DOI: 10.5061/dryad.j3tx95xjg
title: Assessing the value of monitoring to biological inference and expected management performance for a European goose population

description: 1. Informed conservation and management of wildlife require sufficient
 monitoring to understand population dynamics and to direct conservation
 actions. Because resources available for monitoring are limited,
 conservation practitioners must strive to make monitoring as
 cost-effective as possible. 2. Our focus was on assessing the value of
 monitoring to the adaptive harvest management (AHM) program for
 pink-footed geese (Anser brachyrhynchus). We conducted a retrospective
 analysis to assess the costs and benefits of a capture-mark-resight (CMR)
 program, a productivity survey, and biannual population censuses. Using
 all available data, we fit an integrated population model (IPM) and
 assumed that inference derived from it represented the benchmark against
 which reduced monitoring was to be judged. We then fit IPMs to reduced
 sets of monitoring data and compared their estimates of demographic
 parameters and expected management performance against the benchmark IPM.
 3. Costs and the precision and accuracy of key demographic parameters
 decreased with the elimination of monitoring data. Eliminating the CMR
 program, while maintaining other monitoring instruments, resulted in the
 greatest cost savings, usually with small effects on inferential
 reliability. Productivity surveys were also expensive and some reduction
 in survey effort may be warranted. The biannual censuses were inexpensive
 and generally increased inferential reliability. 4. The expected
 performance of AHM strategies was surprisingly robust to a loss of
 monitoring data. We attribute this result to explicit consideration of
 parametric uncertainty in harvest-strategy optimization and the fact that
 a broad range of population sizes is acceptable to stakeholders. 5.
 Synthesis and applications: Our study suggests that existing or potential
 monitoring instruments for wildlife populations should be scrutinized as
 to their cost-effectiveness for improving biological inference and
 management performance. Using Svalbard pink-footed geese as a case study,
 we show that the loss of some existing monitoring instruments may not be
 as adverse as commonly assumed if data are jointly analyzed in an
 integrated population model. Finally, regardless of the monitoring data
 available, we suggest that conservation strategies that explicitly account
 for uncertainty in demography are more likely to be successful than those
 that do not.
description: Population counts conducted in spring and autumn –
 Internationally coordinated population counts of pink-footed geese have
 been performed annually since 1990 in Denmark, Belgium, and the
 Netherlands in late October or early November (hereafter referred to as
 the November count) (Madsen et al., 1999). Over time, the population has
 expanded its distribution and the spatial coverage of the count has
 repeatedly been extended to capture new sites occupied by geese (Madsen,
 Christensen, Balsby, & Tombre, 2015). Since 2005, the population
 has also been counted in Norway, and since 2016 in Sweden. Because of
 increasing challenges in monitoring the autumn population, an additional
 count was introduced in May in 2010, which includes Norway, Denmark,
 Sweden and, since 2016, Finland. The known sites are covered by a network
 of trained observers who coordinate the coverage. The May census costs
 €5,327 per year for academic staff salary and travel costs of volunteers.
 The cost of the November census is similar, costing €3,350 per
 year. Harvest estimates – Pink-footed geese are subject
 to an open hunting season in Norway, including Svalbard, and in Denmark.
 The species is protected in the Netherlands, Belgium, Sweden, and Finland.
 In both Norway and Denmark, reporting the harvest is mandatory and hunters
 report their harvests online. Harvest monitoring imposes no additional
 costs on the pink-footed goose management community.
 Temporal distribution of harvest in Denmark – The November count
 occurs after the start of hunting seasons in Norway and Denmark and
 therefore it is important to be able to partition harvest into that
 occurring before and after the count. We assumed all the harvest in Norway
 occurs prior to the November count. In most years, the harvest in Norway
 occurs prior to the November count (Gundersen, Clausen, & Madsen,
 2017; Jensen, Madsen, & Tombre, 2016). Moreover, the average
 number of pink-footed geese in Norway during the November count is very
 low, consisting of less than five percent of the total. The temporal
 distribution of the pink-footed goose harvest in Denmark is derived from
 wings submitted to the Danish wing survey. Danish hunters voluntarily
 submit wings from harvested individuals, providing information on date,
 location, and age of harvested geese. Monitoring the temporal distribution
 of the harvest in Denmark imposes no additional costs on the pink-footed
 goose management community. Proportion of young in the
 autumn – Based on age-specific plumage characteristics, random counts of
 the number of young of the year and of older geese in flocks have been
 conducted by trained observers in the Netherlands and Denmark since 1980
 (Madsen, 1982; Madsen et al., 1999), and in more recent years in Belgium,
 Norway, and Sweden as migratory behavior has changed. To minimize the
 effect of seasonal changes in age ratios, we used only counts occurring
 between October 12 and November 4, inclusive. Fall counts of young and
 adults are relatively expensive, and at the current level of effort cost
 €21,897 per year for academic staff salary, contract observers, and travel
 costs. Capture-mark-recapture program – Estimates of
 annual survival rate (i.e., reflecting all sources of mortality) are
 available from a capture-mark-recapture (CMR) program (Madsen et al.
 2002). During the period 1991 – 2020, 4,984 pink-footed geese were
 captured and fitted with neck collars and tarsus metal rings during spring
 staging in Denmark and Norway and, in four years (2007, 2008, 2012, and
 2018), on the Svalbard breeding grounds. At marking, all birds had
 survived at least one fall migration and one hunting season. Re-sightings
 were made by a network of professional and volunteer observers outside the
 breeding grounds (September-May). Dead recoveries are reported by members
 of the public to the ringing centers involved (Denmark and Norway). They
 include both geese reported shot by hunters and those found dead by other
 members of the public. Using program MARK, we fit a number of Burnham’s
 joint recovery-recapture models (Burnham 1993) by including all possible
 combinations of fixed and fully time-dependent survival (S), resighting
 probability (p), recovery probability (r), and fidelity (F). The fidelity
 parameter expresses the proportion of geese surviving from year to year
 that are available for resighting and might, therefore, (among other
 things) reflect marker retention and emigration to poorly covered sites or
 other populations. Models were evaluated using AICc (Burnham &
 Anderson, 2003) and we chose for our use model S(t) p(t) r(t) F(.),
 indicating a fixed F-parameter and full temporal variability in survival,
 detection and recovery probability. Estimates of annual survival and their
 standard errors (inflated using a variance inflation factor) from this
 best-performing model were used to specify prior beta distributions for
 the IPM using the method of moments (Bolker, 2008). We omitted the last
 survival estimate in the time series due to identifiability
 issues. It should be noted that using survival
 estimates to generate informative prior distributions is not necessarily
 equivalent to using the encounter data directly in the IPM joint
 likelihood. Although we investigated the possibility of including the
 encounter histories as data in the joint likelihood, we eventually
 abandoned this approach for several reasons. First and foremost, the CMR
 data are needed to estimate both annual survival and May population size.
 To do this simultaneously from the raw data within the IPM requires use of
 the Jolly-Seber model (Jolly, 1965; Seber, 1965). A major obstacle with
 use of this model, however, is a key assumption that the “capture” rate of
 marked and unmarked birds is the same. This assumption is certainly
 violated when the initial capture is a physical capture and subsequent
 “captures” are sightings from a distance. Failure of this assumption
 biases the estimates of population size (although not of survival)
 (Nichols, Hines, & Pollock, 1984). We also considered embedding
 the Cormack-Jolly-Seber model (Cormack, 1964; Jolly, 1965; Seber, 1965) to
 estimate survival from live encounters, but mortality and permanent
 migration are confounded and, thus, survival estimates can be biased low
 (especially if re-sightings occur primarily in only a portion of the
 animal’s range). Finally, we considered the use of Burnham’s joint model
 (Burnham, 1993) for live and dead encounters. There were practical
 problems with this model related to excessive computing time (especially
 when many data-reduction scenarios had to be run) and to the necessity of
 specifying a single set of constraints for the structure of the encounter
 data in the IPM. The latter contrasts with the independent analysis in
 MARK, in which a large number of constraints could be tested to provide
 the most parsimonious model. Using informative priors based on previous
 studies is a widely accepted approach in Bayesian analysis (Hobbs
 & Hooten, 2015; King, Morgan, Gimenez, & Brooks, 2009).
 McCaffery & Lukacs (2016) provide an example involving the use of
 informative priors for survival in birds. Nonetheless, the ideal approach
 would be to use the encounter histories as data in the IPM joint
 likelihood when possible and practical (Kéry & Schaub,
 2012). The CMR data are also used to derive an estimate
 of pink-footed goose population size (N) by dividing the number of marked
 geese in the population (M) by the ratio of the number of marked geese to
 the number of geese observed (R) (Sheaffer & Jarvis, 1995): N =
 M/R . This approach has been used in the monitoring program since 1991
 (Ganter & Madsen, 2001) and resighting effort has increased
 significantly since 2011. As described by Clausen et al. (2019), the CMR
 data are used to derive estimates of pink-footed goose population size in
 spring, which are independent of the May counts. M is estimated from the
 number of marked birds seen alive in any given year corrected for annual
 variation in detection probability, and R is estimated as the average of
 the ratios from observations of marked individuals throughout the flyway.
 The time series of population estimates were used as input data for the
 IPM using a log-normal likelihood. The CMR program is
 the most expensive of all monitoring instruments, costing €93,569 per year
 for materials, salary, and travel.  Spring temperature
 in Svalbard – Warm May temperatures on the breeding grounds, used as a
 proxy for snow melt in relation to the timing of egg-laying by geese, tend
 to improve reproductive success of pink-footed geese (G H Jensen, Madsen,
 Johnson, & Tamstorf, 2014). Therefore, daily average temperatures
 during May at two locations in Svalbard (Ny Ålesund and Svalbard Airport)
 are retrieved each year from the Norwegian Center for Climate Services
 (https://seklima.met.no/observations/). The number of days in which the
 average temperature is above freezing are tabulated and the number of
 “thaw days” are calculated by averaging values from the two stations.
 There is no additional cost incurred in acquiring these data.