36) Dietzel, K.; Valle, D.; Fierer, N.; U’Ren, J. M.; Barberan, A. In press. Geographical distribution of fungal plant pathogens in dust across the United States. Frontiers in Ecology and Evolution.
35) Valle, D.; Kaplan, D. 2019. Quantifying the impacts of dams on riverine hydrology under non-stationary conditions using incomplete data and Gaussian copula models. Science of the Total Environment 677: 599-611.
34) Amratia, P.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Millar, J.; Oppong, S.; Koram, K.; Valle, D. 2019. Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana. Malaria Journal 18:81
33) Valle, D.; Toh, K.; Millar, J. 2019. Rapid prototyping of decision support tools for conservation. Conservation Biology
32) Valle, D.; Toh, K.; Laporta, G.; Zhao, Q. 2019. Ordinal regression models for zero-inflated and/or over-dispersed count data. Scientific Reports, 9:3046
31) Albuquerque, P.; Valle, D.; Li, D. 2019. Bayesian LDA for mixed-membership clustering analysis: the Rlda package. Knowledge-Based Systems 163: 988-995
30) Hyde, J.; Bohlman, S.; Valle, D. 2018. Transmission lines are an under-acknowledged conservation threat to the Brazilian Amazon. Biological Conservation 228: 343-356.
29) Millar, J.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Amratia, P.; Koram, K.; Oppong, S.; Valle, D. 2018. Detecting risk factors for residual malaria using Bayesian Model Averaging. Malaria Journal, 17:343. doi.org/10.1186/s12936-018-2491-2
28) Simmons, C. S.; Famolare, L.; Macedo, M.; Walker, R. T.; Coe, M.; Scheffers, B.; Arima, E.; Munoz-Carpena, R.; Valle, D.; Fraisee, C.; Moorecroft, P.; Diniz, M.; Diniz, M.; Szlafsztein, C.; Pereira, R.; Ruiz, C.; Rocha, G.; Juhn, D.; Lopes, L. O. C.; Waylen, M.; Antunes, A. 2018. Science in support of Amazonian conservation in the 21st century: the case of Brazil. Biotropica 50(6): 850-858.
27) Valle, D.; Albuquerque, P.; Barberan, A.; Zhao, Q.; Fletcher Jr, R. J. 2018. Extending the Latent Dirichlet Allocation model to presence/absence data: a case study on North American breeding birds and biogeographic shifts expected from climate change. Global Change Biology 1-13.
26) Robertson, E. P.; Fletcher Jr., R. J.; Cattau, C. E.; Udell, B. J.; Reichert, B. E.; Austin, J. D.; Valle, D. 2018. Isolating the roles of movement and reproduction on effective connectivity alters conservation priorities for an endangered bird. Proceedings of the National Academy of Sciences of the United States of America.
25) Chaves, W. A.; Valle, D.; Monroe, M. C.; Wilkie, D. S.; Sieving, K. E.; Sadowsky, B. 2017. Changing wild meat consumption: an experiment in the central Amazon, Brazil. Conservation Letters. doi: 10.1111/conl.12391
24) Valle, D.; Cvetojevic, S.; Robertson, E.; Reichert, B. E.; Hochmair, H. H.; Fletcher Jr., R. J. 2017. Individual movement strategies revealed through novel clustering of emergent movement patterns. Scientific Reports 7, 44052. DOI: 10.1038/srep44052
23) Baiser, B.; Valle, D.; Zelazny, Z.; Burleigh, G. 2017. Non-random patterns of invasion and extinction reduce phylogenetic diversity in island bird assemblages. Ecography 40:001-014.
22) Seva, A. P.; Mao, L.; Ovallos, F. G.; Tucker-Lima, J. M.; Valle, D. 2017. Risk analysis and prediction of visceral leishmaniasis dispersion in Sao Paulo State, Brazil. PLOS Neglected Tropical Diseases 11(2): e0005353
21) Tucker-Lima, J.; Vittor, A.; Rifai, S.; Valle, D. 2017. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philosophical Transaction of the Royal Society B: Biological Sciences 372:20160125
20) Valle, D.; Millar, J.; Amratia, P. 2016. Spatial heterogeneity can undermine the effectiveness of country-wide test and treat policy for malaria: a case study from Burkina Faso. Malaria Journal, 15:513.
19) Tucker-Lima, J.; Valle, D.; Moretto, E.; Pulice, S.; Zuca, N.; Roquetti, D.; Beduschi, L.; Praia, A.; Okamoto, C.; Carvalhaes, V.; Branco, E.; Barbezani, B.; Labandera, E.; Timpe, K.; Kaplan, D. 2016. A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon. Scientific Data. 3:160071 doi: 10.1038/sdata.2016.71
18) Valle, D.; Tucker Lima, J. M.; Millar, J.; Amratia, P.; Haque, U. 2015. Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches. Malaria Journal, 14:434.
17) Valle, D.; Tucker Lima, J. M. 2014. Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model. Malaria Journal, 13:443.
16) Valle, D.; Baiser, B.; Woodall, C.; Chazdon, R. 2014. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method. Ecology Letters, 17:1591-1601
15) Valle, D. 2014. Response to the critique by Hahn et al. entitled “Conservation and Malaria in the Brazilian Amazon”. Am. J. Trop. Med. Hyg., 90(4), pp. 595-596
14) Valle, D.; Clark, J. 2013. Improving the Modeling of Disease Data from the Government Surveillance System: a Case Study on Malaria in the Brazilian Amazon. PLOS Computational Biology 9(11): e1003312.
13) Valle, D.; Zaitchik, B.; Feingold, B.; Spangler, K.; Pan, W. 2013. Abundance of Water Bodies is Critical to Guide Mosquito Larva Control Interventions and Predict Risk of Mosquito-Borne Diseases. Parasites & Vectors, 6:179.
12) Valle, D.; Clark, J. 2013. Conservation Efforts May Increase Malaria Burden in the Brazilian Amazon. PLoS ONE 8(3): e57519.
11) Zhao, K.; Valle, D.; Popescu, S.; Zhang, X.; Mallick, B. 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, 102-119
10) Valle, D.; Berdanier, A. 2012. Computer programming skills for environmental sciences. Bulletin of the Ecological Society of America, 93.
9) Valle, D.; Clark, J.; Zhao, K. 2011. Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region. PLOS One, 6(11): e27462
8) Clark, J.; Bell, D.; Hersh, M.; Kwit, M.; Moran, E.; Salk, C.; Stine, A.; Valle, D.; Zhu, K. 2011. Individual-scale variation, species-scale differences: Inference needed to understand diversity. Ecology Letters, 14: 1273-1287
7) Valle, D. 2011. Incorrect representation of uncertainty on the modeling of growth leads to biased estimates of future biomass. Ecological Applications, 21(4), pp. 1031-1036
6) Clark, J. S.; Bell, D. M.; Chu, C.; Courbaud, B.; Dietze, M. C.; Hersh, M.; HilleRisLambers, J.; Ibanez, I.; LaDeau, S.; McMahon, S.; Metcalf, J. E.; Mohan, J. E.; Moran, E.; Pangle, L.; Pearson, S. F.; Salk, C. F.; Shen, Z.; Valle, D.; Wyckoff, P. 2010. High dimensional coexistence based on individual variation: a synthesis of evidence. Ecological Monographs, 80(4), pp. 569-608.
5) Valle, D; Staudhammer, C. L.; Cropper Jr., W. P.; van Gardingen, P. 2009. The importance of multimodel projections to assess uncertainty in projections from simulation models. Ecological Applications 19(7), pp. 1680–1692
4) Valle, D.; Staudhammer, C.; Cropper Jr., W. P. 2007. Simulating nontimber forest product management in tropical mixed forests. Journal of Forestry, 105, 6, 301-306.
3) Valle, D.; Phillips, P.; Vidal, E.; Schulze, M.; Grogan, J.; Sales, M.; van Gardingen, P. 2007. Adaptation of a spatially explicit individual tree-based growth and yield model and long-term comparison between reduced-impact and conventional logging in eastern Amazonia, Brazil. Forest Ecology and Management. 243: 187-198.
2) Valle, D.; Schulze, M.; Vidal, E.; Sales, M.; Grogan, J. 2006. Identifying bias in stand-level growth and yield estimations: a case study in eastern Brazilian Amazonia. Forest Ecology and Management. 236: 127-135.
1) van Gardingen, P.; Valle, D.; Thompson, I. 2006. Evaluation of yield regulation options for primary forest in Tapajós National Forest, Brazil. Forest Ecology and Management. 231: 184-195.
35) Valle, D.; Kaplan, D. 2019. Quantifying the impacts of dams on riverine hydrology under non-stationary conditions using incomplete data and Gaussian copula models. Science of the Total Environment 677: 599-611.
34) Amratia, P.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Millar, J.; Oppong, S.; Koram, K.; Valle, D. 2019. Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana. Malaria Journal 18:81
33) Valle, D.; Toh, K.; Millar, J. 2019. Rapid prototyping of decision support tools for conservation. Conservation Biology
32) Valle, D.; Toh, K.; Laporta, G.; Zhao, Q. 2019. Ordinal regression models for zero-inflated and/or over-dispersed count data. Scientific Reports, 9:3046
31) Albuquerque, P.; Valle, D.; Li, D. 2019. Bayesian LDA for mixed-membership clustering analysis: the Rlda package. Knowledge-Based Systems 163: 988-995
30) Hyde, J.; Bohlman, S.; Valle, D. 2018. Transmission lines are an under-acknowledged conservation threat to the Brazilian Amazon. Biological Conservation 228: 343-356.
29) Millar, J.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Amratia, P.; Koram, K.; Oppong, S.; Valle, D. 2018. Detecting risk factors for residual malaria using Bayesian Model Averaging. Malaria Journal, 17:343. doi.org/10.1186/s12936-018-2491-2
28) Simmons, C. S.; Famolare, L.; Macedo, M.; Walker, R. T.; Coe, M.; Scheffers, B.; Arima, E.; Munoz-Carpena, R.; Valle, D.; Fraisee, C.; Moorecroft, P.; Diniz, M.; Diniz, M.; Szlafsztein, C.; Pereira, R.; Ruiz, C.; Rocha, G.; Juhn, D.; Lopes, L. O. C.; Waylen, M.; Antunes, A. 2018. Science in support of Amazonian conservation in the 21st century: the case of Brazil. Biotropica 50(6): 850-858.
27) Valle, D.; Albuquerque, P.; Barberan, A.; Zhao, Q.; Fletcher Jr, R. J. 2018. Extending the Latent Dirichlet Allocation model to presence/absence data: a case study on North American breeding birds and biogeographic shifts expected from climate change. Global Change Biology 1-13.
26) Robertson, E. P.; Fletcher Jr., R. J.; Cattau, C. E.; Udell, B. J.; Reichert, B. E.; Austin, J. D.; Valle, D. 2018. Isolating the roles of movement and reproduction on effective connectivity alters conservation priorities for an endangered bird. Proceedings of the National Academy of Sciences of the United States of America.
25) Chaves, W. A.; Valle, D.; Monroe, M. C.; Wilkie, D. S.; Sieving, K. E.; Sadowsky, B. 2017. Changing wild meat consumption: an experiment in the central Amazon, Brazil. Conservation Letters. doi: 10.1111/conl.12391
24) Valle, D.; Cvetojevic, S.; Robertson, E.; Reichert, B. E.; Hochmair, H. H.; Fletcher Jr., R. J. 2017. Individual movement strategies revealed through novel clustering of emergent movement patterns. Scientific Reports 7, 44052. DOI: 10.1038/srep44052
23) Baiser, B.; Valle, D.; Zelazny, Z.; Burleigh, G. 2017. Non-random patterns of invasion and extinction reduce phylogenetic diversity in island bird assemblages. Ecography 40:001-014.
22) Seva, A. P.; Mao, L.; Ovallos, F. G.; Tucker-Lima, J. M.; Valle, D. 2017. Risk analysis and prediction of visceral leishmaniasis dispersion in Sao Paulo State, Brazil. PLOS Neglected Tropical Diseases 11(2): e0005353
21) Tucker-Lima, J.; Vittor, A.; Rifai, S.; Valle, D. 2017. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philosophical Transaction of the Royal Society B: Biological Sciences 372:20160125
20) Valle, D.; Millar, J.; Amratia, P. 2016. Spatial heterogeneity can undermine the effectiveness of country-wide test and treat policy for malaria: a case study from Burkina Faso. Malaria Journal, 15:513.
19) Tucker-Lima, J.; Valle, D.; Moretto, E.; Pulice, S.; Zuca, N.; Roquetti, D.; Beduschi, L.; Praia, A.; Okamoto, C.; Carvalhaes, V.; Branco, E.; Barbezani, B.; Labandera, E.; Timpe, K.; Kaplan, D. 2016. A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon. Scientific Data. 3:160071 doi: 10.1038/sdata.2016.71
18) Valle, D.; Tucker Lima, J. M.; Millar, J.; Amratia, P.; Haque, U. 2015. Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches. Malaria Journal, 14:434.
17) Valle, D.; Tucker Lima, J. M. 2014. Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model. Malaria Journal, 13:443.
16) Valle, D.; Baiser, B.; Woodall, C.; Chazdon, R. 2014. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method. Ecology Letters, 17:1591-1601
15) Valle, D. 2014. Response to the critique by Hahn et al. entitled “Conservation and Malaria in the Brazilian Amazon”. Am. J. Trop. Med. Hyg., 90(4), pp. 595-596
14) Valle, D.; Clark, J. 2013. Improving the Modeling of Disease Data from the Government Surveillance System: a Case Study on Malaria in the Brazilian Amazon. PLOS Computational Biology 9(11): e1003312.
13) Valle, D.; Zaitchik, B.; Feingold, B.; Spangler, K.; Pan, W. 2013. Abundance of Water Bodies is Critical to Guide Mosquito Larva Control Interventions and Predict Risk of Mosquito-Borne Diseases. Parasites & Vectors, 6:179.
12) Valle, D.; Clark, J. 2013. Conservation Efforts May Increase Malaria Burden in the Brazilian Amazon. PLoS ONE 8(3): e57519.
11) Zhao, K.; Valle, D.; Popescu, S.; Zhang, X.; Mallick, B. 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, 102-119
10) Valle, D.; Berdanier, A. 2012. Computer programming skills for environmental sciences. Bulletin of the Ecological Society of America, 93.
9) Valle, D.; Clark, J.; Zhao, K. 2011. Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region. PLOS One, 6(11): e27462
8) Clark, J.; Bell, D.; Hersh, M.; Kwit, M.; Moran, E.; Salk, C.; Stine, A.; Valle, D.; Zhu, K. 2011. Individual-scale variation, species-scale differences: Inference needed to understand diversity. Ecology Letters, 14: 1273-1287
7) Valle, D. 2011. Incorrect representation of uncertainty on the modeling of growth leads to biased estimates of future biomass. Ecological Applications, 21(4), pp. 1031-1036
6) Clark, J. S.; Bell, D. M.; Chu, C.; Courbaud, B.; Dietze, M. C.; Hersh, M.; HilleRisLambers, J.; Ibanez, I.; LaDeau, S.; McMahon, S.; Metcalf, J. E.; Mohan, J. E.; Moran, E.; Pangle, L.; Pearson, S. F.; Salk, C. F.; Shen, Z.; Valle, D.; Wyckoff, P. 2010. High dimensional coexistence based on individual variation: a synthesis of evidence. Ecological Monographs, 80(4), pp. 569-608.
5) Valle, D; Staudhammer, C. L.; Cropper Jr., W. P.; van Gardingen, P. 2009. The importance of multimodel projections to assess uncertainty in projections from simulation models. Ecological Applications 19(7), pp. 1680–1692
4) Valle, D.; Staudhammer, C.; Cropper Jr., W. P. 2007. Simulating nontimber forest product management in tropical mixed forests. Journal of Forestry, 105, 6, 301-306.
3) Valle, D.; Phillips, P.; Vidal, E.; Schulze, M.; Grogan, J.; Sales, M.; van Gardingen, P. 2007. Adaptation of a spatially explicit individual tree-based growth and yield model and long-term comparison between reduced-impact and conventional logging in eastern Amazonia, Brazil. Forest Ecology and Management. 243: 187-198.
2) Valle, D.; Schulze, M.; Vidal, E.; Sales, M.; Grogan, J. 2006. Identifying bias in stand-level growth and yield estimations: a case study in eastern Brazilian Amazonia. Forest Ecology and Management. 236: 127-135.
1) van Gardingen, P.; Valle, D.; Thompson, I. 2006. Evaluation of yield regulation options for primary forest in Tapajós National Forest, Brazil. Forest Ecology and Management. 231: 184-195.
