DOI: 10.5061/dryad.p8cz8wb4r
title: Effects of grassland fragmentation and precipitation on Secretarybird (<i>Sagittarius serpentarius</i>) reproduction in the Serengeti ecosystem

description: Accurate demographic data are essential for understanding population
 dynamics and developing effective conservation strategies for threatened
 raptor species. This study provides the first quantitative assessment of
 how grassland fragmentation at landscape scale, driven by woody plant
 encroachment, and interannual variation in precipitation affect the
 secretarybird Sagittarius serpentarius reproduction within the Serengeti
 ecosystem in Tanzania. Through nest density estimation models based on
 nest survival and detection probabilities, and generalized additive mixed
 models, we estimated nesting success and productivity across three habitat
 types with different grassland fragmentation levels and rainfall regimes
 during the 2023 and 2024 breeding seasons. Nest density was highest in
 habitats with moderate to low fragmentation (50–&lt;90% grassland
 cover: 7.6 nests/100 km²), lower in highly fragmented habitats
 (&lt;50% grassland cover), and lowest in minimally fragmented ones
 (≥90% grassland cover). Nesting success increased significantly with
 grassland cover, from 18.9% in highly fragmented habitats – possibly due
 to greater influence of edge effects – to 71% in minimally fragmented
 ones. Productivity was lowest in highly fragmented habitats (0.8
 successful nests/100 km²), highest in moderately to low fragmented ones
 (2.5), and intermediate in minimally fragmented habitats (1.7). Wet-season
 rainfall emerged as dominant driver of nest abundance across all habitat
 types. Substantially higher precipitation in 2024 than 2023 correlated
 with a nearly three times higher nest abundance in 2024 compared to 2023,
 positively influencing productivity (2.2 vs. 0.8 successful nests/100
 km²). Additionally, lower long-term dry-season rainfall negatively
 affected nesting success. These findings highlight the secretarybird
 vulnerability to habitat and climate change, which may disrupt
 predator-prey dynamics and reduce nesting site quality and the number of
 breeding attempts. Conservation efforts in fragmented habitats should
 prioritize limiting the effects of woody plant encroachment and actively
 protecting nests to mitigate predation risks, thereby enhancing
 productivity and sustaining steady populations of this African savannah
 raptor.
description: # Effects of grassland fragmentation and precipitation on Secretarybird
 (*Sagittarius serpentarius*) reproduction in the Serengeti ecosystem
 Dataset DOI:
 [10.5061/dryad.p8cz8wb4r](https://doi.org/10.5061/dryad.p8cz8wb4r) ##
 Description of the data and file structure **Dataset Structure** The
 dataset consists of five distinct files. The first four are associated
 with the construction of the four sets of models presented in the
 reference article. Files ‘MODEL_2023_2024.csv’, ‘MODEL_2024.csv’, and
 ‘MODEL_All_nests.csv’ contain the data used to develop the time-to-event
 density estimator (TNDE) models. The files presented here are named in
 accordance with the model sets as described in the reference article. File
 ‘GAMMs.csv’ contains the dataset used to build the generalised additive
 mixed models. File ‘Grassland_patches_cover_plot.csv’ contains the data
 used to assess the correlation between patch density and the percentage of
 grassland cover at the landscape scale (plot level). A detailed
 description of the variables and analytical procedures is provided in the
 manuscript. **Key Variables** The variables in these files follow the
 naming conventions required to run the models in the *NestAbund v. 2.0*
 package (Péron et al. 2014), with the sole exception of the precipitation
 variables, which must be labelled as ‘Robel’ within each model, as
 specified by Péron et al. (2014). A detailed description of the
 precipitation variables is provided in the reference article. * The binary
 variable ‘Cattle’ represents the variable ‘tree species’ in the reference
 article, where the value 1 corresponds to *Balanites aegyptiaca* and the
 value 0 corresponds to *Vachellia tortilis*. * In File ‘MODEL_2023_2024’,
 the variable ‘Site’ indicates the sampling year: the value 1 corresponding
 to 2023 and the value 2 to 2024. In Files‘MODEL_2024’ and
 ‘MODEL_All_nests’ , the variable ‘Site’ denotes the specific habitat type,
 as described in the reference article. * NestID (numeric or text
 identifying nest) * FirstFound (week number when nest was first found) *
 LastPresent (week number when nest was last located) * Fate (fate= 0 in
 nest successfully produced young, 1 otherwise) * AgeFound (age of nest in
 weeks when first found) * AgeDay1 (age of nest on first week of survey) *
 Max Brood Size (maximum number of incubation weeks). * DS1021_mm (mean
 total precipitation during the dry season in mm). Long-term rainfall
 (2010-2021). * RS1021_mm (mean total precipitation during the rainy
 season, including the break between short and long rains). Long-term
 rainfall (2010-2021). * Pre1021_mm (mean annual precipitation). Long-term
 rainfall (2010-2021). * PDS_mm (total precipitation during the dry season
 preceding each breeding season in mm). June-October 2022 and June-October
 2023. * PRS_mm (total precipitation during the rainy season in the two
 study years, in mm). November 2022–May 2023 and November 2023–May 2024,
 including the break between short and long rains. * PNS_mm (total
 precipitation during the nesting period for each nest in mm, including the
 20 days preceding laying, the incubation period, and the 20 days
 post-hatching). * deltaPre (the difference between PRS and PDS, in mm). *
 Number of nests (total number of identified nests) * ID Plot (sampling
 plot identifier) * Year (year of sampling) * Habitat type TNDE models (1 =
 &lt;50% grassland cover; 2 = 50–&lt;90% grassland cover; 3 = ≥90%
 grassland cover) * Habitat type GAMMs (1 = grassland coverage &lt; 50%; 2
 = grassland coverage ≥ 50%) * Effort_km (length of transects surveyed
 within each plot) * Grassland_coverage (percentage of grassland within
 each plot) * Detectability_TNDE (nest detection probability estimated
 using Time-to-Event Density Estimator models, according to habitat type) *
 latitude_roads and longitude_roads (coordinates of the centroid of the
 road transect surveyed within each plot) * N_grassland_patches (number of
 grassland patches within each plot) **Reference** Péron, G., Walker, J.,
 Rotella, J., Hines, J. E. and Nichols, J. D. 2014. Estimating nest
 abundance while accounting for time‐to‐event processes and imperfect
 detection. – Ecology 95: 2548–2557.