existing count data by providing insights into which population vital rates are driving changes in abundance. Available funding for field studies is often limited, and quick population surveys can be a more efficient way of obtaining information related to basic population assessment (e.g., Chandler and Clark 2014). Our example of combining long-term count data with intensive vital-rate sampling could be applied to other long-term datasets (e.g., Breeding Bird Survey data, state waterfowl or game bird surveys) to provide a more complete understanding of population drivers of a given species. If a baseline of variation in population vital rates is available, our analysis also provides a framework for conducting a power analysis to determine what levels of effect sizes could be detected given different sample sizes, helping provide insight into allocation of resources. Our IPM provides a broader understanding of Lesser Prairie-Chicken population dynamics in relation to PDSI and supports management actions that are focused on improving adult, chick, and juvenile survival during drought conditions. Because the resilience of the species to withstand extreme drought conditions likely decreases as the amount of cropland in a region increases (Ross et al. 2016a), habitat improvements that focus on increasing and maintaining grassland in a region may increase adult, chick, and juvenile survival and buffer the population against the harmful effects of severe drought. However, because mean adult survival decreased in years with

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

238 IPMs for Lesser Prairie-Chicken conservation

extreme drought, habitat improvements that help mediate the effects of drought on nesting and foraging habitat would also likely improve the population growth rate. Additionally, as the species’ range expands northward, focusing on habitat improvement for juveniles and dispersers in the northern extent of the range could be critical for preserving the species during predicted climate change. ACKNOWLEDGMENTS The manuscript was improved by comments from C. Morrison and M. Patten. We thank J. Kramer, M. Mitchener, D. Dahlgren, J. Prendergast, and S. Hyberg for their assistance with the project. We thank the biologists from the Kansas Department of Wildlife, Parks, and Tourism for collecting the lek survey data. Clemson University is acknowledged for generous allotment of compute time on Palmetto cluster. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Funding statement: Funding for the project was provided by Kansas Wildlife, Parks, and Tourism (Federal Assistance Grant KS W-73-R-3); USDA Farm Services CRP Monitoring, Assessment, and Evaluation (12-IA-MRE CRP TA#7, KSCFWRU RWO 62); and USDA Natural Resources Conservation Service through the Lesser Prairie-Chicken Initiative. Ethics statement: All capture and handling procedures were approved by the Kansas State University Institutional Animal Care and Use Committee protocol (3241) and Kansas Department of Wildlife, Parks, and Tourism scientific wildlife permits (SC-042-2013 and SC-079-2014). Author contributions: B.E.R., D.A.H., C.A.H., and J.P. conceived the study, designed the methods, and conducted the research. B.E.R. analyzed the data. B.E.R. and D.A.H. wrote the paper. D.A.H. and C.A.H. contributed substantial resources.

LITERATURE CITED Abadi, F., O. Gimenez, R. Arlettaz, and M. Schaub (2010). An assessment of integrated population models: Bias, accuracy, and violation of the assumption of independence. Ecology 91:7–14. Abadi, F., O. Gimenez, H. Jakober, W. Stauber, R. Arlettaz, and M. Schaub (2012). Estimating the strength of density dependence in the presence of observation errors using integrated population models. Ecological Modelling 242:1–9. Alvarez, I., J. Niemi, and M. Simpson (2014). Bayesian inference for a covariance matrix. arXiv:1408.4050. https://arxiv.org/ pdf/1408.4050.pdf Bain, M. R., and G. H. Farley (2002). Display by apparent hybrid prairie-chickens in a zone of geographic overlap. The Condor 104:683–687. Besbeas, P., S. N. Freeman, B. J. T. Morgan, and E. A. Catchpole (2002). Integrating mark–recapture–recovery and census data

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

to estimate animal abundance and demographic parameters. Biometrics 58:540–547. Chandler, R. B., and J. D. Clark (2014). Spatially explicit integrated population models. Methods in Ecology and Evolution 5: 1351–1360. Converse, S. J., J. A. Royle, P. H. Adler, R. P. Urbanek, and J. A. Barzen (2013). A hierarchical nest survival model integrating incomplete temporally varying covariates. Ecology and Evolution 3:4439–4447. Cook, B. I., T. R. Ault, and J. E. Smerdon (2015). Unprecedented 21st century drought risk in the American Southwest and Central Plains. Science Advances 1:e1400082. Dahlgren, D. K., M. R. Guttery, T. A. Messmer, D. Caudill, R. D. Elmore, R. Chi, and D. N. Koons (2016). Evaluating vital rate contributions to Greater Sage-Grouse population dynamics to inform conservation. Ecosphere 7:e01249. Denwood, M. J. (2016). Runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of Statistical Software 71:1–25. de Valpine, P., and A. Hastings (2002). Fitting population models incorporating process noise and observation error. Ecological Monographs 72:57–76. Earl, J. E., S. D. Fuhlendorf, D. Haukos, A. M. Tanner, D. Elmore, and S. A. Carleton (2016). Characteristics of Lesser PrairieChicken (Tympanuchus pallidicinctus) long-distance movements across their distribution. Ecosphere 7:e01441. Fields, T. L., G. C. White, W. C. Gilgert, and R. D. Rodgers (2006). Nest and brood survival of Lesser Prairie-Chickens in west central Kansas. The Journal of Wildlife Management 70:931– 938. Fuhlendorf, S. D., A. W. Woodward, D. M. Leslie, and J. S. Shackford (2002). Multi-scale effects of habitat loss and fragmentation on Lesser Prairie-Chicken populations of the US southern Great Plains. Landscape Ecology 17:617–628. Garton, E. O., C. A. Hagen, G. M. Beauprez, S. C. Kyle, J. C. Pitman, D. S. Schoeling, and W. E. Van Pelt (2016). Population dynamics of the Lesser Prairie-Chicken. In Ecology and Management of Lesser Prairie-Chickens (D. A. Haukos and C. W. Boal, Editors). CRC Press, Boca Raton, FL, USA. pp. 49– 76. Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin (2013). Bayesian Data Analysis, third edition. CRC Press, Boca Raton, FL, USA. Godar, A. J. (2016). Influence of climate change and land use on Lesser Prairie-Chicken (Tympanuchus pallidicinctus) population persistence in the sand sagebrush and short-grass prairies. M.S. thesis, Texas Tech University, Lubbock, TX, USA. Grisham, B. A., C. W. Boal, D. A. Haukos, D. M. Davis, K. K. Boydston, C. Dixon, and W. R. Heck (2013). The predicted influence of climate change on Lesser Prairie-Chicken reproductive parameters. PLOS One 8:e68225. Grisham, B. A., P. K. Borsdorf, C. W. Boal, and K. K. Boydston (2014). Nesting ecology and nest survival of Lesser PrairieChickens on the Southern High Plains of Texas. Journal of Wildlife Management 78:857–866. Grisham, B. A., A. J. Godar, and C. P. Griffin (2016). Climate change. In Ecology and Management of Lesser PrairieChickens (D. A. Haukos and C. W. Boal, Editors). CRC Press, Boca Raton, FL, USA. pp. 221–242.

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society

B. E. Ross, D. A. Haukos, C. A. Hagen, and J. Pitman

Hagen, C. A., J. C. Pitman, B. K. Sandercock, R. J. Robel, and R. D. Applegate (2007). Age-specific survival and probable causes of mortality in female Lesser Prairie-Chickens. Journal of Wildlife Management 71:518–525. Hagen, C. A., B. K. Sandercock, J. C. Pitman, R. J. Robel, and R. D. Applegate (2009). Spatial variation in Lesser Prairie-Chicken demography: A sensitivity analysis of population dynamics and management alternatives. The Journal of Wildlife Management 73:1325–1332. IPCC (2013). Climate Change 2013: The physical science basis. In Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley, Editors). Cambridge University Press, Cambridge, UK. Johnson, H. E., L. S. Mills, J. D. Wehausen, and T. R. Stephenson (2010). Combining ground count, telemetry, and mark– resight data to infer population dynamics in an endangered species. Journal of Applied Ecology 47:1083–1093. Koons, D. N., S. Pavard, A. Baudisch, and C. J. E. Metcalf (2009). Is life-history buffering or lability adaptive in stochastic environments? Ecology 118:972–980. McCaffery, R., and P. M. Lukacs (2016). A generalized integrated population model to estimate Greater Sage-Grouse population dynamics. Ecosphere 7:e01585. McDonald, L., G. Beauprez, G. Gardner, J. Griswold, C. Hagen, F. Hornsby, D. Klute, S. Kyle, J. Pitman, T. Rintz, D. Schoeling, and B. Van Pelt (2014). Range-wide population size of the Lesser Prairie-Chicken: 2012 and 2013. Wildlife Society Bulletin 38:536–546. NCDC (1994). Time bias corrected divisional temperatureprecipitation-drought index. Documentation for dataset TD9640. Available from DBMB, NCDC, NOAA, Federal Building, 37 Battery Park Ave., Asheville, NC 28801-2733. https:// www7.ncdc.noaa.gov/CDO/divisionaldesc.html Oppel, S., G. Hilton, N. Ratcliffe, C. Fenton, J. Daley, G. Gray, J. Vickery, and D. Gibbons (2014). Assessing population viability while accounting for demographic and environmental uncertainty. Ecology 95:1809–1818. Oyler-McCance, S. J., R. W. DeYoung, J. A. Fike, C. A. Hagen, J. A. Johnson, L. C. Larsson, and M. A. Patten (2016). Rangewide genetic analysis of Lesser Prairie-Chicken reveals population structure, range expansion, and possible introgression. Conservation Genetics 17:643–660. Pitman, J. C., C. A. Hagen, B. E. Jamison, R. J. Robel, T. M. Loughin, and R. D. Applegate (2006). Survival of juvenile Lesser PrairieChickens in Kansas. Wildlife Society Bulletin 34:675–681. Plumb, R. T. (2015). Lesser Prairie-Chicken movement, space use, survival, and response to anthropogenic structures in Kansas and Colorado. M.S. thesis, Kansas State University, Manhattan, KS, USA. Plummer, M. (2012). JAGS version 3.3.0 User Manual. https:// cran.r-project.org/web/packages/rjags/

IPMs for Lesser Prairie-Chicken conservation

239

R Core Team (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Robinson, R. A., C. A. Morrison, and S. R. Baillie (2014). Integrating demographic data: Towards a framework for monitoring wildlife populations at large spatial scales. Methods in Ecology and Evolution 5:1361–1372. Robinson, S. (2015). Landscape ecology, survival, and space use of Lesser Prairie-Chickens. M.S. thesis, Kansas State University, Manhattan, KS, USA. Ross, B. E., D. A. Haukos, C. A. Hagen, and J. C. Pitman (2016a). Landscape composition creates a threshold influencing Lesser Prairie-Chicken population resilience to extreme drought. Global Ecology and Conservation 6:179–188. Ross, B. E., D. Haukos, C. Hagen, and J. Pitman (2016b). The relative contribution of climate to changes in Lesser PrairieChicken abundance. Ecosphere 7:e01323. Sæther, B.-E., and Ø. Bakke (2000). Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81:642–653. Schaub, M., H. Jakober, and W. Stauber (2013). Strong contribution of immigration to local population regulation: Evidence from a migratory passerine. Ecology 94:1828–1838. Schaub, M., T. S. Reichlin, F. Abadi, M. K´ery, L. Jenni, and R. Arlettaz (2012). The demographic drivers of local population dynamics in two rare migratory birds. Oecologia 168:97–108. Sohl, T. L., B. M. Sleeter, K. L. Sayler, M. A. Bouchard, R. R. Reker, S. L. Bennett, R. R. Sleeter, R. L. Kanengieter, and Z. Zhu (2012). Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States. Agriculture, Ecosystems and Environment 153:1–15. Spencer, D. A. (2014). A historical record of land cover change of the Lesser Prairie-Chicken Range in Kansas. M.S. thesis, Kansas State University, Manhattan, KS, USA. Sullins, D. S. (2017). Regional variation in demography, distribution, foraging, and strategic conservation of Lesser Prairie-Chickens in Kansas and Colorado. Ph.D. dissertation, Kansas State University, Manhattan, KS, USA. Tempel, D. J., M. Z. Peery, and R. J. Gutie´ rrez (2014). Using integrated population models to improve conservation monitoring: California Spotted Owls as a case study. Ecological Modelling 289:86–95. Thompson, W. L. (Editor) (2004). Sampling Rare or Elusive Species: Concepts, Designs, and Techniques for Estimating Population Parameters. Island Press, Washington, DC, USA. Weegman, M. D., S. Bearhop, A. D. Fox, G. M. Hilton, A. J. Walsh, J. L. McDonald, D. J. Hodgson (2016). Integrated population modelling reveals a perceived source to be a cryptic sink. Journal of Animal Ecology 85:467–475. Willi, Y., J. Van Buskirk, and A. A. Hoffmann (2006). Limits to the adaptive potential of small populations. Annual Review of Ecology, Evolution, and Systematics 37:433–458.

The Auk: Ornithological Advances 135:228–239, Q 2018 American Ornithological Society