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Multi-targeted management of upland game birds at the agroecosystem interface in midwestern North America

  • Marlis R. Douglas,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – review & editing

    Affiliation Biological Sciences, University of Arkansas, Fayetteville, Arkansas, United States of America

  • Whitney J. B. Anthonysamy,

    Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – review & editing

    Current address: Basic Sciences, St. Louis College of Pharmacy, Saint Louis, Missouri, United States of America

    Affiliation Biological Sciences, University of Arkansas, Fayetteville, Arkansas, United States of America

  • Steven M. Mussmann,

    Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Writing – review & editing

    Current address: U.S. Fish & Wildlife Service, Southwestern Native Aquati Resource and Recovery Center (SNARRC), Dexter, New Mexico, United States of America

    Affiliation Biological Sciences, University of Arkansas, Fayetteville, Arkansas, United States of America

  • Mark A. Davis,

    Roles Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

    Affiliation Illinois Natural History Survey, University of Illinois, Champaign, Illinois, United States of America

  • Wade Louis,

    Roles Funding acquisition, Investigation, Project administration, Supervision, Writing – review & editing

    Affiliation Illinois Department of Natural Resources, Gibson City, Illinois, United States of America

  • Michael E. Douglas

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliation Biological Sciences, University of Arkansas, Fayetteville, Arkansas, United States of America

Multi-targeted management of upland game birds at the agroecosystem interface in midwestern North America

  • Marlis R. Douglas, 
  • Whitney J. B. Anthonysamy, 
  • Steven M. Mussmann, 
  • Mark A. Davis, 
  • Wade Louis, 
  • Michael E. Douglas


Despite its imperative, biodiversity conservation is chronically underfunded, a deficiency that often forces management agencies to prioritize. Single-species recovery thus becomes a focus (often with socio-political implications), whereas a more economical approach would be the transition to multi-targeted management (= MTM). This challenge is best represented in Midwestern North America where biodiversity has been impacted by 300+ years of chronic anthropogenic disturbance such that native tall-grass prairie is now supplanted by an agroecosystem. Here, we develop an MTM with a population genetic metric to collaboratively manage three Illinois upland gamebirds: common pheasant (Phasianus colchicus; pheasant), northern bobwhite quail (Colinus virginianus; quail), and threatened-endangered (T&E) greater prairie chicken (Tympanuchus cupido pinnatus; prairie chicken). We first genotyped our study pheasant at 19 microsatellite DNA loci and identified three captive breeding stocks (N = 143; IL Department of Natural Resources) as being significantly bottlenecked, with relatedness >1st-cousin (μR = 0.158). ‘Wild’ (non-stocked) pheasant [N = 543; 14 Pheasant-Habitat-Areas (PHAs)] were also bottlenecked, significantly interrelated (μR = 0.150) and differentiated (μFST = 0.047), yet distinct from propagation stock. PHAs that encompassed significantly with larger areas also reflected greater effective population sizes (μNE = 43; P<0.007). We juxtaposed these data against previously published results for prairie chicken and quail, and found population genetic structure driven by drift, habitat/climate impacts, and gender-biased selection via hunter-harvest. Each species (hunter-harvested or T&E) is independently managed, yet their composite population genetic baseline provides the quantitative criteria needed for an upland game bird MTM. Its implementation would require agricultural plots to be rehabilitated/reclaimed using a land-sharing/sparing portfolio that differs markedly from the Conservation Reserve Program (CRP), where sequestered land decreases as agricultural prices escalate. Cost-savings for an MTM would accrue by synchronizing single-species management with a dwindling hunter-harvest program, and by eliminating propagation/stocking programs. This would sustain not only native grasslands and their resident species, but also accelerate conservation at the wildlife-agroecosystem interface.


Anthropogenic impacts are a serious challenge for biological diversity [1], with major contributors being global climate change and habitat fragmentation. Each can rapidly and independently extirpate biodiversity (i.e., "with high confidence [2]," [3,4]). Recreational hunting also exerts a relatively consistent pressure [5], under the tenet that wildlife is a resource that can be optimally harvested, much like timber, yet with similar complications [6].

Interestingly, the interactions among anthropogenic drivers has been relatively unexplored [7], due largely to difficulties in gauging their gradual, consistent, and low intensity impacts in the field. However, reliable quantification has occurred in the laboratory. The synergy among overharvest, habitat fragmentation, and environmental warming, for example, reduces rotifer populations 50x faster than each driver acting independently [8]. This issue was first recognized some 30 years ago [9], with impacts identified as multiplicative rather than additive. Yet surprisingly, subsequent research has been limited [10,11], and perceptions have shifted concomitantly. A more expansive interpretation now recognizes this synergy as “… a chronic anthropogenic disturbance” [12,13] strongly associated with global species-extinctions (25% mammalian, 13% avian, and >21,000 ‘other;’ [14]).

Multi-species management

The gradual but ongoing progression of chronic anthropogenic disturbance negatively impacts adaptive management, and in a variety of ways. For example, it seriously strains budgets [15], and its gradual manifestation often promotes managerial indecision. In addition, studies that strive to quantify its effects often yield results that vary temporally [16], and with ambiguous interpretations. More traditional conservation programs attempt to compensate by shifting perspectives from local to regional [17], yet this often creates difficulties in that a coalesced approach can be too broad of a template for single-species management [18].

In contrast, an MTM approach (i.e., a framework for management decisions with benefits optimized for a community) offers several positive considerations. It can reduce management conflicts when several at-risk species are incorporated, and adequately address common threats across each while simultaneously eliminating the potential for species-specific redundancy [19]. Conservation efforts can also be effectively optimized under this approach, particularly when study species are taxonomically related, co-occur in similar habitats, and are impacted by comparable threats (as herein). We employed these considerations in this study as a focus for our management plan.

Midwestern North America

A history of chronic anthropogenic disturbance is clearly reflected in midwestern North America, with its 300+ year record of forests felled, prairies plowed, and streams sequestered for agricultural and urban purposes. The region supports an energy-intensive economy where greenhouse gas emissions exceed the national average by >20% [20]. Furthermore, its growing season and agricultural row-crop technology have now been significantly extended [21, Table 1], and both greatly enhance the well-established agricultural capacity of the region [22].

Table 1. Genetic diversity estimates based on 19 microsatellite DNA loci derived for 686 pheasant sampled from 14 Illinois pheasant habitat areas (PHAs; see S1 Table for abbreviations).

Such enhancements, while economically positive, have also compressed regional biodiversity into novel prairie-like parcels distributed randomly across an expansive agricultural matrix. Its persistence is inexorably challenged not only by a suite of ongoing anthropogenic pressures (as above), but also by a concomitant erosion of ecosystem services (i.e., anthropogenic benefits directly or indirectly received; [23]). Yet the negative aspects of agriculture, as seen from a conservation stance [24], may in fact offer positive considerations when placed within a more management-oriented land-sharing/sparing portfolio (below).

Upland game birds

Upland birds are an historic component of North American biodiversity, well documented in both Pleistocene fossil records and the earliest regional ornithological collections [25]. Their life histories juxtapose with the extensive open grassland on the east coast of North America, as promoted by natural disturbances such as wildfire and the felling of trees by beaver. The burning and clearing practices of Native Americans sustained and extended open areas and were subsequently emulated by early European settlers [25].

Chronic anthropogenic disturbance had an early initiation in North America, and subsequently became quite challenging for upland game birds, many of which are (or were) hunter-harvested [26]. We now have a management imperative to sustain these species, not just from an economic stance [27], but also in support of a uniquely American tradition: hunting open to all but subject to access when land is privately owned [28]. An additional challenge is that federal/state agencies are often tasked with dual but diametrically opposed mandates in this regard: to accommodate wildlife for recreation on one hand, yet also sustain and recover T&E species on the other. Thus, an ongoing requirement is to gather sufficient data for congruent, economically feasible management strategies that extend across multiple species. Contemporary technologies and integrated approaches such as a land-sharing/ land-sparing portfolio help facilitate decision-making and stand in contrast with more politically-biased policies [29] such as The Conservation Reserve Program (CRP, a provision of the 1985 U.S. Farm Bill) (discussed below).

Genetic integration and MTM

The spatial constraints of habitat fragmentation generally require management at the population genetic level, in that genetic drift (i.e., random fluctuations in allele frequencies over time; [30]) is a frequent byproduct. A second important parameter is effective population size (Ne), which reflects the loss of heterozygosity due to drift and links strongly with demographic factors such as sex ratio, population size, and lifetime fitness. Consequently, those population effects manifested through demography and environment can best be gauged by evaluating Ne.

In a similar manner, severe impacts also emerge when the size of a population is reduced by harvest. In the near term, genetic variability and individual fitness are depleted, with the trajectory of the population being depressed in the long-term [31]. Ongoing selection on gender and maturity also targets the reproductive component of populations, with reverberations again tracked via Ne. Importantly, these effects can be not only documented with population genetic tools, but also remediated as well [32]. Despite these caveats, population genetic approaches have yet to be fully implemented into wildlife management [32,33], as opposed to that found in fisheries [34].

We employ population genetic approaches to characterized genetic diversity as it relates to chronic anthropogenic disturbance in the non-native common pheasant [(Phasianus colchicus; pheasant)] [35]. We then contrasted these results with those previously derived for the state-endangered greater prairie chicken (Tympanuchus cupido pinnatus; prairie chicken) [36] and hunter-harvested northern bobwhite quail (Colinus virginianus; quail) [37] (Fig 1).

Fig 1. Study species.

(A) Male common pheasant, Jefferson Co., IL; (B) Male greater prairie chicken on booming ground, Jasper Co., IL; (C) Male northern bobwhite quail, Marion Co., IL (pictures courtesy of Richard Day, Daybreak Imagery, Alma IL 62807,

In this sense, our comparative approach extends from introduced to native species, and from hunter-harvested to T&E components. This allows us to explore the capacity of population genetics as a baseline for an upland game bird MTM. Our contemporary and economical approaches were designed to resonate with stakeholders and interest groups [38]. By doing so, we transition policy and planning from a more traditional single-species approach to one that engages multiple species [32]. We first evaluated wild pheasant in Illinois, as sampled from non-supplemented habitat fragments (PHAs: pheasant-habitat-areas). We then tested if these were distinct from state-maintained propagation stock employed annually to supplement “controlled hunting reserves” (CHRs). We then tested PHAs for temporal and/or spatial structure, and for evidence of inbreeding and interrelatedness. These results were contrasted with those from two other Illinois upland game species (i.e., prairie chicken and quail). This allowed us to ascertain whether our composite results are a basis for a state-driven MTM plan, and a potential blueprint for a similar plan region-wide.

Materials and methods

Upland game bird natural history

Common pheasant.

Pheasant was initially introduced into Oregon from mainland China (1880–81; available from: to serve as an additional upland game bird suitable for anthropogenic hunter-harvest. However, repeated serial releases by state and federal agencies were required over many years before it became a self-sustaining component of the Great Plains and an icon of hunter-harvest [39]. Its abundance increased steadily through the mid-20th century, peaking in the early 1960s with >one million harvested [40]. Hunter-harvest subsequently declined continent-wide through the 1970s, with <30,000 harvested in Illinois during 2016 [41].

The Illinois Department of Natural Resources (IDNR) proactively established 22 statewide Pheasant Habitat Areas (PHAs) as a non-augmented public hunting resource, with male-only take allocated via lottery. Additional ‘controlled hunting reserves’ (CHRs) also provide recreational opportunities, supplemented annually by state-propagated stock. The potential for gene flow among PHAs and CHRs is reduced by intervening agricultural land (20km minimum; [42]) (Figs 1 and 2).

Fig 2. Map of Illinois depicting pheasant habitat areas (PHAs) and controlled hunting reserves (CHRs).

Interstate highways (= red); Illinois River (= blue); non-supplemented Illinois Department of Natural Resources (IDNR) Pheasant Habitat Areas (PHAs = green dots; N = 14); Controlled Hunting Sites (CHRs = black triangles; N = 13) are supplemented by propagated pheasant.

Small, temporary groups of male pheasant coalesce during winter, whereas females represent larger, more stable flocks. Prominent components of pheasant life history are territorial defense and polygyny, with males actively competing for and subsequently defending territories. Here, the male strategy is to actively monopolize open ground adjacent to cover, as these represent prime locations for females to forage [43]. The latter disperse in spring and are actively recruited by males into small harems within territories. Females will nest outside these territories yet often return to the same male the following year.

Greater prairie chicken.

Much like pheasant, the prairie chicken is also a ground-nesting game bird that inhabits mixed-grass/ tallgrass prairie interspersed with patches of cropland. Dense brush is critical for nesting, as it offers protection from climate and predators, whereas more open areas are necessary for foraging. During winter, prairie chicken gather near croplands to access supplemental food. However, modern agricultural techniques frequently reduce and fragment adjoining habitat, and this leads to sharp declines in population numbers (available from:

The breeding strategy of prairie chicken involves dominance polygyny, where males display for females on leks. Yet only a small subset subsequently reproduce. Prairie chicken was once widely distributed across the North American great plains but has now been reduced to small, isolated fragments that require intensive management. It has declined sharply in Illinois from millions (mid-nineteenth century), to 2000 (1962), then 46 (1998), necessitating serial translocations from out-of-state. The most recent four-year population estimate (2010–2013; [36]) averaged but 79 males.

Northern bob-white quail.

This species resides year-round in ephemeral upland habitat with multiple successional stages, to include agricultural fields, grasslands, and open forest. It exhibits many characteristics of an r-selected species, i.e., early reproduction, high reproductive capacity, and short life span [37]. Bobwhite is highly social, often found in groups, or coveys containing up to 20 individuals that roost in a close-packed, outward-facing circle so as to conserve heat and sustain group-awareness. It was once common in eastern North America, but now reflects substantial widespread and cumulative declines (~85%; available from:

Small-scale agriculture often provided suitable habitat for quail, but this has largely been eliminated by the advent of larger, more mechanized farming practices. For example, old fields were once prime habitat for quail. They have not only been replaced, but those remaining have been invaded by exotic grasses that now render them unsuitable for quail. In addition, larger tracts of intensive row crop agriculture, or contiguous mature forest, now act to segregate quail populations and promote genetic drift.

Pheasant samples and DNA techniques

From 2010–2012, feathers from wild males (= ILWI) were harvested by lottery-selected hunters across 22 PHAs (Fig 2), thus no IACUC approval was required. Feathers were also obtained from three propagated stocks: (1) a private facility as a source of original Manchurian stock (MacFarlane Pheasants, Inc., Janesville, Wisconsin = MFMA) < available from:>; (2) original Manchurian stock now maintained for many years by IDNR (James Helfrich Wildlife Propagation Center, Lincoln, Illinois = JHMA); and (3) ‘game farm’ progeny derived from JHMA roosters x ILWI hens (Helfrich Propagation Center = JHGF). The latter are used to restock CHRs on an annual basis. We extracted genomic DNA from sample feathers using a protocol (Qiagen DNeasy® Kit) that compensates for low DNA yields.

Microsatellite amplification and genotyping.

We tested 83 microsatellite DNA loci originally developed for eight galliform species [44,45,46,47,48,49,50], with 24 (29%) yielding unambiguous genotypes. Forward primers were labeled with Applied Biosystems (ABI) fluorescent dyes. Polymerase chain reactions (PCR) were run in 10–15μl volumes containing 1x Go-taq flexi buffer (PROMEGA), 3.5mM MgCL2, 0.25mM dNTPs, 0.2μg BSA, 0.5–1.0 units Go-taq DNA polymerase (PROMEGA) and 40ng DNA. Cycling conditions were: initial denaturation 3m at 95°C, 15 cycles for 45s at 95°C, 45s at 52°C, 1m at 72°C, 25 cycles for 30s at 95°C, 30s at 52°C and 45s at 72°C. Fragments were resolved on an ABI 3730 Genetic Analyzer and an internal size standard (Liz500) was included with each sample.

Alleles were scored with GeneMapper v4.0 (ABI) and data quality assessed for three captive stocks and each of the 14 PHAs using MicroChecker v2.2.3 [51]. Deviations from Hardy–Weinberg Equilibrium (HWE) and linkage equilibria (LD) were computed using exact tests in GenePop [52,53], with P-values estimated via Markov Chain with 10,000 dememorizations, 200 batches, and 5,000 iterations. Level of significance was evaluated using sequential Bonferroni tests.

Genetic diversity and population structure.

Genotypes were assayed across 19 microsatellite DNA loci. From these data, standard genetic indices were calculated, including observed heterozygosity (HO) and mean number of alleles (AM) for each of 14 PHAs N = 543) and captive propagation stock (i.e., MFMA, JHMA, JHGF; N = 143) (GenAlex v6.5 [54]). HO is proportional to the amount of genetic variance at a microsatellite locus (i.e. heritability), yet also reflects the manner by which genetic variation is impacted by population size. Allelic richness (AR) and private allelic richness (APR) was estimated using rarefaction based on the smallest diploid sample (HP-Rare [55]).

An hierarchical approach was employed to: (1) evaluate genetic diversity and divergence among wild pheasant (PHAs) and propagation stocks (i.e. MFMA, JHMA, JHGF), and (2) assess population genetic structure among PHAs. Pairwise FST values were calculated to assess gene flow among the four groups, as well as among PHAs (Arlequin v3.5 [57]). To gauge isolation-by-distance (IBD) among PHAs, a Mantel test was employed (GenAlex) so as to compare pairwise genetic distance [FST/ (1- FST)] and pairwise geographic distance (log10+1 transformed).

A Bayesian assignment test (Structure v2.3.4 [58]) was employed to assess genetic structure among propagated stocks and PHAs. The combined analysis involved an admixture model with no priors and correlated allele frequencies, with K-values = 1–20. The program was run for 1,100,000 generations with the first 100,000 discarded as burn-in. Independent replicates (N = 32) were performed at each K-value to test for consistency and to derive ΔK and L(K) [59]. As recommended [61], ΔK was compared against biological and biogeographic patterns (Structure Harvester v0.6.94 [60]), with both statistics evaluated to determine an appropriate K [61]. Outputs were assessed (Clumpp v1.1.2 [62]) to ascertain multimodality at each K, with individuals then appropriately assigned to gene pools (Distruct v1.1 [63]). The above process was repeated for PHAs only, using a more restricted range of K values (K = 1–15), with similar iterations, burn-in, ΔK derivation, and visualizations as above.

Demography and consanguinity.

Population persistence can be gauged in several ways, but most frequently via demographics (i.e., small population paradigm [64]). Here we quantified recent population bottlenecks (<five generations) by contrasting Ho empirically derived against that expected (He) under Hardy–Weinberg equilibrium (HWE) (BottleNeck 1.2.02 [65]).

We applied the infinite alleles model (IAM [66]) to gauge significance of heterozygosity-excess in each PHA (Wilcoxon signed-rank test [67]). A mode-shift test was also applied to evaluate historic bottlenecks (i.e., ~20 generations), with Ne (heterozygosity loss in each generation due to genetic drift) estimated for each PHA and propagation stock using the linkage disequilibrium (LD) method (NeEstimator_v2 [68]). Estimates were derived at P = <0.01, with jackknifed 95% confidence intervals.

BayesAss3 [56] was employed to test for demographic independence among propagation stocks and PHAs. A Bayesian approach (NewHybrids [69] ( was used to examine hybridization/introgression between Manchurian, game farm, and PHAs. Individuals were assigned via posterior probabilities into one of six hybrid classes (i.e., pure game farm; pure Manchurian; F1; F2; game farm backcross; Manchurian backcross) [70]. All Manchurians were classified as one parental and game farm as a second. Calculations were performed with 100,000 burn-in generations followed by 1,000,000 sampling generations.

Mean relatedness (R) was used to test for siblings and/or parent–offspring pairs (TrioML method; Coancestry v1.0 [71,72]). A value of R = 0 indicates no relation; R = 0.125 = 1st cousin, R = 0.25 = half-siblings, and R = 0.5 = parent/offspring or full sibling [73]. A Mantel test in GenAlex contrasted pairwise mean relatedness versus geographic distances separating PHAs.

Spatial structure.

Genetic discontinuities were evaluated by using a Bayesian clustering method (R-package Geneland ver. 4.0.4 [74]) to model the multi-locus, geo-referenced pheasant genotypes. The uncorrelated model of allele frequencies was utilized, as was a non-zero value for the uncertainty among coordinates. This allowed the program to assign individuals to different clusters despite being sampled from the same site. It also has the potential to detect migrants that might otherwise remain undiscovered by taking into account their spatial coordinates, then allocating genotypes into K-clusters with HWE and LD minimized within groups. Four independent runs with 10 million MCMC iterations were performed, with every 1,000th being saved. The number of genetic clusters (K) was initially set to vary between 1 and 21, and the model was run four consecutive times, with clusters treated as ‘known’ based upon inferences from previous runs. The posterior probability of population membership was computed using a 300-iteration burn-in. Spatial relationship among PHAs was also evaluated by testing for isolation-by-distance (IBD).

We performed a Discriminant Analysis of Principal Components (DAPC) to visualize differentiation among PHAs and propagation stock (Adegenet v2.0; [75]). This method first transforms the input microsatellite data into PCA loadings, then subsequently employs these uncorrelated variables as input for a discriminant analysis. The efficient summarization of high-dimensional data allows for the genetic structure among populations to be visually assessed. Sixty (of 185) PC axes explained 90.4% of the variance in the DAPC analysis. When performed on only PHAs, the first 60 (of 168) PC axes were again retained, with 92.9% of the variance explained. All discriminant axes were retained in both analyses.

Upland game bird structure and demography

PHAs with ≥14 samples for each of three years were subsequently evaluated for genetic stability over time using assignment tests that quantified temporal structure. Demographic data for prairie chicken, to include relatedness, were previously derived by our lab [36] and employed herein. However, these data were not included in the published results for quail [37], and we thus obtained genotypes from the web (N = 434) so as to derive suitable statistics. We first calculated a population genetic baseline (diveRsify, [76]) followed by Ne and Bottleneck analyses so as to parallel the approach used for pheasant above. Recent migration rates were also calculated among assemblages using BayesAss [56].

Published analyses for relatedness in quail excluded N = 66 individuals due to the potential for consanguinity (these individuals were not identified in the original data; [77,78]). However, we deemed relatedness as an inherent component of upland game bird natural history, and hence included all individuals when we derived relatedness values (R-program related [79,80]).


Preliminary analysis of pheasant data

Feathers were obtained from 22 PHAs (S1 Table), eight of which had insufficient sample sizes for analysis. The remaining 14 yielded 686 unique samples, with 543 successfully genotyped (μ = 39; Fig 2). Propagation stock also yielded 143 individuals (μ = 48; Table 1). Although data were generated across 24 loci, five of these were subsequently eliminated (three due to scoring issues and two others that expressed null alleles across multiple populations). Significant LD was detected for six pairs of loci, but these occurred once in four sampling groups and were non-significant following Bonferroni correction. Thus, 19 loci were employed in subsequent analyses (S2 Table).

Genetic diversity was relatively low among the three propagated stocks of pheasant, with AR ranging from 5.9–6.6, and Ho ≤ 0.60. The stocks differed significantly among themselves with regards to FST, and from the average FST for PHAs (S3 Table). Genetic diversity was also reduced within the 14 PHAs, with AR ranging from 4.3–5.2, and HO ≤ 0.62. Most pairwise comparisons among PHAs were significant (Bonferroni-corrected α = 0.0005), save those within the same county or immediately adjacent (S4 Table). We also found a significant pattern of IBD among PHAs (r = 0.52; P<0.002).

Additional results for pheasant population structure, demography, consanguinity, and spatial structure are reported below. This was done to facilitate comparisons with prairie chicken and quail.

Upland game bird population structure

For the combined propagation stock/ PHA dataset, K = 4 was selected as the best estimate for groupings, per output from the ΔK plot. Propagated stock was genetically distinct from PHAs, with the two distinct propagated stocks being Manchurian (MFMA, JHMA) and Game Farm (JHGF) (Fig 3).

Fig 3. Illinois pheasant (both propagated stock and wild individuals in Illinois) assigned to group (K = 4), as determined by posterior probabilities in a Structure analysis based upon 19 microsatellite DNA loci.

Lateral lines in the plot represent 686 individuals. Propagation Stock = Two groups: Manchurian (MFMA + JHMA) and Game Farm (JHGM) (Total N = 143; groups demarcated by red lines). MFMA = MacFarlane Manchurian; JHMA = J. Helfrich Manchurian; JHGM = J. Helfrich “game farm.” The remaining 14 (left column; see S1 Table for abbreviations) represent wild pheasant from Pheasant Habitat Areas (PHAs; N = 543), are partitioned into two aggregates: 12 PHAs statewide (upper) separated by a horizontal red line from two lower PHA that are isolated on all sides by interstate highways in Central Illinois; Fig 2).

Bayesian assignment tests revealed scant separation among the 14 PHAs (Fig 3), save for two that formed a distinct group in central Illinois (Fig 2). Mean migration rates between these two aggregates (μm =0.08%) supported their demographic isolation. Pairwise FST-comparisons among PHAs (80/91) also indicated significant isolation (μFST = 0.047; Bonferroni-corrected p<0.0006; S4 Table). However, 10 non-significant FST comparisons involved a single PHA (= VEHW) that was not only significantly bottlenecked but had an extremely small sample size (N = 16; Table 1).

We then evaluated our 14 PHAs separately in Structure, without the potential influence of the three propagation stocks. Given previous results (Fig 3), we first elected to contrast ΔK and L(K) results for the PHAs, and in doing so found frequent conflicts. Given this, we subsequently treated ΔK as a “lower bound” for the number of genetic clusters, and L(K) as our “upper bound.” We also noted that K-values ≥5 rarely coincided with any discernible spatial pattern, a result subsequently corroborated by spatial structuring (below).

K = 2 emerged as the best estimate for groupings across 14 PHAs, with additional but less informative peaks recorded at K = 3, 8, and 11. The two groups were DWFF and DWHV (per Fig 3) versus the remaining 12 PHAs (Fig 4). We then employed Structure to evaluate the four PHAs that sustained an N≥14 over three successive years. These results (Fig 5) reflect a consistent demographic trend across years in the population structure of these PHAs.

Fig 4. Illinois pheasant from 14 pheasant habitat areas (PHAs) assigned to group (K = 2) according to posterior probabilities in a Structure analysis based upon 19 microsatellite DNA loci.

Lateral lines in the plot represent 543 individuals. Two aggregates are present, separated by a horizontal red line. The upper group contains 12 PHAs, while the lower group contains but two (DWFF and DWHV) isolated on all sides in Central Illinois by interstate highways; Fig 2).

Fig 5. Results of a Structure analysis employing 19 microsatellite DNA loci that depict changes in genotypic frequencies across four pheasant habitat areas (PHAs) in Illinois over three consecutive years.

Individuals are represented as vertical lines in plots that represent four pheasant habitat area (PHAs; left column) assigned according to year of capture (i.e., 2010/ 2011/ 2012). IQMG = Milks Grove; DWFF = Finfrock; MLSB = Saybrook; FOSI = Sibley (see S1 Table).

Although prairie chicken populations differed significantly across FST values, leks within each were genetically similar. Likewise, quail populations also differed significantly across FST values [77]. We subsequently corroborated the latter result, and in so doing found but a single non-significant comparison out of 15 (Fisher’s Exact Text for sample independence with 5000 Monte Carlo replications using program diveRsify; S5 Table).

Upland game bird demography and consanguinity

All PHAs save two were significantly bottlenecked (Table 1). Effective population size also varied among PHAs (μNe = 43.3, range = 21.0–90.3; Table 1). These estimates were significantly associated with PHA area (in ha) (F1,12 = 10.4, P<0.007), thus underscoring the importance of patch size in determining upland game bird demography.

Mean relatedness within PHAs (μR = 0.150) generally exceeded 1st cousin (R = 0.125; Table 1), with a significant but inverse correlation between pairwise relatedness and geographic distance (r = -0.33; P<0.01). Little evidence was found for the introgression of Manchurian stock into wild populations (NewHybrids; Fig 6). Similarly, mean migration rates (μm =0.006%) between PHAs and propagated stock clearly supported demographic isolation.

Fig 6. Results of a Bayesian assignment test (NewHybrids) employing 19 microsatellite DNA loci analyzed across 143 Illinois pheasant.

Plot depicts potential admixture among three groups: Manchurian, game farm, and wild. Individuals are plotted as vertical lines, with potential assignment to six categories. Man = Manchurian stock derived from original brood and maintained by IL Dept. Natural Resources (IDNR); Game = Progeny of Manchurian roosters and IL Wild hens maintained by IDNR; IL Wild = Wild pheasant taken from Pheasant Habitat Areas (PHAs); GF = Game (as above); MA = Man (as above); F1 = First generation hybrid; F2 = Second generation hybrid; GF Mx = Backcross with game farm; BA Mx = Backcross with Manchurian.

Both prairie chicken populations had extremely low Ne values (12.7 and 13.5), with statistically significant evidence for recent and historic bottlenecks. Leks (N = 6) also reflected low Ne (μ = 15.9, range = 2.9–38.4), with significant bottlenecks apparent in four, and an historic signal manifested in three others. Quail populations also reflected reduced Ne, comparable to that found in pheasant (μ = 62.1; range = 31–107). Five (of 6) were significantly bottlenecked but lacked an historic signal across generations (S6 Table).

Overall relatedness was significantly higher in quail than expected by chance alone (p<0.02; S1 Fig). One county (Saline) had restricted gene flow (μm =0.04%), and thus represented a distinct management unit [81].

Upland game bird spatial structure

Significant IBD was also observed among PHAs (r = 0.52; P<0.002). Although population structure was predominantly global in nature (p<0.016), gene flow was seemingly modulated by landscape features.

Results from Geneland were somewhat reduced in that samples from PHAs and leks associated with a single UTM. Thus, genotypes could only be parsed into K-clusters by minimizing Hardy–Weinberg disequilibrium and gametic phase disequilibrium within groups. All wild pheasant were assigned to 14 clusters, representing PHAs (per Fig 3), whereas prairie chicken grouped into two populations, each representing a separate Illinois county (congruent with published results [36]). No ‘ghost’ populations or migrants were identified in either species.

Our DAPC analyses included both PHAs and captive broodstock, with 60 (of 185) PC axes retained, explaining 90.4% of the variance in the data. A plot of discriminant axes 1 and 2 (Fig 7A) depicts both propagated Manchurian stocks (i.e., MFMA and JHMA) as relatively distinct on axis 1 and separated from the third propagated stock (i.e., JHGF), as well as the 14 PHAs. The second axis separates DWFF (Finrock PHA) from the remaining 13 PHAs.

Fig 7. Results of a discriminant analysis of principal components (DAPC) analysis depicting differentiation among 14 pheasant habitat areas (N = 543) and three state propagated stocks (N = 143) in Illinois, as based on genotypes across 19 microsatellite loci.

Part A: Two propagated stocks, i.e., MFMA (MacFarlane Manchurian) and JHMA (J. Helfrich Manchurian) are separated on discriminant axis 1 from the third propagated stock (JHGF; J. Helfrich “game farm”) as well as the 14 PHAs. Axis 2 distinguishes DWFF (Finrock PHA; S1 Table) from the remainder. Part B: Two PHAs, i.e., DWFF and DWHV (Hallsdale PHA; S1 Table) separate on axis 1 while KXVI (Victoria PHA; S1 Table) is not consideration distinct at the apex of axis 2 as it is scattered quite diffusely.

The results of a similar analysis, performed only with the 14 PHAs, is presented in Fig 7B. The first 60 (of 168) PC axes were again retained, explaining 92.9% of the variance. DWFF is again relatively discrete on axis 1, with DWHV (Hallsdale PHA) somewhat peripheral on that axis as well. KXVI (Victoria PHA) and DWBB PHA (Birkbeck; S1 Table), at top and bottom of axis 2, respectively, are diffusely scattered with weak separation.

Upland game bird comparisons

Pheasant, quail, and state-endangered prairie chicken differed with regard to numbers of aggregates and individuals (Table 2). Pairwise aggregations within each species also differed significantly. Pheasant and prairie chicken reflected significant IBD whereas quail did not. Structure analyses clearly separated prairie chicken populations, but not quail or pheasant, yet aggregates within each differed significantly when compared using FST-tests. Average heterozygosity, suggestive of short-term survival, did not differ significantly among the three, whereas allelic diversity did (suggesting a diminished long-term survival). In summary, population genetic parameters clearly juxtapose across all three species, and this uniformity is driven largely by chronic anthropogenic disturbance.

Table 2. Comparison of population genetic and life-history parameters gauged among populations of wild pheasant (pheasant habitat areas = IL PHAs), greater prairie chicken (= IL GRPC), and bobwhite quail (= IL BWQ) in Illinois.


Management priorities

Biodiversity must be managed cooperatively so as to minimize costs and optimize investments, particularly when evaluating anthropogenically-modified regions such as Midwestern North America. This framework often provides the basis for an MTM plan, pending additional covariates such as landscapes, phylogenetic relationships, dispersal capacities, and economic appraisals [13,82,83]. Yet, when this is done, an overly complex plan often emerges such that effectiveness becomes an overriding concern. For example, can a few covariates effectively parse numerous species? Is it effective when compared with alternatives? [84]. Our approach to this issue involved the development of an MTM that included a series of precise and consistent metrics as its basis, and we summarize our results below.

Context-specific options

Propagation stocks.

One concern with captive propagation is that genetic and demographic repercussions quickly surface when stocks or introductions are inappropriately managed [85]. For example, deleterious and partially recessive alleles are often sustained within brood stock due to the relaxed selection inherent to propagation facilities [86]. As an example, the propagated pheasant stock in this study were found to be significantly bottlenecked.

Our results also underscored significant relatedness among individuals, another compounding issue for propagation stock. The original Manchurian broodstock (MFMA) were most closely related (at half-sib, R = 0.24), whereas Illinois Manchurian (JHMA) exceeded first cousin (R = 0.143). These metrics are particularly relevant in that captive-reared parents are often bred iteratively for supplementation purposes, a practice that not only diminishes the effectiveness of propagated stock, but more importantly, impacts the fitness of progeny.

A potential remediation would be to constrain the time in captivity for parental stock [87]. Alternatively, controlled hunts could also be managed more sustainably by relying on wild rather than propagated individuals as a means of supplementation [88], an approach successfully employed in fisheries management. However, one important question for such a strategy is whether wild populations can adequately sustain the loss of adults so as to bolster reintroductions into controlled hunt areas.


Several questions emerge when the genetic consequences of exploitative hunting are discussed [31]. Can it be detected and mitigated? Does it indeed impact demography and yield? A response to the first question [31] was primarily circumstantial, whereas the second response was ‘under consideration.’

Hunter-harvest (particularly with a focus on body size and/or gender) can have serious demographic impacts, and these are consistently reflected in population genetic parameters. For example, a modeling exercise on the dynamics of a well-researched Fennoscandian moose population [89] demonstrated that the selective harvest of males promoted genetic drift in each subsequent generation, concomitant with a reduction in Ne. These effects persisted despite a consistent population growth, and without considering the potential for individual differences in male quality (i.e., all males treated equally).

In this study, we attempted to quantify ‘detection’ and ‘impact’ by utilizing a population genetic framework in our evaluation of wild and stocked pheasant. We also addressed the question of ‘mitigation’ by applying (and subsequently comparing) our metrics across additional upland game bird species (i.e., prairie chicken and quail).

Population genetics of upland game birds

We found that PHAs were not only isolated from one another, but with fluctuating demographics as well, as evidenced by significant differences in FST, relatedness, and bottleneck values. There was no gene flow from propagated stock to PHEs, despite the fact that CHRs (Fig 2) were annually supplemented with thousands of captive-bred individuals. This suggests two possibilities: hunting pressure is substantial in the CHRs, with population densities relatively depressed as a result. This would potentially reduce the competitive pressure that may result from substantial and iterative restocking. Alternatively, hunting pressure is reduced, but few individuals survive the winter season. Both scenarios would sustain the limited emigration observed from CHRs to PHAs.

Relevant population genetic parameters for T&E species such as prairie chicken are often difficult to derive, due largely to the inherent difficulties with sampling. However, we managed to derive genotypes at 19 microsatellite loci by extracting DNA from feathers shed on leks [36]. Our results pointed to significant demographic isolation, bottlenecks, minimal migration rates (m<1%), with Ne values quite low (4-year μ = 13.1). Relatedness and inbreeding values were also significantly elevated, with dispersal constrained by the presence of dominance hierarchies within leks, as manifested by 12 significantly different family groups (R = 0.31). Genetic patterns in prairie chicken clearly parallel those found in pheasant and do so despite potential life-history differences (Table 2).

Quail, a third management target, is hunter-harvested as well as being propagated within state facilities (yet the latter were not assayed [37]). Instead, wild, hunter-harvested individuals (N = 434) were sampled across six Illinois counties. Results parallel those found in pheasant and prairie chicken, with populations significantly isolated (S5 Table), bottlenecked (S6 Table), and more closely related than by chance alone (S1 Fig). Gene flow and population structure were also alarmingly depressed in quail [77,78], with IBD being apparent. However, interstate highways seemingly had little discriminatory effect on quail, mirroring a similar result in pheasant where 86% of PHAs were unaffected (save two surrounded on all sides by interstates).

Quail is also sedentary and ground-dwelling, with a strong communal instinct and a low capacity for dispersal [39]. An effective management plan for this species would be to acquire additional habitat adjacent to currently occupied sites (see sharing/sparing below). This approach would also juxtapose well with quail life history by providing additional habitat proximal to family groups.

These results, in combination, provide a clear understanding of the manner by which chronic anthropogenic disturbance negatively impacts population structure, demography, and landscape genetics of upland game birds in midwestern North America. As such, they represent a major challenge for wildlife management, particularly given the ongoing mandate that a given conservation investment should indeed elicit an optimized economic return [90].

Can a genetically informed MTM be effective?

Systems that incorporate multiple threats and species (as herein) are difficult to prioritize and monitor, and this in turn impacts an effective return from conservation expenditures. To be successful in this regard, an MTM must not only be robust (per population genetic metrics), but also flexible enough to sustain future activities and decisions. One mechanism is to employ the Open Standards for the Practice of Conservation [91], with goals that would modify/increase prairie habitat patches, establish their inter-connectivity, and promote ‘no-take’ reserves that prevent hunter-harvest. Here, the conservation ‘targets’ would be prairie-obligate birds, with measurable ‘indicators’ being precise and consistent population genetic metrics. Below, we outline the manner by which these goals can be attained for upland game birds, particularly within the extensive agroecosystem of midwestern North America.

Clearly, the resource most limiting in this situation is habitat. Agriculture occupies 40% of global ice-free land [92], and is regarded as the single greatest threat to global biodiversity [93]. Impacts on biodiversity will be extensive, given that anthropogenic food demands will double in a scant few decades [14]. Consequently, it is imperative that methodologies be employed within the context of Open Standards that will actively and collaboratively integrate both agroecosystems and biodiversity. One such solution is the concept of ‘land sharing,’ where both components coexist within the same landscape. A second is ‘land sparing,’ a process that effectively isolates biodiversity from an agricultural matrix [94].


Here, emphases are two-fold: to retain small patches of unfarmed natural or semi-natural vegetation within larger agricultural plots, and to reduce the negative effects of mechanized agriculture on lands adjacent to these plots [95]. The approach is particularly germane for inherently large midwestern agroecosystems where monoculture predominates. Less productive agricultural areas are not only available in this context, but also sufficient to accommodate native vegetation as well as edge habitat that allows for dispersal and connectivity (per Open Standards). Downsides include a potential reduction in agricultural yields, as well as the habitat degradation that can emerge when small, segregated plots are gradually engulfed by an expanding agroecosystem [21].

Land-sharing was once prevalent in midwestern North America, with small circa-1950 farms producing grains, hay, and livestock within fields demarcated by fencerows. But a generational shift in agricultural efficiency has resulted in these smaller, more marginal plots of habitat being lost. Now, crops are predominantly corn/soybean interspersed by pastures/waterways of dense, cool-season brome/fescue unfavorable for upland game birds. Agricultural efficiency has promoted the incorporation of topographically more diverse habitat as well. Yet, these situations can be easily rectified. The less-productive edge-habitat, largely subsumed by more efficient agricultural methods, can be easily re-established [41], and with but minor reductions in agricultural yield as a result.

We recognize that edge habitats will not sustain large, genetically diverse populations of upland game birds, but they will provide the corridors necessary for dispersal among currently isolated fragments. Elevated connectivity and its resulting gene flow would not only counteract demographic isolation but at the same time leverage those genetic metrics already depressed, such as migration rates, bottleneck effects, and reduced Ne. Those negative rates that currently characterize our three study species [i.e., elevated relatedness (R) and inbreeding (F)] would also be reversed as well.


Rather than modify existing agroecosystems for the benefit of wildlife (as above), the land-sparing approach emphasizes the protection/ restoration of as much native vegetation as possible. Establishing sufficiently large ‘no-take’ reserves, for example, would foster larger population sizes of upland game birds, provide the necessary refugia, as well as provide a buffer against ongoing climate change [96]. However, success is contingent upon two factors: individual movements, as well as reserve size, as both promote genetic diversity [97]. In this study, elevated Ne values for pheasant were significantly associated with larger PHAs (μNE = 43; P<0.007). A potential downside would be if agriculture was intensified on neighboring plots. This, in turn, would entail greater use of agrochemical, water, and energy resources [98]. Production costs become elevated while the quality of habitat in adjacent plots is simultaneously reduced.

Land-sparing is a positive concept for biodiversity, but only if land is actually ‘spared.’ Two limitations are apparent: spared land is not actually utilized for conservation (i.e., incomplete area sparing), and/or its quality may have diminished following an earlier assessment (i.e., lower habitat quality sparing). Despite these limitations, land-sparing still outperforms land-sharing, but only as long as ≥28% of the land is devoted to conservation, and if ≥29% of its original quality is retained [99].

For upland game birds, land sparing would allow additional populations to be established, an aspect particularly important for prairie chicken in that only two remain in Illinois. It would also promote larger, demographically more stable populations in all three study species. This, in turn, would enhance population genetic metrics such as FST and Ne, while also buffering against bottlenecks.

The Conservation Reserve Program (CRP).

Land sparing actually has a recognized legacy in the Midwest. The primary goal of the Conservation Reserve Program (CRP), a provision of the 1985 U.S. Farm Bill, was to ensure food security in the United States. In doing so, it provided a 10-year subsidy for removal of crops from farmland deemed suboptimal [100], and given this, wildlife habitat and water quality were enhanced, but only as an indirect effect.

A point of contention is that CRP acreage fluctuates in response to market conditions, with expansion occurring as agricultural prices drop, and retraction as they rise. For example, CRP land declined 35% from 2007 to 2014 (i.e., from14.9 to 9.7 million ha) [101]. These impacts were compounded as well: not only did we lose previously conserved land, but the increase in cultivation served to exacerbate global carbon emissions and depress ecosystem services [102]. An additional negative is that honeybee forage was similarly reduced, a situation concomitant with a reduction in colony numbers [103].

Despite early (and indirect) success, the CRP is now in a steady decline as agricultural prices escalate [104]. CRP-plots become strongly transitional as a result, (i.e., available only for a limited time), and thus are of limited conservation value. The situation may actually be detrimental, in that bottlenecks in resident species are iteratively induced as habitat is consistently reduced. This allows the same negative demographic mandates to again emerge: i.e., depressed Ne, heightened inbreeding, and elevated relatedness.

In summary, the CRP program provides upland birds with only indirect and intermittent benefits, due largely to the variance in (and loss of) allocated land. An additional discrepancy is the unregulated management of plots once so incorporated. In this sense, inappropriate plantings frequently occur, and non-native (pioneer) vegetation is allowed to encroach. In addition, the frequent mowing that occurs as a control mechanism for non-natives also serves to block natural succession.

The CRP clearly has an uncertain future, particularly given elevated commodity prices and ongoing energy developments that work in tandem to reduce its scope (available from: However, a land sharing/sparing program, one that incorporates a rigorous framework coupled with a long-term management plan, would not only sustain current CRP parcels but also sustain on a long-term basis those indirect conservation benefits that are now in serious decline.

Will land-sparing benefit upland game birds?

Common pheasant.

Direct benefits of land-sharing have yet to be recorded for upland game birds, although they can be extrapolated from those that have been gleaned from CRP-lands. In this sense, pheasant has directly benefitted from the high-diversity seed mix that was often employed on CRP-managed parcels, not only as a trophic component, but also as a means to promote pasture and small grain habitat [105,106,107,108]. However, these CRP-derived commodities are diminishing due to ongoing agroecosystem expansion. This not only reduces midwestern grasslands [109], but secondarily impacts pheasant. Despite the bleak prognosis, previous results clearly demonstrate that a well-thought out land-sharing portfolio would directly benefit pheasant in particular, and grasslands in general.

Northern bob-white quail.

Quail, on the other hand, clearly associates with farmland habitat, and its abundance is demonstrably promoted by the greater proportion of herbaceous vegetation found on CRP-land [110,111]. This underscores an important point: a necessary requirement for a land-sparing portfolio, particularly in the Midwest and Southeast, is the active promotion of early successional native plant communities [112]. Positive results for quail are achieved when management is proactive, and this means the elimination of dense exotic grasses while simultaneously optimizing edge habitat and open spaces [113]. Quail have repeatedly responded in a positive manner to well-managed CRP-parcels and will continue to do so if a well thought out land-sparing portfolio is employed.

Greater prairie chicken.

Breeding leks of prairie chicken have also increased as a direct result of the land-use characteristics most often promoted by CRP-lands (i.e., smaller residential-farmstead plots, reduced forest patches, and more expansive habitat parcels) [114]. Promoting an increase in the number of leks will also elevate the numbers of resident and competing males [115], an important consideration given the presence of dominant family groups [36]. Similarly, the retention of abundant grass and forb cover on CRP-fields also serves to promote nest survival in prairie chicken [116], a situation that similarly resonates with pheasant and quail. In summary, CRP-protected lands are a positive asset for prairie chicken, particularly when they are adjacent to grasslands, and when invasive plants are actively suppressed [113]. However, these requisites must become a management baseline within the land-sparing portfolio of an upland game bird MTM.

Edge habitat across species.

A proactive focus on edge habitat as a component of land-sharing will provide ecosystem services comparable to those found in more standard reference systems [117]. In addition, small patch sizes inherent to land-sharing can easily sustain small-scale generalists such as quail, with limited dispersal ability but a broader tolerance for agricultural practices [21]. These aspects can be enhanced when edges are placed adjacent to and/ or connected with a land-sparing component, such as that currently employed with Illinois PHAs. They also blunt small population effects, such as bottlenecks, low Ne, and elevated relatedness, each of which is currently manifested across our study species. The coupling of edge-habitat with a land-sharing augmentation would also stimulate upland game bird demographics by optimizing offspring survival, genetic variability, and Ne, while minimizing variance in reproductive success.

A potential solution

Conservation decisions are driven by economics and public concern, and these reverberate equally among policy makers and stakeholders. For sure, resources become limited and conflicts subsequently emerge when management objectives and societal needs overlap (such as with agricultural yields and recreational hunting). An economic baseline must clearly reside within an MTM, and although positive manifestations are recognized (as above), their delivery may be more difficult. Land-sparing would be less pressing of an issue if a land-sharing component could become a more viable approach. Instead, land-sparing is deemed the best approach to accommodate both agricultural production and biodiversity conservation, particularly for species with more restricted global distributions [98].

Thus, an MTM plan for upland game birds, one that would promote biodiversity and alleviate habitat loss/modification, must by necessity incorporate a land-sparing strategy. Similarly, it would also be divorced by necessity from the declining and market-driven CRP, where management directives are weakly established with regard to allocated land, whereas linkages with agricultural commodity pricing are quite strong.

On the positive side, the partitioning of land to protect biodiversity is recognized as a new global initiative [for example, The Half-Earth Concept [118] and Nature Needs Half (available from:]. Both require greater yields from areas already under cultivation, a process that may best be implemented at the state-level. In addition, funds now allocated for single-species conservation, and for propagation facilities that sustain a put-and-take hunter-harvest [119], could potentially be pooled as a cost-saving to initiate a state or region-wide MTM. This would also offset potential losses that may stem from a strong land-sparing mandate. The objective is contemporary management at the wildlife-agroecosystem interface, but importantly, with grasslands conserved, upland game birds promoted, and agricultural production sustained without the loss of additional habitat.

Supporting information

S1 Fig. Relatedness values (Wang, 2002) derived for 434 hunter-harvested northern bobwhite quail distributed across six aggregates in southern Illinois.


S1 Table. Location (latitude/ longitude) and size (in ha) for 22 Illinois pheasant habitat areas (PHAs).


S2 Table. Description of microsatellite (msat) DNA primers used in the study.

This includes number of sampled genotypes, alleles detected, multiplex assignment, annealing temperature, and violations of Hardy-Weinberg equilibrium (HWE) for each locus.


S3 Table. Pairwise FST values calculated for common pheasant propagation stocks and wild populations (ILWI) in Illinois.


S4 Table. Pairwise FST values calculated for common pheasant sampled from 14 Illinois pheasant habitat areas (PHAs).


S5 Table. Fishe’s Exact tests for pairwise independence of hunter-harvested northern bobwhite quail aggregations in Illinois.


S6 Table. Bottleneck and effective population size (Ne) estimates recorded for hunter-harvested northern bobwhite aggregations in Illinois.



We thank the following individuals: B.D. Anderson (Illinois Natural History Survey) and J. Buhnerkempe (Illinois Department of Natural Resources). Sampling was done by Illinois Department of Natural Resources. This research was supported by: U.S. Fish & Wildlife Service Federal Aid in Wildlife Restoration, Project W-155-R to MRD and MED (Illinois Natural History Survey); University of Arkansas Distinguished Doctoral Fellowship to SMM; and two University of Arkansas Endowments: Bruker Professorship in Life Sciences (MRD) and 21st Century Chair in Global Change Biology (MED). Analytical resources were provided by the Arkansas Economic Development Commission (Arkansas Settlement Proceeds Act of 2000) and the Arkansas High Performance Computing Center (AHPCC). Opinions expressed herein represent those of the authors and do not reflect those of the Illinois Department of Natural Resources, the Illinois state government, or the U.S. Fish & Wildlife Service.


  1. 1. Millennium Ecosystem Assessment. Ecosystems and human well-being: Biodiversity synthesis. Washington DC: World Resources Institute; 2005.
  2. 2. IPCC Climate Change: Synthesis Report. In: Pachauri RK, Meyer LA, editors. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC: Geneva, Switzerland. 2014.
  3. 3. Brook BW, Sodhi NS, Bradshaw CJA. Synergies among extinction drivers under global change. Trends Ecol Evol. 2008; 23: 453–460. pmid:18582986
  4. 4. Pimm SL. Biodiversity: Climate change or habitat loss—Which will kill more species? Curr Biol. 2008; 18: R117—R119. pmid:18269905
  5. 5. Darimont CT, Carlson SM, Kinnison MT, Paquet PC, Reimchen TE, Wilmers CC. Human predators outpace other agents of trait change in the wild. Proc Natl Acad Sci USA. 2009; 106: 952–954. pmid:19139415
  6. 6. Artelle KA. Is wildlife conservation policy based in science? Am Sci. 2018; 107: 38–45.
  7. 7. Opdam P, Wascher D. Climate change meets habitat fragmentation: Linking landscape and biogeographical scale levels in research and conservation. Biol Conserv. 2004; 117: 285–297.
  8. 8. Mora C, Metzger R, Rollo A, Myers RA. Experimental simulations about the effects of overexploitation and habitat fragmentation on populations facing environmental warming. Proc Royal Soc B. 2007; 274: 1023–1028. pmid:17284407
  9. 9. Myers N. Synergistic interactions and environment. Bioscience.1989; 39: 506.
  10. 10. Myers N. Environmental unknowns. Science. 1995; 269: 358–360. pmid:17841254
  11. 11. Johnson CN, Balmford A, Brook BW, Buettel JC, Galetti M, Guangchum L, et al. Biodiversity losses and conservation responses in the Anthropocene. Science. 2017; 356: 270–275. pmid:28428393
  12. 12. Singh SP. Chronic disturbance, a principal cause of environmental degradation in developing countries. Environ Conserv. 1998; 25: 1–2.
  13. 13. Martínez-Blancas A, Paz H, Salazar GA, Martorell C. Related plant species respond similarly to chronic anthropogenic disturbance: Implications for conservation decision-making. J Appl Ecol. 2018; 55: 1860–1870.
  14. 14. Tilman D, Clark M, Williams DR, Kimmel K, Polasky S, Packer C. Future threats to biodiversity and pathways to their prevention. Nature. 2018; 546: 73–81. pmid:28569796
  15. 15. Waldron A, Mooers AO, Miller DC, Nibbelink N, Redding DW, Kuhn TS, et al. Targeting global conservation funding to limit immediate biodiversity declines. Proc Natl Acad Sci USA. 2013; 10: 12144–12148. pmid:23818619
  16. 16. Tulloch AIT, Maloney RF, Joseph LN, Bennett JR, DiFonzo MMI, Probert WJM, et al. Effect of risk aversion on prioritizing conservation projects. Conserv Biol. 2015; 29: 513–524. pmid:25327837
  17. 17. Briggs SV. Priorities and paradigms: Directions in threatened species recovery. Conserv Lett. 2009; 2: 101–108.
  18. 18. Guillaumet A, Paxton EH. Evaluating community-level response to management actions across a diverse Hawaiian forest bird community. Ecol Appl. 2019; e01953. pmid:31206869
  19. 19. SARA–Species at Risk Act Policies. Canada. Ministry of the Environment. 2009;
  20. 20. Pryor SC, Scavia D, Downer C, Gaden M, Iverson L, Nordstrom R, et al. Ch. 18: Midwest. Climate Change Impacts in the United States. In: Melillo JM, Richmond TC, Yohe GW, editors. Climate Change Impacts in the United States: The Third National Climate Assessment. U.S. Washington DC: Global Change Research Program; 2014. pp. 418–440. Also
  21. 21. Tscharntke T, Klein AM, Kreuss A, Steffan-Dewenter I, Thies C. Landscape perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol Lett. 2005; 8: 857–874.
  22. 22. Hibbard K, Wilson T, Avery K, Harriss R, Newmark R, Rose , et al. Ch. 10: Energy, water, and land use. In: Melillo JM, Richmond TC, Yohe GW, editors. Climate Change Impacts in the United States: The Third National Climate Assessment. Washington DC: U.S. Global Change Research Program; 2014. pp. 257–285.
  23. 23. Farber S, Costanza R, Childers DL, Erickson DL, Gross K, Grove M, et al. Linking ecology and economics for ecosystem management. BioScience. 2006; 56: 121–133.
  24. 24. Kotowska MM, Leuschner C, Triadiati T, Meriem S, Hertel D. Quantifying above‐ and below-ground biomass carbon loss with forest conversion in tropical lowlands of Sumatra (Indonesia). Glob Change Biol. 2015; 2: 3620–3634. pmid:25980371
  25. 25. Askins RA. History of grassland birds in eastern North America. Stud Avian Biol. 1999; 31: 63–79. Available from:
  26. 26. Brennan LA, Kuvlesky AP. North American grassland birds: An unfolding conservation crisis? J Wildl Manage. 2005; 69: 1–13.
  27. 27. Sharp R, Wollscheid K-U. An overview of recreational hunting in North America, Europe and Australia. In: Dickson B B., Hutton J, Adams WM, editors. Recreational hunting, conservation and rural livelihoods: Science and practice. U.K.: Blackwell Publishing Ltd.; 2009. pp. 25–38.
  28. 28. Geist V, Mahoney SP, Organ JF. Why hunting has defined the North American model of wildlife conservation. Transactions of the North American Wildlife and Natural Resources Conference. 2001; 66: 175–185. Available from:
  29. 29. Artelle KA, Reynolds JD, Treves A, Walsh JC, Paquet PC, Darimont CT. Hallmarks of science missing from North American wildlife management. Sci Adv. 2018; 4: eaao0167. pmid:29532032
  30. 30. Allendorf FW, England PR, Luikart G, Ritchie PA, Ryman N. Genetic effects of harvest on wild animal populations. Trends Ecol Evol. 2008, 23: 327–337. pmid:18439706
  31. 31. Allendorf FW, Hard JJ. Human-induced evolution caused by unnatural selection through harvest of wild animals. Proc Nat Acad Sci USA. 2009; 106: 9987–9994. pmid:19528656
  32. 32. Cook CN, Sgrò CM. Aligning science and policy to achieve evolutionarily enlightened conservation. Conserv Biol. 2017; 31: 501–512. pmid:27862324
  33. 33. Allendorf FW. Genetics and the conservation of natural populations: Allozymes to genomes. Mol Ecol. 2017; 26: 420–430. pmid:27933683
  34. 34. Harris RB, Wall WA, Allendorf FW. Genetic consequences of hunting: What do we know and what should we do? Wildl Soc Bull. 2002; 30: 634–643. Available from:
  35. 35. Jorgensen CF, Powell LA, Lusk JJ, Bishop AA, Fontaine JJ. Assessing landscape constraints on species abundance: Does the neighborhood limit species response to local habitat conservation programs? PLoS ONE. 2014; 9(6): e99339. pmid:24918779
  36. 36. Mussmann SM, Douglas MR, Anthonysamy WJB, Davis MA, Simpson SA, Louis W., et al. Genetic rescue, the greater prairie chicken and the problem of conservation reliance in the Anthropocene. Royal Soc Open Sci. 2017; 4: 160736. pmid:28386428
  37. 37. Berkman LK. Landscape genetics of northern bobwhite and swamp rabbits in Illinois. Ph.D. Dissertation, Southern Illinois University, Carbondale. 2012.
  38. 38. Martín-López B, Montes C, Ramírez L, Benayas J. What drives policy decision-making related to species conservation? Biol Conserv. 2009; 142: 1370–1380.
  39. 39. Leopold A. Report on a game survey of the North Central States. Madison WI: Sporting Arms and Ammunition Manufacturer’s Institute; 1931.
  40. 40. Warner RE. Illinois pheasants: Population, ecology, distribution, and abundance, 1900–1978. Biological Notes. 1981; 115. Champaign: Illinois Natural History Survey.
  41. 41. McTaggart S. Ring-necked pheasant status report for 2016. Agriculture and grassland wildlife program note 13–3, Illinois Department of Natural Resources, Springfield IL.
  42. 42. Giesel JT, Brazeau D, Koppelman R, Shiver D. Ring-necked pheasant population genetic structure. J Wildl Manage. 1997; 61: 1332–1338.
  43. 43. Ridley MW. The Mating System of the Pheasant Phasianus colchicus. Ph.D. Dissertation, UK. Oxford University; Magdalen College. 1983.
  44. 44. Cheng HH, Levin I, Vallejo RL, Khatib H, Dodgson JB, Crittenden LB, Hillel J. Development of a genetic map of the chicken with markers of high utility. Poult. Sci. 1995; 74: 1855–1874. pmid:8614694
  45. 45. Segelbacher G, Paxton RJ, Steinbrü G, Trontelj P, Storch I. Characterization of microsatellites in Capercaillie Tetrao urogallus (Aves). Mol. Ecol. 2000; 9: 1934–1935. pmid:11091338
  46. 46. Baratti M, Alberti A, Groenen M, Veenendaal T, Fulgheri FD. Polymorphic microsatellites developed by cross-species amplifications in common pheasant breeds. Anim. Genet. 2001; 32, 222–225. pmid:11531703
  47. 47. Ferrero ME, González-Jara P, Blanco-Aguiar JA, Sánchez-Barbudo I, Dávila JA. Sixteen new polymorphic microsatellite markers isolated for red-legged partridge (Alectoris rufa) and related species. Mol. Ecol. Notes 2007; 7: 1349–1351.
  48. 48. Wang N, Chang J, Gu LY, Zhang ZW. Polymorphic microsatellites in the Reeves’s pheasant developed by cross-species amplification. Eur. J. Wildl. Res. 2009; 55: 627–629.
  49. 49. Bech N, Novoa C, Allienne JF, Boissier J. Transferability of microsatellite markers among economically and ecologically important galliform birds. Gen. Mol. Res. 2010; 9: 1121–1129. pmid:20568057
  50. 50. Gu L, Yang L, Wang N, Zhang Z-W. A panel of polymorphic microsatellites in the Blue Eared Pheasant (Crossoptilon auritum) developed by cross-species amplification. Chin. Birds 2012; 3: 103–107.
  51. 51. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. MicroChecker: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 2004; 4: 535–538.
  52. 52. Raymond M, Rousset F. GENEPOP (version 1.2)—Population genetics software for exact tests and ecumenicism. J. Hered. 1995; 86, 248–249.
  53. 53. Rousset F. GENEPOP ’ 007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 2008; 8: 103–106. pmid:21585727
  54. 54. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics 2012; 28: 2537–2539. pmid:22820204
  55. 55. Kalinowski ST. HP-RARE 1.0: a computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 2005; 5: 187–189.
  56. 56. Wilson G, Rannala B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 2003; 163: 1177–1191. pmid:12663554
  57. 57. Excoffier L, Lischer HEL. Arlequin suite ver. 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 2010; 10: 564–567. pmid:21565059
  58. 58. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics 2000; 155: 945–959. pmid:10835412
  59. 59. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol. Ecol. 2005; 14: 2611–2620. pmid:15969739
  60. 60. Earl DA, vonHoldt BM. Structure Harvester: a website and program for visualizing Structure output and implementing the Evanno method. Cons. Gen. Resour. 2011; 4: 359–361.
  61. 61. Janes JK, Miller JM, Dupuis JR, Malenfant RM, Gorrell JC, Cullingham CI, et al. The K = 2 conundrum. Mol. Ecol. 2017; 26: 3594–3602. pmid:28544181
  62. 62. Jackobsson M, Rosenberg NA. Clumpp: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 2007; 23: 1801–1806. pmid:17485429
  63. 63. Rosenberg NA. Distruct: A program for the graphical display of population structure. Mol. Ecol. Notes 2004; 4: 137–138.
  64. 64. Davis MA, Douglas MR, Webb CT, Collyer ML, Holycross AT, Painter CW, et al. Nowhere to go but up: Impacts of climate change on demography of a short-range endemic (Crotalus willardi obscurus) in the sky-islands of southwestern North America. PLoS ONE 2015; 10: e0131067. pmid:26114622
  65. 65. Piry S, Luikart G, Cornuet JM. Bottleneck: A computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 1999; 90: 502–503.
  66. 66. Cristescu R, Sherwin WB, Handasyde K, Cahill V, Cooper DW. Detecting bottlenecks using Bottleneck 1.2.02 in wild populations: The importance of the microsatellite structure. Cons. Gen. 2010; 11: 1043–1049.
  67. 67. Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Beër E, Robinson S, et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 2012; 21: 3403–3418. pmid:22646281
  68. 68. Do C, Waples RS, Peel D, MacBeth GM, Tillett BJ, Ovenden R. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 2014; 14: 209–214. pmid:23992227
  69. 69. Anderson EC, Thompson EA. A model-based method for identifying species hybrids using multilocus genetic data. Genetics 2002; 160: 1217–1229. pmid:11901135
  70. 70. Wringe BF, Stanley RE, Jeffrey NW, Anderson EC, Bradbury IR. Parallelnewhybrid: An R package for the parallelization of hybrid detection using NewHybrids. Mol. Ecol. Resour. 2017; 17: 91–95. pmid:27617417
  71. 71. Wang J. Triadic IBD coefficients and applications to estimating pairwise relatedness. Genet. Res. 2007; 89: 135–153. pmid:17894908
  72. 72. Wang J. A new likelihood estimator and its comparison with moment estimators of individual genome-wide diversity. Heredity 2011; 107: 433–443. pmid:21522168
  73. 73. Blouin MS. DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol. Evol. 2003; 18: 503–511.
  74. 74. Guillot G, Mortier F, Estoup A. Geneland: A computer package for landscape genetics. Mol. Ecol. Notes 2005; 5: 712–715.
  75. 75. Jombart T. Adegenet: An R package for the multivariate analysis of genetic markers. Bioinformatics 2008; 24: 1403–1405. pmid:18397895
  76. 76. Keenan K, McGinnity P, Cross TF, Crozier WW, Prodhöhl PA. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol Evol. 2013; 4: 782–788.
  77. 77. Berkman LK, Nielsen CK, Roy CL, Heist EJ. Population genetic structure among bobwhite in an agriculturally modified landscape. J Wildl Manage. 2013a; 77: 1472–1481.
  78. 78. Berkman LK, Nielsen CK, Roy CL, Heist EJ. Resistance is futile: Effects of landscape features on gene flow of the Northern Bobwhite. Conserv Genet. 2013b; 14: 323–332.
  79. 79. Pew J, Muir PH, Wang J, Frasier TR. related: An R package for analysing pairwise relatedness from codominant molecular markers. Mol Ecol Resour. 2015; 15: 557–561 pmid:25186958
  80. 80. Wang J. An estimator for pairwise relatedness using molecular markers. Genetics. 2002; 160: 1203–1215. pmid:11901134
  81. 81. Palsbøll PJ, Bérubé M, Allendorf FW. Identification of management units using population genetic data. Trends Ecol Evol. 2006; 22: 11–16. pmid:16982114
  82. 82. Kujala H, Moilanen A, Gordon A. Spatial characteristics of species distributions as drivers in conservation prioritization. Methods Ecol Evol. 2018; 9: 1121–1132.
  83. 83. Palkovacs EP, Moritsch MM, Contolini GM, Pelletier F. Ecology of harvest-driven changes and implications for ecosystem management. Front Ecol Environ. 2018; 16: 20–28.
  84. 84. Possingham HP, Andelman SJ, Burgman MA, Medellín RA, Master LL, Keith DA. Limits to the use of threatened species lists. Trends Ecol Evol. 2002; 17: 503–507.
  85. 85. Araki H, Cooper B, Blouin MS. Genetic effects of captive breeding cause a rapid, cumulative fitness decline in the wild. Science. 2007; 318: 100–103. pmid:17916734
  86. 86. Frankham R. Genetic adaptation to captivity in species conservation programs. Mol Ecol. 2008; 17: 325–333. pmid:18173504
  87. 87. Willoughby JR, Fernandez NB, Lamb MC, Ivy JA, Lacey RC, DeWoody A. The impacts of inbreeding, drift and selection on genetic diversity in captive breeding populations. Mol Ecol. 2015; 24: 98–110. pmid:25443807
  88. 88. Sokos CK, Birtsas PK, Efstahios PT. The aims of galliform release and choice of techniques. Wildl Biol. 2008; 14: 412–420.
  89. 89. Sæther B-E, Engen S, Solberg EJ. Effective size of harvested ungulate populations. Anim Conserv. 2009; 12: 488–495.
  90. 90. Shogren JF, Tschjirhart J, Anderson T, Ando A, Beissinger SR, Brookshire D, et al. Why economics matters for endangered species protection. Conserv Biol. 1999; 13: 1257–1261.
  91. 91. Conservation Measures Partnership. The open standards for the practice of conservation, Ver 3.0. 2013.
  92. 92. Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, et al. Solutions for a cultivated planet. 2011. Nature; 478: 337–342. pmid:21993620
  93. 93. IUCN—The IUCN Red List of Threatened Species. Version 2016–3. 2016.
  94. 94. Phalan BT. What have we learned from the land sparing-sharing model? Sustainability. 2018; 10: 1760.
  95. 95. Fischer J, Abson DJ, Bustic V, Chappell MJ, Ekroos J, Hanspach J, et al. Land sparing versus land sharing: Moving forward. Conserv Lett. 2014; 7: 149–157.
  96. 96. Phalan B, Green RE, Dicks LV, Dotta G, Feniuk C, Lamb A, et al. How can higher-yield farming help to spare nature? Science. 2016; 351: 450–451. pmid:26823413
  97. 97. Kuparinen A, Festa-Bianchet M. Harvest-induced evolution: Insights from aquatic and terrestrial systems. Philos Trans R Soc Lond B Biol Sci. 2017; 372: 20160036. pmid:27920381
  98. 98. Baudron F, Giller KE. Agriculture and nature: Trouble and strife? Biol Conserv. 2014; 170: 232–245.
  99. 99. Balmford B, Green RE, Onial M, Phalan P, Balmford A. How imperfect can land sparing be before land sharing is more favourable for wild species? J Appl Ecol. 2019; 56: 73–84.
  100. 100. Hellerstein D. The U.S. Conservation Reserve Program: The evolution of an enrollment mechanism. Land Use Policy. 2017; 63: 601–610.
  101. 101. Morefield PE, LeDuc SD, Clark CM, Iovanna R. Grasslands, wetlands, and agriculture: The fate of land expiring from the Conservation Reserve Program in the midwestern United States. Environ Res Lett. 2016; 11: 094005.
  102. 102. Cannon PG, Gilroy JJ, Tobias JA, Anderson A, Haugaasen T, Edwards DP. Land‐sparing agriculture sustains higher levels of avian functional diversity than land sharing. Glob Change Biol. 2019; 25: 1576–1590. pmid:30793430
  103. 103. Otto CRV, Zheng H, Gallant AL, Iovanna R, Carlson BL, Smart MD, et al. Past role and future outlook of the Conservation Reserve Program for supporting honey bees in the Great Plains. Proc Natl Acad Sci USA. 2018; 115: 7629–7634. pmid:29967144
  104. 104. Lute ML, Gillespie CR, Martin DR, Fontaine JJ. Landowner and practitioner perspectives on private land conservation programs. Soc Nat Resour. 2018; 31: 218–231.
  105. 105. Matthews TW, Taylor JS, Powell LA. Ring-necked Pheasant hens select managed Conservation Reserve Program grasslands for nesting and brood-rearing. J Wildl Manage. 2012; 76: 1653–1660;
  106. 106. Geaumont BA, Sedivec KK, Schauer CS. Ring-necked pheasant use of post−conservation preserve program lands. Rangeland Ecol Manag. 2017; 70: 569–575.
  107. 107. Hiller TL, Taylor JS, Lusk JJ, Powell LA, Tyre AJ. Evidence that the Conservation Reserve Program slowed population declines of Pheasants on a changing landscape in Nebraska, USA. Wildl Soc Bull. 2015; 39: 529–535.
  108. 108. Taylor JS, Rogenschutz TR, Clark WR. Pheasant responses to U.S. Cropland Conversion Programs: A review and recommendations. Wildl Soc Bull. 2018; 42: 184–194;
  109. 109. Knight AT, Cowling RM, Difford M, Campbell BM. Mapping human and social dimensions of conservation opportunity for the scheduling of conservation action on private land. Conserv Biol. 2010; 24:1348–58. pmid:20345404
  110. 110. Smith OM. Population responses of the Northern Bobwhite (Colinus virginianus) to land use changes in the agricultural landscapes of Ohio, USA. Popul Ecol. 2017; 59: 363–370.
  111. 111. Blank PJ. Northern Bobwhite response to Conservation Reserve Program habitat and landscape attributes. J Wildl Manage. 2013; 77: 68–74;
  112. 112. Greenfield KC, Burger LW Jr., Chamberlain MJ, Kurzejeski EW. Vegetation management practices on Conservation Reserve Program fields to improve northern bobwhite habitat quality. Wildl Soc Bull. 2002; 30: 527–538. Available from:
  113. 113. Vandever MW, Allen AW. Management of conservation reserve program grasslands to meet wildlife habitat objectives. U.S. Geological Survey Report 2015; 507.
  114. 114. Merrill MD, Chapman KA, Poiani KA, Winter B. Land-use patterns surrounding Greater Prairie Chicken leks in northwestern Minnesota. J Wildl Manage. 1999; 63: 189–198. Available from:
  115. 115. Adkins K, Roy CL, Andersen DE, Wright RG. Landscape‐scale greater prairie‐chicken habitat relations and the Conservation Reserve Program. J Wildl Manage. 2019; 83: 1415–1426;
  116. 116. Matthews TW, Tyre AJ, Taylor JS, Lusk JJ, Powell LA. Greater Prairie-Chicken nest success and habitat selection in southeastern Nebraska. J Wildl Manage. 2013; 77: 1202–1212;
  117. 117. Barral MP, Benayas JMR, Meli P, Maceira NO. Quantifying the impacts of ecological restoration on biodiversity and ecosystem services in agroecosystems: A global meta-analysis. Agr Ecosyst Environ. 2015; 202: 223–231.
  118. 118. Wilson EO. Half-earth: Our planet’s fight for life. New York. Liveright/W.W. Norton & Co.; 2016.
  119. 119. Sokos CK, Birtsas PK, Tsachalidis EP. The aims of galliforms release and choice of techniques. Wildl Biol. 2008; 14: 412–422.