Gabriela Cruz-Piñón

Dr. Gabriela Cruz-Piñón

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 January 2020 in MethodsX
Verified
Journal Article
Open access
HMHem Nalini Morzaria-Luna
Hem Nalini Morzaria-Luna
PTPeggy Turk-Boyer
Peggy Turk-Boyer
JHJosé Manuel Dorantes Hernández
José Manuel Dorantes Hernández
EPElia Polanco-Mizquez
Elia Polanco-Mizquez
CDCaroline Downton-Hoffmann
Caroline Downton-Hoffmann
AMAdrian Munguia-Vega
Adrian Munguia-Vega
A management approach was developed that combined spatial and non-spatial tools to inform a Coastal and Marine Spatial Planning Process (CMSP) in the Puerto Peñasco-Puerto Lobos Coastal Corridor, Northern Gulf of California, Sonora, Mexico. Four fisheries management tools were applied with an emphasis on ecosystem level management for eleven small-scale fisheries. Two spatial management tools, using a spatial prioritization approach, were combined with a permit regularization process, a non-spatial quota prioritization, and a tradeoff analysis in a novel way:

1 October 2025 in Forests
Verified
Journal Article
Open access
PDF file
JPJorge Palomo-Kumul
Jorge Palomo-Kumul
MVMirna Valdez-Hernández
Mirna Valdez-Hernández
GIGerald Alexander Islebe
Gerald Alexander Islebe
Edith OsorioEdith Osorio
Edith Osorio
GCGabriela Cruz-Piñón
Gabriela Cruz-Piñón
RJRaul Juárez-Aguirre
Raul Juárez-Aguirre
Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change.

8 August 2023 in Human Ecology
Verified
Journal Article
Closed access
PDF file
HMHem Nalini Morzaria-Luna
Hem Nalini Morzaria-Luna
MUMabilia Urquidi
Mabilia Urquidi
GCGabriela Cruz-Piñón
Gabriela Cruz-Piñón
JHJosé Manuel Dorantes Hernández
José Manuel Dorantes Hernández
PJPaloma A. Valdivia Jiménez
Paloma A. Valdivia Jiménez
IMIlse A. Martínez-Candelas
Ilse A. Martínez-Candelas

23 February 2026 in Fisheries Research
Verified
Journal Article
Closed access
RCRicardo Cavieses-Nuñez
Ricardo Cavieses-Nuñez
QLQingyuan Lu
Qingyuan Lu
HMHem Nalini Morzaria-Luna
Hem Nalini Morzaria-Luna
PMPratyush Mallick
Pratyush Mallick
SKSoundar Kumara
Soundar Kumara
KLKaren Lopez-Olmedo
Karen Lopez-Olmedo

1 April 2025 in bioRxiv
Verified
Preprint
Open access
PDF file
RCR. Cavieses-Nuñez
R. Cavieses-Nuñez
QLQ. Lu
Q. Lu
HMH.N. Morzaria-Luna
H.N. Morzaria-Luna
PMP. Mallick
P. Mallick
SKS. Kumara
S. Kumara
KLK. Lopez-Olmedo
K. Lopez-Olmedo
Small-scale, multispecific fisheries in the Gulf of California face significant challenges including limited species-specific catch data, uncertainty about climate change impacts, and insufficient biological information needed for traditional deterministic models. These knowledge gaps hamper efforts to forecast future conditions and develop appropriate management strategies accurately. The complexity of these multi-species fisheries, combined with data scarcity for many target species, creates substantial barriers to quantifying and addressing climate vulnerability. Deep learning approaches offer a promising alternative by leveraging available data to identify patterns and project trends despite these limitations, providing valuable insights for fisheries management in data-poor contexts. Here, we apply a Mixture of Expert, a deep learning forecasting models for small-scale, multi-specific fisheries in the Gulf of California under future climate change scenarios. Results show varied responses across marine habitats, with reef and benthic fish projected to experience substantial declines (-12.46% and -9.37%) during the 2050s-2060s, followed by recovery in the 2070s-2080s. Economic implications are significant, with reef fish facing projected losses of $1.2 million by the 2050s before recovering by the 2080s. Shapley Additive Explanations (SHAP) analysis was applied to evaluate the importance of features for each predictive model, the analysis revealed the effects of the temperature in different depths for each fishery, and the sensitive analysis pointed to the magnitude of the effect. Our findings suggest that climate impacts will not be uniform across the Gulf, necessitating region-specific management approaches and highlighting the value of maintaining diverse fishing portfolios to enhance resilience against climate-driven changes.