Combining Demographic and Count-Based Approaches to Identify Source–sink Dynamics of a Threatened Seabird

Abstract

Identifying source-sink dynamics is of fundamental importance for conservation but is often limited by an inability to determine how immigration and emigration influence population processes. We demonstrate two ways to assess the role of immigration on population processes without directly observing individuals dispersing from one population to another and apply these methods to a population of Marbled Murrelets (Brachyramphus marmoratus) in California (USA). In the first method, the rate of immigration (i) is estimated by subtracting local recruitment (recruitment from within the population due to reproduction) estimated with demographic data from total recruitment (f; recruitment from within the population plus recruitment from other populations) estimated using temporal symmetry mark-recapture models developed by R. Pradel. The second method compares population growth rates estimated with temporal symmetry models (lambdaTS) and/or population growth rates estimated from counts of individuals over multiple sampling periods (lambdaC) with growth estimates from a stage-structured projection matrix model (lambdaM). Both lambdaTS and lambdaC incorporate all demographic processes affecting population change (birth, death, immigration, and emigration), whereas matrix models are usually constructed without incorporating immigration. Thus, if lambdaTS and lambdaC are > or = 1 and lambdaM < 1, the population is sustained by immigration and is considered to be a sink. Using the first method, recruitment estimated with temporal symmetry models was high (f= 0.182, SE = 0.058), the mean adult birth rate, as estimated using the ratio of juveniles to > or = 1 year old individuals (observed during ship-based surveys) was low (bA = 0.039, SE = 0.014), and immigration was 0.160 (SE = 0.057). Using the second method, murrelet numbers in central California were stable (lambdaC = 1.058, SE = 0.047; lambdaTS = 1.064, SE = 0.033), but were projected to decline 9.5% annually in the absence of immigration (lambdaM = 0.905, SE = 0.053). Our results suggest that Marbled Murrelets in central California represent a sink population that is stable but would decline in the absence of immigration from larger populations to the north. However, the extent to which modeled immigration is due to permanent recruitment or temporarily dispersing individuals that simply mask population declines is uncertain.

Climate change‐induced decline in bamboo habitats and species diversity: implications for giant panda conservation

“…However, recent studies emphasized that some important environmental factors and species characteristics may be critical to improve the predictive power of SDMs (Boulangeat et al, 2012;Grassein et al, 2014). New advances in this field have started to develop species modelling approaches by incorporating more factors and parameters, such as metapopulation demography and landscape interactions (Keith et al, 2008;Vos et al, 2008), species life history traits (Peery et al, 2006), biotic interactions (Preston et al, 2008), species dispersal ability (Duckett et al, 2013), species evolution and adaptation (Byrne 2008) and human activities and disturbances (Midgley et al, 2003;Thuiller et al, 2004). For example, inclusion of biotic interactions with other species in SDMs is expected for long-term prediction of climate change-induced species range shifts, particularly for species strongly dependent on biotic interactions (Brooke et al, 2012).…”

Genetic analyses of historic and modern marbled murrelets suggest decoupling of migration and gene flow after habitat fragmentation

“…Murrelet population size averaged 572 individuals from 1999 to 2003 based on at sea surveys, and experienced a non-significant increase during this period using simple linear regression (F 1,3 ¼ 2.51, p ¼ 0.087, r 2 ¼ 0.68; figure 3; Peery et al 2006). To determine if increasing dispersal compromised our ability to detect a population decline from 1999 to 2003, we conducted Monte Carlo simulation-based power analyses with and without a 1.4 per cent annual increase in the proportion of migrants in the population (see appendix S2 in the electronic supplementary material).…”

“…(a) Sampling and laboratory methods For modern analyses, we captured and sampled blood from 601 marbled murrelets at sea in five locations from southeast Alaska to central California in waters adjacent to known concentrations of old-growth nesting habitat from 1 April to 15 October 1997-2007 following methods described in Peery et al (2006) (figure 1). For historic analyses, we sampled a small amount of tissue from the toepads of 192 murrelets that were collected at sea from central California to southeast Alaska and held in North American museum collections.…”

“…Temporal-N e estimators assume nonoverlapping generations, an assumption that was violated for murrelets. The direction of the bias depends on the species' life history and the sampling design, but biases are small when samples are separated by five or more generations (Waples & Yokota 2007), and approximately 12 generations separated the average sampling date in the modern and historic sampling periods (see the electronic supplementary material, table S3) assuming a generation time of eight years (generation time was estimated using equation 2.6 in Ebert (1999) and demographic parameters in Peery et al (2006)). Temporal-N e provides an estimate of the harmonic-mean N e between sampling periods (Wang 2005), and is therefore expected to produce an estimate more closely reflecting the population size in the second sampling period for declining species.…”

Melding kin structure and demography to elucidate source and sink habitats in fragmented landscapes

“…In addition, we computed an overall k for each identified cluster (see results) and estimated the standard error surrounding each clusterspecific estimate of k using the delta method (MATLAB 7.9; 43). To determine whether k differed significantly from one for each cluster, we used a two-tailed Z test following Franklin et al (1996) and Peery et al (2006).…”