AbstractNatal Habitat Preference Induction (NHPI) plays a significant role in shaping settlement decisions in dispersive animals. Despite its importance, limited research has explored how NHPI varies during natal dispersal phases and across different types of natal habitats. In this study, we examined NHPI in 77 GPS-tagged juvenile red kites (Milvus milvus) originating from different natal habitats along an elevational gradient in Switzerland.

摘要出生栖息地偏好诱导（NHPI）在塑造分散动物的定居决策中起着重要作用。。在这项研究中，我们检测了来自瑞士海拔梯度不同出生栖息地的77只带有GPS标签的幼年红风筝（Milvus Milvus）的NHPI。

We applied individual-based step selection analysis to investigate habitat selection from independence to settlement. We found that during the prospecting phase, individuals predominantly selected habitats similar to their natal environments. However, this pattern changed in the settlement phase: individuals fledged from habitats at higher elevations or closer to urban areas mostly avoided similar habitats (negative NHPI), while those from areas with more farmlands or pastures (combined with forests) showed a preference for similar habitats (positive NHPI).

我们应用基于个体的步骤选择分析来研究从独立到定居的栖息地选择。我们发现，在勘探阶段，个体主要选择与其出生环境相似的栖息地。然而，这种模式在定居阶段发生了变化：来自海拔较高或靠近城市地区的栖息地的个体大多避免了类似的栖息地（负NHPI），而来自农田或牧场（与森林结合）较多的地区的个体则表现出对类似栖息地的偏好（正NHPI）。

Moreover, the magnitude and individual variation in NHPI differed depending on the natal habitat types from which individuals originated. These findings highlight that strength, direction, and individual variation in NHPI differ between natal habitat types and dispersal phases. Natal habitats therefore can have pervasive legacy effects on subsequent habitat selection, likely affecting population and range dynamics..

此外，NHPI的大小和个体差异取决于个体起源的出生栖息地类型。这些发现突出表明，NHPI的强度，方向和个体变异在出生栖息地类型和扩散阶段之间有所不同。因此，出生栖息地可能会对随后的栖息地选择产生普遍的遗留影响，可能会影响种群和范围动态。。

IntroductionNatal dispersal consists of three phases: departure from the birth location, prospecting for suitable habitats, and final settlement at a breeding site1,2,3. Natal dispersal is central in shaping individual life-histories4 and intersects with developmental and behavioural factors affecting ecological and evolutionary outcomes5,6,7.

引言出生扩散包括三个阶段：离开出生地点，寻找合适的栖息地，以及在繁殖地最终定居1,2,3。出生扩散是塑造个体生活史的核心4，并与影响生态和进化结果的发展和行为因素相交5,6,7。

Early-life experiences, particularly through mechanisms like ‘ecological imprinting’, have long been recognized to play a critical role in shaping dispersal patterns8. Imprinting also represents an important mechanism shaping individual preferences in food, habitat, mate, or host selection (reviewed in9), extending its impact beyond individual ecology to broader evolutionary processes, such as reproductive isolation and speciation5,10,11.The concept of Natal Habitat Preference Induction (NHPI) generalizes processes of habitat imprinting and describes how early experiences in the natal habitat influence the likelihood of selecting a similar environment during later life-history stages12.

早期的生活经历，特别是通过“生态印记”等机制，长期以来被认为在塑造扩散模式方面发挥着关键作用8。印记也代表了塑造个体在食物，栖息地，配偶或宿主选择中偏好的重要机制（综述见9），将其影响扩展到个体生态学以外的更广泛的进化过程，如生殖隔离和物种形成5,10,11。出生栖息地偏好诱导（NHPI）的概念概括了栖息地印记的过程，并描述了出生栖息地的早期经历如何影响在以后的生活史阶段选择类似环境的可能性12。

Experimental support for NHPI has been found across a range of animal taxa, primarily in controlled laboratory conditions (reviewed in12, but see two studies that haven’t found any effects13,14). In free-living vertebrates, the influence of natal habitat on subsequent settlement habitat preferences has been demonstrated in mammals such as rodents15,16,17,18, canids19,20,21,22, and ungulates23,24, as well as in reptiles25 and fishes13.

NHPI的实验支持已经在一系列动物分类群中发现，主要是在受控的实验室条件下（12年综述，但见两项尚未发现任何效果的研究13,14）。在自由生活的脊椎动物中，出生栖息地对随后定居栖息地偏好的影响已经在哺乳动物中得到证实，如啮齿动物15,16,17,18，犬科动物19,20,21,22和有蹄类动物23,24，以及爬行动物25和鱼类13。

More recent studies have also strengthened the evidence for NHPI in birds26,27,28,29,30,31,32.While the occurrence of NHPI is widely recognized, there is a gap in understanding its intraspecific variation. This variation includes individual differences, changes across life stages, and spatial differences related to na.

。这种变化包括个体差异，生命阶段的变化以及与na相关的空间差异。

(1)

(1)

With xo and y0 representing the values of PC1 and PC2 for the natal area, while xi and yi denote the values of PC1 and PC2 for each of the i-step location (both observed and available). Lower dissimilarity values therefore indicate habitats more similar to the natal habitat.Distance to natal nestTo account for the tendency of individuals to select areas closer to their natal area, we also extracted the geographic distance to the natal nest (distance, in km) for each observed and available step (following42) and used this distance as covariate in the models.

xo和y0表示出生区域的PC1和PC2值，而xi和yi表示每个i步位置（观察到的和可用的）的PC1和PC2值。因此，较低的不相似性值表明栖息地与出生栖息地更相似。。

The two covariates, dissimilarity and distance, were not strongly correlated (Pearson’s correlation < 0.6).Individual-year modelling of NHPITo investigate the impact of the dissimilarity on step selection, while controlling for the distance to the natal nest, we first standardized and centred both covariates60.

相异性和距离这两个协变量没有强相关性（Pearson相关系数<0.6）。NHPITo的个体年建模研究了差异对步骤选择的影响，在控制到出生巢的距离的同时，我们首先对两个协变量进行了标准化和集中60。

We then fitted conditional logistic regressions to each individual-year separately to specifically assess individual variation39,41. This analysis was conducted using the IndRSA package which allows for individual-iSSA and the extraction of individual-year coefficients for both covariates39. We hereafter refer to NHPI as the negative of the dissimilarity coefficients.Inter-individual variation in NHPITo separate population averages from inter-individual variation in NHPI coefficients, three metrics were calculated for each individual-year i (equations recently formulated by39):$$\:Average=\overline{x}=\frac{1}{n}{\sum\:}_{i=1}^{n}{x}_{i}$$.

然后，我们将条件逻辑回归分别拟合到每个年份，以专门评估个体变量39,41。这项分析是使用IndRSA软件包进行的，该软件包允许单个iSSA和提取两个协变量的单个年份系数39。以下我们将NHPI称为不相似系数的负值。NHPI的个体间变异为了将人口平均数与NHPI系数的个体间变异分开，为每个年份i计算了三个指标（最近由39公式化的方程式）：$$：平均值=\上划线{x}=\分数{1}{n}{\总和：}{ui=1}^{n}{x}_{i} $$。

(2)

(2)

NHPI average (Eq. 2) represents the population average of individual-year NHPI coefficients xi with a total of n individual-years (n = 216 + 129 = 345).$$\:Specialization=\:\frac{1}{n}\:\sum\:_{i=1}^{n}Abs\left({x}_{i}\right)$$

NHPI平均值（等式2）表示个人年份NHPI系数xi的人口平均值，总共n个个人年份（n=216++129=345）。$$\：专门化=\：\frac{1}{n}\：\sum\：\ui=1}^{n}Abs\左({x}_{i} \右）$$

(3)

(3)

NHPI specialization (Eq. 3) represents the average of the magnitude of the individual-year NHPI coefficients xi (independent of the direction), and it is strictly positive, with higher values indicating higher individual specialization. This metric can be particularly informative when the overall population average is equal to zero and/or to capture a bimodal (or more complex) pattern in a population coefficients39.$$\:Heterogeneity=\:\sqrt{{\frac{1}{n-1}\:\sum\:_{i=1}^{n}\left({x}_{i}-\:\stackrel{-}{x}\right)}^{2}}$$.

NHPI专业化（方程3）表示单个年份NHPI系数xi（与方向无关）大小的平均值，它是严格正的，数值越高表示个体专业化程度越高。当总体总体平均值等于零和/或在总体系数39中捕获双峰（或更复杂）模式时，此度量可能特别有用。$$\：异质性=\：\ sqrt{\ frac{1}{n-1}\：\ sum\：\ ui=1}^{n}\ left({x}_{i}-\：\ stackrel{-}{x}\右）}^{2}}$$。

(4)

(4)

NHPI heterogeneity (Eq. 4) corresponds to the standard deviation of individual-year NHPI coefficients xi with higher values indicating higher inter-individual heterogeneity.Intra-individual variation in NHPINHPI change from prospecting to settlementTo quantify intra-individual variation i.e., how an individual’s NHPI coefficients change from the prospecting to settlement, we applied a consistency and reversal metrics39 to the dispersal phases prospecting p and settlement s (with the same notation as above):$$\:Consistency=\:\frac{1}{n}\:\sum\:_{i=1}^{n}Abs({x}_{i,p}-\:{x}_{i,\:s})$$.

NHPI异质性（方程4）对应于个体年份NHPI系数xi的标准偏差，较高的值表明个体间异质性较高。NHPINHPI的个体内变异从勘探到定居的变化为了量化个体内变异，即个体的NHPI系数如何从勘探到定居的变化，我们将一致性和反转度量39应用于勘探p和定居s的扩散阶段（与上述符号相同）：$$\：一致性=\：\ frac{1}{n}\：\ sum\：\ ui=1}^{n}Abs（笑声）({x}_{i,p}-\{x}_{i，\：s}）$$。

(5)

(5)

$$\:Reversal=\:\frac{1}{n}\:\sum\:_{i=1}^{n}\left\{\begin{array}{c}1\:if\:sign\left({x}_{i,p}\right)\ne\:\:sign\left({x}_{i,s}\right)\:\\\:0\:if\:sign\left({x}_{i,p}\right)=\:sign\left({x}_{i,s}\right)\end{array}\right.$$

$$\：反转=\：\ frac{1}{n}\：\ sum\：\ ui=1}^{n}\ left\{\ begin{array}{c}1\：if \：符号\左({x}_{i，p}\右）\ne\：\：符号\左({x}_{i，s}\右）\：\ \：0 \：if \：符号\左({x}_{i，p}\右）=\：符号\左({x}_{i，s}\右）\end{array}\右$$

(6)

(6)

This involved re-running the step selection models with data only from the final year of prospecting with the first year of settlement per individual (n = 77). We specifically modelled the interaction of dispersal phase (prospecting vs. settlement) with both covariates (distance and dissimilarity).

这涉及重新运行步骤选择模型，数据仅来自勘探的最后一年，每个人的第一年结算（n=77）。我们专门模拟了扩散阶段（勘探与沉降）与两个协变量（距离和差异）的相互作用。

Following39, only the interaction terms were tested, providing a separate coefficient for each phase. These interaction coefficients were then used to compute the consistency and reversal metrics, quantifying intra-individual shifts in habitat preferences between the two phases.NHPI consistency represents the average of the absolute differences between the coefficients of the two dispersal phases across all n individuals (n = 77, Eq. 5).

在39之后，仅测试了相互作用项，为每个阶段提供了单独的系数。然后使用这些相互作用系数来计算一致性和逆转指标，量化两个阶段之间栖息地偏好的个体内变化。NHPI一致性表示所有n个个体的两个扩散阶段的系数之间的绝对差异的平均值（n=77，等式5）。

Consistency is an inverse measure as values closer to zero indicate a higher consistency between prospecting and settlement (indicating minimal difference in the coefficients). Conversely, higher values indicate greater changes in the coefficients between the two phases, implying lower consistency. NHPI reversal (Eq. 6) represents the proportion of individuals showing reversal preference between the two dispersal phases, switching from positive to negative (or vice versa).

一致性是一种反向度量，因为接近零的值表示勘探和沉降之间的一致性更高（表明系数差异最小）。相反，较高的值表示两个阶段之间的系数变化较大，这意味着一致性较低。NHPI逆转（方程6）表示在两个扩散阶段之间表现出逆转偏好的个体比例，从正面转换为负面（反之亦然）。

It is bounded between 0 and 1 with high values indicating an increased proportion of reversal.Annual variation in NHPI during prospectingFinally, to assess interannual variation (consistency across consecutive years) for each individual during the prospecting phase (which can last several years) we customized the consistency metric by incorporating ‘year’ as an interactive factor, with both covariates, in each individual-iSSA and calculated the metric as follows:$$\:Interranual\:Consistency\:=\:\frac{1}{n}\sum\:_{i=1}^{n}\left[\frac{1}{{m}_{i}-1}\sum\:_{j=1}.

它在0和1之间有界，高值表示反转比例增加。勘探期间NHPI的年变化最后，为了评估勘探阶段（可能持续几年）每个人的年际变化（连续几年的一致性），我们通过在每个iSSA中将“年”作为一个交互因子（带有两个协变量）来定制一致性度量，并按如下方式计算度量：$$\：年间\：一致性\：=：\ frac{1}{n}\ sum\：\ui=1}^{n}\ left[\ frac{1}{{m}_{i}-1}\和\：{j=1}。

(7)

(7)

Using the same notation as before, here m refers to the total number of years for each individual i. The first summation calculates the absolute differences for each consecutive year j for each individual i. The term mi – 1 normalizes these differences across individuals. The second summation thus provides an average measure of temporal NHPI consistency across n individuals (n = 77).Propagation of uncertainties, models validation and residuals spatial autocorrelationTo estimate uncertainties within the five NHPI metrics (average, specialization, heterogeneity, consistency, and reversal), we applied a data augmentation method using the IndRSA package.

使用与之前相同的符号，这里m是指每个个体i的总年数。第一次求和计算每个个体i的每个连续年份j的绝对差异。术语mi-1标准化了个体之间的这些差异。因此，第二次求和提供了n个个体的时间NHPI一致性的平均度量（n=77）。不确定性的传播，模型验证和残差空间自相关为了估计五个NHPI指标（平均值，专业化，异质性，一致性和反转）内的不确定性，我们使用IndRSA软件包应用了数据增强方法。

This method involved the generation of 1000 simulated replicates of each individual coefficient from each covariate for each model, drawn from a normal distribution centred around the given coefficient values. The chosen standard deviation was the standard error corresponding to these coefficients (see39 for detailed explanations).

该方法涉及从每个模型的每个协变量中生成每个系数的1000个模拟重复，这些重复来自以给定系数值为中心的正态分布。。

For each set of individual simulated coefficients, we then calculated the NHPI metrics, producing 1000 values for each metric39. This method, similar to bootstrapping, minimizes the effects of autocorrelation by treating each individual as independent, ensuring more reliable errors estimations41. We then assessed model performance using k-fold cross-validation using the indRSA package39, withholding 20% of the data, assessing model fit, and repeating the process five times for each individual model.

对于每组单独的模拟系数，我们然后计算NHPI指标，为每个指标产生1000个值39。这种方法类似于自举，通过将每个个体视为独立的个体来最小化自相关的影响，从而确保更可靠的误差估计41。然后，我们使用indRSA软件包39使用k倍交叉验证评估模型性能，扣留20%的数据，评估模型拟合，并对每个模型重复该过程五次。

Cross-validation provided relatively high Spearman rank correlations with averages ranging from 0.63 to 0.75 indicating good model performances61,62. Finally, the absence of strong spatial autocorrelation in the residuals of each model was checked visually using variograms using the ge.

交叉验证提供了相对较高的Spearman等级相关性，平均值范围为0.63至0.75，表明模型性能良好61,62。最后，使用ge使用变异函数目视检查每个模型的残差中不存在强空间自相关。

Data availability

数据可用性

The GPS datasets analysed in the current study are available in Movebank (www.movebank.org), under the project named “Milvusmilvus_Milsar_SOI_final” (ID 1356790386) and 'Milvusmilvus_GSM_SOI' (ID 230545451). Data and codes to reproduce the analyses are made available through the Open Repository and Archive from vogelwarte.ch (https://doi.org/10.5281/zenodo.13970545)..

本研究中分析的GPS数据集可在Movebank（www.Movebank.org）上获得，该项目名为“Milvusmilsar\u SOI\u final”（ID 1356790386）和“Milvusmilv\u GSM\u SOI”（ID 230545451）。重现分析的数据和代码可通过vogelwarte.ch的开放存储库和存档获得(https://doi.org/10.5281/zenodo.13970545)。。

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Download referencesAcknowledgementsWe thank all the fieldworkers who participated in the project. We also thank Y-C. Chan and S. Oppel from the Red Kite team for fruitful discussions, L. Gross for the help with detecting territories, and R. Carrizo for the help with the equations. We would like to provide special thanks to Guillaume Bastille-Rousseau for his help with the IndRSA package.

下载参考文献致谢我们感谢所有参与该项目的现场工作人员。我们还要感谢红风筝团队的Y-C.Chan和S.Oppel进行了富有成效的讨论，感谢L.Gross在探测领土方面的帮助，感谢R.Carrizo在方程式方面的帮助。我们要特别感谢纪尧姆·巴士底·卢梭（GuillaumeBastilleRousseau）为IndRSA方案提供的帮助。

This work was funded by the Swiss National Science Foundation (Grant 310030 212469).Author informationAuthors and AffiliationsSwiss Ornithological Institute, Sempach, SwitzerlandFlorian Orgeret, Urs G. Kormann, Benedetta Catitti, Stephanie Witczak, Valentijn S. van Bergen, Patrick Scherler & Martin U.

这项工作由瑞士国家科学基金会（Grant 310030212469）资助。作者信息作者和附属机构威斯康星州鸟类研究所，塞姆帕克，瑞士弗洛里安·奥尔盖雷特，乌尔斯·G·科尔曼，贝内德塔·卡蒂蒂，斯蒂芬妮·维特扎克，瓦伦蒂金·S·范·伯根，帕特里克·舍勒和马丁·U。

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PubMed Google ScholarContributionsFO, UK and MG conceived and designed the study. PS, VVB, BC, SW collected data. MG, UK and PS secured long-term funding. FO performed the analyses and wrote the initial version of the manuscript. All authors contributed to the writing and gave their final approval for publication.Corresponding authorCorrespondence to.

PubMed Google ScholarContributionsFO，UK和MG构思并设计了这项研究。PS，VVB，BC，SW收集的数据。MG、英国和PS获得了长期资金。FO进行了分析并撰写了手稿的初始版本。所有作者都为写作做出了贡献，并最终批准了出版。对应作者对应。

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Reprints and permissionsAbout this articleCite this articleOrgeret, F., Kormann, U.G., Catitti, B. et al. Imprinted habitat selection varies across dispersal phases in a raptor species.

转载和许可本文引用本文Orgeret，F.，Kormann，U.G.，Catitti，B。等人。猛禽物种的印迹栖息地选择因扩散阶段而异。

Sci Rep 14, 26656 (2024). https://doi.org/10.1038/s41598-024-75815-1Download citationReceived: 11 April 2024Accepted: 08 October 2024Published: 04 November 2024DOI: https://doi.org/10.1038/s41598-024-75815-1Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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KeywordsMovement ecologyEarly-lifeProspectionGPS telemetryConditional logistic regressionIndividual specialization

关键词运动生态学年度寿命展望GPS遥测条件逻辑回归个体专业化

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Behavioural ecologyBiogeographyEcological modellingEvolutionary ecologyPopulation dynamicsUrban ecology

行为生态学生物地理学生态学模型进化生态学人口动态城市生态学