Benefits of modelling abundance for rare species conservation: a case study with multiple birds across one million hectares

Aim: Many management programs that are based on the needs of rare or threatened species are ineffective because they fail to collect enough data to reliably estimate abundance and map distributions for their target species. Information that does exist for rare species is often based on presence-only data, because it is difficult to collect sufficient data on abundance for such species. We targeted ten rare bird species that were excluded from a recent study due to insufficient data. For these species, we aimed to (a) collect sufficient abundance data, (b) identify important locations and (c) estimate population sizes. Location: A large reserve system (~1M-ha) in south-eastern Australia. Methods: We undertook intensive field surveys, using repeat area searches of 660 independent 25-ha sites, totalling 2,640 hrs of surveys (2-hr surveys; two surveys per site). We used N-mixture models to estimate abundance whilst accounting for imperfect detection. Results: This survey effort returned eno..., This DOI comprises all the data required to run the N-Mixture models for each of the target species. It is also formatted for this purpose, with separate files for Bird observations, site covariates (e.g. fire-age, elevation) and observation covariates (e.g. wind, time of day). Details of data collection method are below: Site selection We randomly sampled 660 sites (25 ha each; 410 x 610 m), stratified according to fire-age (years since fire) and fire type (planned burn or wildfire). Through stratification we attempted to balance the dataset by maximising the number of sites in uncommon fire-age classes and in planned burns, which were scarce compared to wildfires. All sites were separated by > 1 km. Sites were arranged in ‘sets’ of three so that a single surveyor could complete one set per day. At least one site per set was within 1 km of the nearest track. Survey method From May to October 2022, we conducted 1,346 surveys (2,692 hrs covering 33,650 ha). To achieve this survey e..., , # Benefits of modelling abundance for rare species conservation: a case study with multiple birds across one million hectares [https://doi.org/10.5061/dryad.c59zw3rgg](https://doi.org/10.5061/dryad.c59zw3rgg) All the files required to run N-Mixture models for each target species in R ('unmarked' package; Fiske & Chandler 2011). Each species was analysed separately and each species required three different file types: Bird Observations (species counts per site per survey), Site Covariates (e.g. fire-age, elevation) and Observation covariates (e.g. wind, time of day). Details of each file type are provided below. ## Description of the data and file structure The data files are formatted for N-Mixture modelling i.e. separate files for Bird Observations, Site Covariates and Observation Covariates. ### BIRD\_OBSERVATIONS\_SPECIES\_NAME.csv files (separate file for each species): Each of these files includes the bird survey results for a single species. Each 25-hectare study site s...【研究目标】当前诸多基于珍稀濒危物种保护需求制定的管理方案收效甚微，究其原因在于其未能采集足够数据，以可靠估算目标物种的种群丰度并绘制其分布图谱。现有珍稀物种相关信息往往仅基于仅存在数据（presence-only data），这是因为此类物种的种群丰度数据极难充分采集。本研究选取了10种因数据不足被排除在近期研究之外的珍稀鸟类，针对这些物种，我们的研究目标为：(a) 采集足够的种群丰度数据；(b) 确定其重要栖息位点；(c) 估算种群规模。 【研究区域】澳大利亚东南部一处总面积约100万公顷的大型保护地系统。 【研究方法】我们开展了高强度野外调查，对660个独立的25公顷样地进行重复区域搜索，总调查时长共计2640小时（每个样地开展2次时长2小时的调查）。本研究采用N-mixture模型（N混合模型）估算种群丰度，并同时校正不完全检测偏差。 【研究结果】本次调查投入共获取[原文截断]，本数据集包含运行所有目标物种N-mixture模型所需的全部数据，且已针对该分析目的进行格式优化，包含鸟类观测、样地协变量（如火烧年限、海拔）及观测协变量（如风速、当日时段）三类独立文件。数据采集方法详情如下： ### 样地选取 我们依据火烧年限（火灾发生后的年数）与火烧类型（计划烧除或野火）进行分层，随机选取了660个25公顷的样地（每个样地尺寸为410×610米）。通过分层抽样，我们力求平衡数据集：最大化稀有火烧年限类别以及计划烧除样地的占比——相较于野火，计划烧除样地较为稀缺。所有样地间距均大于1公里。样地以“组”为单位排布，每组包含3个样地，以便1名调查员单日即可完成1组样地的调查。每组中至少有1个样地距离最近步道不超过1公里。 ### 调查方法 2022年5月至10月期间，我们共开展了1346次调查，总时长2692小时，覆盖面积达33650公顷。为达成该调查规模[原文截断]。 # 珍稀物种保护中种群丰度建模的效益：基于百万公顷尺度多鸟类物种的案例研究 [https://doi.org/10.5061/dryad.c59zw3rgg](https://doi.org/10.5061/dryad.c59zw3rgg) 本数据集包含在R语言中运行各目标物种N-mixture模型所需的全部文件（使用`unmarked`包；Fiske & Chandler 2011）。每个物种单独开展分析，且每个物种对应三类不同格式的文件：鸟类观测文件（单次调查中每个样地的物种计数数据）、样地协变量文件（如火烧年限、海拔）及观测协变量文件（如风速、当日时段）。各类文件的详细说明如下： ## 数据与文件结构说明 本数据集文件已针对N-mixture模型分析进行格式优化，分为鸟类观测、样地协变量与观测协变量三类独立文件。 ### `BIRD_OBSERVATIONS_SPECIES_NAME.csv`（每个物种对应一个独立文件） 此类文件包含单一物种的鸟类调查结果。每个25公顷研究样地的[原文截断]