Characterization of the variability and repeatability of gonadotropin-releasing hormone–induced luteinizing hormone responses in dairy cows within a synchronized ovulation protocol

peer-reviewedThe primary objective was to determine the variability and repeatability of GnRH-induced LH responses. The secondary objective was to evaluate the associations among plasma LH, FSH, estradiol (E2), and progesterone (P4) concentrations. One hundred lactating Holstein cows (35 primiparous, 65 multiparous) were initially subjected to a presynchronization protocol (d 0, PGF2α; d 3, GnRH) followed 7 d later by Ovsynch (d 10, GnRH; d 17, PGF2α; 56 h later, GnRH) and timed artificial insemination 16 h after the last GnRH. Blood samples were collected immediately before the GnRH injection of presynchronization and the second GnRH of Ovsynch to determine plasma concentrations of LH, FSH, and P4. A second blood sample was collected 2 h after each of the above GnRH injections to determine GnRH-induced LH and FSH concentrations. Plasma concentrations of E2 were also determined in samples collected immediately before the second GnRH of Ovsynch. Cows that (1) had higher LH concentrations at 0 h than at 2 h after GnRH, (2) showed an ongoing spontaneous LH surge, (3) did not respond to GnRH, and (4) had P4 ≥ 0.5 ng/mL at GnRH of presynchronization and the second GnRH of Ovsynch were excluded from the analysis. The variability (coefficient of variation) and repeatability [between animal variance/(within animal variance + between animal variance)] of GnRH-induced LH response were determined from samples collected 2 h after the GnRH of presynchronization and the second GnRH of Ovsynch. The associations among plasma LH, FSH, E2, and P4 were determined at the second GnRH of Ovsynch. Mean (±SEM) LH concentrations before GnRH were 0.5 ± 0.04 and 0.6 ± 0.03 ng/mL, whereas mean LH concentrations 2 h after GnRH were 9.8 ± 1.0 and 12.1 ± 0.8 ng/mL at GnRH of presynchronization and the second GnRH of Ovsynch, respectively. The variability of GnRH-induced LH was 76.1 and 52.1% at GnRH of presynchronization and the second GnRH of Ovsynch, respectively. The repeatability estimate for GnRH-induced LH concentration between GnRH of presynchronization and Ovsynch assessments was 0.10. Plasma concentrations of LH were positively associated with FSH and E2 (r = 0.61 and 0.30, respectively) and negatively associated with P4 (r = −0.46) at the second GnRH of Ovsynch. In summary, GnRH-induced LH responses were highly variable and unrepeatable, and LH concentrations were positively associated with FSH and E2 and negatively associated with P4

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge, it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost