1 00:00:08,400 --> 00:00:10,800 Hello, and welcome to the Physics World Weekly 2 00:00:10,800 --> 00:00:11,300 podcast. 3 00:00:11,679 --> 00:00:13,759 I'm Margaret Harris, and this episode of the 4 00:00:13,759 --> 00:00:15,699 podcast is sponsored by EPL, 5 00:00:16,214 --> 00:00:19,015 a journal that publishes original, high quality letters 6 00:00:19,015 --> 00:00:20,475 in all areas of physics. 7 00:00:21,255 --> 00:00:24,375 EPL operates under the scientific policy and control 8 00:00:24,375 --> 00:00:26,234 of the European Physical Society 9 00:00:26,695 --> 00:00:28,554 and is published by EDP Sciences, 10 00:00:29,000 --> 00:00:31,019 the Societa Italiana de Physica, 11 00:00:31,719 --> 00:00:34,700 and IOP Publishing, which also publishes Physics World. 12 00:00:36,119 --> 00:00:38,119 My guests in this episode are the co 13 00:00:38,119 --> 00:00:40,840 authors of an invited mini review article or 14 00:00:40,840 --> 00:00:41,340 perspective 15 00:00:41,954 --> 00:00:44,774 in EPL on the subject of theoretical ecology. 16 00:00:45,795 --> 00:00:48,034 The article was published in June, its title 17 00:00:48,034 --> 00:00:48,534 is 18 00:00:49,795 --> 00:00:52,534 Statistical Physics Approaches to Ecological Communities, 19 00:00:53,155 --> 00:00:55,954 and its authors are Ada Altieri and Silvia 20 00:00:55,954 --> 00:00:56,454 deMonte. 21 00:00:57,490 --> 00:00:59,570 Ada is an associate professor at the Laboratory 22 00:00:59,570 --> 00:01:02,390 for Matter and Complex Systems at the Universite 23 00:01:02,530 --> 00:01:03,990 Paris Cite, France, 24 00:01:04,689 --> 00:01:06,849 and Sylvia is a senior researcher at the 25 00:01:06,849 --> 00:01:09,909 Institute of Biology in the Ecole Normale Superieure 26 00:01:09,969 --> 00:01:12,525 in Paris and the Max Planck Institute for 27 00:01:12,525 --> 00:01:15,025 Evolutionary Biology in Pleuron, Germany. 28 00:01:23,484 --> 00:01:26,590 Ada Altieri and Silvia de Monte, welcome to 29 00:01:26,590 --> 00:01:27,170 the podcast. 30 00:01:27,870 --> 00:01:30,590 Thank you. Thank you so much. Okay. The 31 00:01:30,590 --> 00:01:32,270 first thing I want to get straight for 32 00:01:32,270 --> 00:01:33,650 our listeners is 33 00:01:34,189 --> 00:01:36,909 what is theoretical ecology? You know, what kinds 34 00:01:36,909 --> 00:01:39,390 of questions are you asking? What kinds of 35 00:01:39,390 --> 00:01:41,010 problems are you trying to solve? 36 00:01:41,325 --> 00:01:42,944 Perhaps, I will start. 37 00:01:43,885 --> 00:01:47,644 So theoretical ecology is a pretty broad field 38 00:01:47,644 --> 00:01:49,185 that has a long history. 39 00:01:49,805 --> 00:01:50,364 It has, 40 00:01:50,765 --> 00:01:52,784 traditionally, we think, it started 41 00:01:53,084 --> 00:01:56,239 around the beginning of the 20 century with, 42 00:01:56,560 --> 00:01:59,700 the proposal of using some low dimensional models, 43 00:02:00,319 --> 00:02:03,840 so models of interaction between, just a few 44 00:02:03,840 --> 00:02:05,540 species, typically two species, 45 00:02:06,159 --> 00:02:07,379 to try and understand 46 00:02:08,014 --> 00:02:09,955 some patterns that were observed 47 00:02:10,814 --> 00:02:11,314 in, 48 00:02:12,175 --> 00:02:13,314 ecological systems. 49 00:02:13,935 --> 00:02:16,034 So the sort of the chief, 50 00:02:16,895 --> 00:02:20,194 model people refer to is, the Lotka Volterra 51 00:02:20,574 --> 00:02:21,875 predator prey equations 52 00:02:22,495 --> 00:02:23,314 that were 53 00:02:23,909 --> 00:02:24,409 independently 54 00:02:24,949 --> 00:02:25,449 proposed, 55 00:02:25,990 --> 00:02:26,490 by 56 00:02:26,870 --> 00:02:27,830 Lotka in, 57 00:02:28,310 --> 00:02:29,349 1910 58 00:02:29,349 --> 00:02:30,250 and by Volterra 59 00:02:30,789 --> 00:02:32,650 in 1926 60 00:02:33,110 --> 00:02:33,770 to explain, 61 00:02:34,389 --> 00:02:34,889 why 62 00:02:35,270 --> 00:02:37,610 natural populations seem to vary 63 00:02:38,034 --> 00:02:41,574 over, different years in the number of individuals 64 00:02:42,435 --> 00:02:43,414 in ways that 65 00:02:43,955 --> 00:02:44,935 looked a bit 66 00:02:45,314 --> 00:02:45,814 disconnected, 67 00:02:46,514 --> 00:02:47,974 from, the environmental 68 00:02:48,435 --> 00:02:49,574 year to year variations. 69 00:02:50,479 --> 00:02:51,860 And so they realized, 70 00:02:52,319 --> 00:02:54,900 and this was a really an important realization 71 00:02:55,439 --> 00:02:56,979 that ecological systems 72 00:02:57,519 --> 00:02:59,459 might be maintained out of equilibrium 73 00:03:00,319 --> 00:03:01,219 by the interactions 74 00:03:01,680 --> 00:03:02,739 between species. 75 00:03:03,805 --> 00:03:05,645 So if you have just, you know, a 76 00:03:05,645 --> 00:03:08,365 few species and you're able to know what 77 00:03:08,365 --> 00:03:11,405 is, the probability of interaction or what are 78 00:03:11,405 --> 00:03:13,245 the growth rates of one and the other, 79 00:03:13,245 --> 00:03:14,925 you can still rely on this kind of 80 00:03:14,925 --> 00:03:16,944 models. But there is another 81 00:03:17,405 --> 00:03:20,000 vast array of models that have been developed, 82 00:03:20,540 --> 00:03:23,200 especially in the last thirty years or so 83 00:03:23,580 --> 00:03:24,700 that deal with, 84 00:03:25,180 --> 00:03:28,379 communities that are composed of a vast number 85 00:03:28,379 --> 00:03:29,120 of species 86 00:03:29,420 --> 00:03:30,400 where it is 87 00:03:30,784 --> 00:03:33,284 much more difficult to estimate parameters. 88 00:03:34,145 --> 00:03:35,585 I want to bring Ada in just because 89 00:03:35,585 --> 00:03:37,525 I want to get a couple of examples 90 00:03:37,585 --> 00:03:39,844 if I can of the types of species 91 00:03:39,985 --> 00:03:41,745 that you're talking about in communities. You know, 92 00:03:41,745 --> 00:03:43,264 what's what was a good example of a 93 00:03:43,264 --> 00:03:46,064 two species interaction that's very easy to model 94 00:03:46,064 --> 00:03:46,680 with these 95 00:03:47,159 --> 00:03:50,539 equations that were developed in 1910, 1926? 96 00:03:51,000 --> 00:03:53,259 We can think of just prey predator, 97 00:03:53,719 --> 00:03:55,500 so fox and rabbit, 98 00:03:55,959 --> 00:03:57,180 kind of species. 99 00:03:57,719 --> 00:04:00,104 But as Heather was saying, so the most 100 00:04:00,425 --> 00:04:00,925 remarkable 101 00:04:01,305 --> 00:04:03,944 aspect of recent years, so the true novelty 102 00:04:03,944 --> 00:04:05,564 right now is that we are trying 103 00:04:06,264 --> 00:04:10,104 to characterize emergent mechanisms and collective behaviors of 104 00:04:10,104 --> 00:04:11,805 large ecological communities, 105 00:04:12,439 --> 00:04:15,719 for instance, gut microbial communities in terms of 106 00:04:15,719 --> 00:04:18,379 odor versus disorder phase transition. 107 00:04:19,160 --> 00:04:21,079 So this is one of the goal of 108 00:04:21,079 --> 00:04:22,060 of our approach. 109 00:04:22,680 --> 00:04:25,000 And the second one is also to to 110 00:04:25,000 --> 00:04:25,500 provide 111 00:04:25,879 --> 00:04:26,379 quantitative 112 00:04:27,044 --> 00:04:29,305 estimators, so quantitative discriminators 113 00:04:29,845 --> 00:04:30,904 for detecting 114 00:04:31,444 --> 00:04:32,425 different rigids 115 00:04:32,805 --> 00:04:34,345 in this kind of communities. 116 00:04:35,444 --> 00:04:38,004 What kind of extra complexities does it add 117 00:04:38,004 --> 00:04:40,404 trying to model, say, microbial species? I think, 118 00:04:40,404 --> 00:04:42,425 Sylvia, you were talking about sometimes 119 00:04:42,810 --> 00:04:44,569 you can't get accurate estimates in the way 120 00:04:44,569 --> 00:04:46,889 you can get accurate estimates of, say, foxes 121 00:04:46,889 --> 00:04:47,550 and rabbits. 122 00:04:48,170 --> 00:04:50,970 Yeah. Indeed. So these microbial communities, they really 123 00:04:50,970 --> 00:04:53,149 have a huge number of species 124 00:04:53,529 --> 00:04:54,029 in 125 00:04:54,569 --> 00:04:56,589 many environments, natural environments. 126 00:04:57,274 --> 00:05:00,794 And you can sometimes reconstruct synthetic communities in 127 00:05:00,794 --> 00:05:03,355 the lab. And then these synthetic communities allow 128 00:05:03,355 --> 00:05:06,415 you to measure growth rates, to measure interaction 129 00:05:06,475 --> 00:05:06,975 rates. 130 00:05:07,354 --> 00:05:08,175 But this involves 131 00:05:08,970 --> 00:05:09,470 estimating 132 00:05:09,850 --> 00:05:12,009 a large number of parameters and a lot 133 00:05:12,009 --> 00:05:13,710 of experimental work. 134 00:05:14,089 --> 00:05:15,949 This can be done for relatively 135 00:05:16,330 --> 00:05:17,150 simple communities 136 00:05:17,610 --> 00:05:19,550 and, in control settings. 137 00:05:20,410 --> 00:05:20,910 However, 138 00:05:21,245 --> 00:05:22,785 when you deal with communities, 139 00:05:23,324 --> 00:05:25,745 in nature or, in a gut, 140 00:05:26,045 --> 00:05:29,345 you can't really go and extract one species 141 00:05:29,645 --> 00:05:31,585 and or measure in situ. 142 00:05:32,125 --> 00:05:32,625 So 143 00:05:33,085 --> 00:05:33,745 you often 144 00:05:34,650 --> 00:05:35,790 only have an idea 145 00:05:36,090 --> 00:05:37,310 of what kind 146 00:05:38,250 --> 00:05:38,910 of statistical 147 00:05:39,850 --> 00:05:40,350 features 148 00:05:40,810 --> 00:05:42,110 have the distribution 149 00:05:43,129 --> 00:05:43,790 of interactions. 150 00:05:44,889 --> 00:05:47,375 Or at least this is what our work 151 00:05:47,595 --> 00:05:48,254 is about, 152 00:05:48,555 --> 00:05:51,375 trying to infer, for instance, the statistical features 153 00:05:52,235 --> 00:05:53,615 from something that can 154 00:05:54,154 --> 00:05:54,814 be measured 155 00:05:55,354 --> 00:05:56,894 at the level of the community 156 00:05:57,514 --> 00:05:58,414 without knowing 157 00:05:58,875 --> 00:05:59,375 exactly 158 00:06:00,210 --> 00:06:02,949 what's in every single species is doing. 159 00:06:03,490 --> 00:06:06,529 So can you use statistical physics models to 160 00:06:06,529 --> 00:06:07,830 predict what will happen 161 00:06:08,290 --> 00:06:09,270 in real ecosystems, 162 00:06:09,730 --> 00:06:12,710 like, I don't know, tipping points or circumstances 163 00:06:12,930 --> 00:06:13,274 which 164 00:06:13,995 --> 00:06:16,634 one species in a community might become extinct 165 00:06:16,634 --> 00:06:18,975 or the community itself becomes unstable? 166 00:06:19,834 --> 00:06:22,475 Yeah. So for instance, a small change in 167 00:06:22,475 --> 00:06:22,975 environmental 168 00:06:23,354 --> 00:06:25,134 condition or system parameters 169 00:06:25,435 --> 00:06:26,574 that can be demographic 170 00:06:26,954 --> 00:06:27,454 fluctuations 171 00:06:27,754 --> 00:06:29,759 of birth and the processes 172 00:06:30,379 --> 00:06:32,080 can cause a sudden turnover, 173 00:06:32,780 --> 00:06:33,600 so irreversible 174 00:06:33,980 --> 00:06:34,480 shift 175 00:06:34,780 --> 00:06:38,220 from one equilibrium state to another equilibrium state. 176 00:06:38,220 --> 00:06:40,620 So we can, for instance, detect a phase, 177 00:06:40,620 --> 00:06:41,520 a self sustaining 178 00:06:42,060 --> 00:06:43,600 phase where all species 179 00:06:44,324 --> 00:06:44,824 survive 180 00:06:45,524 --> 00:06:48,824 and another phase where all species get extinct. 181 00:06:49,125 --> 00:06:50,824 So this kind of transition 182 00:06:51,204 --> 00:06:53,925 is typically referred to as a tipping point 183 00:06:53,925 --> 00:06:57,064 and in theoretical and statistical physics 184 00:06:57,439 --> 00:06:58,579 is also called 185 00:06:58,959 --> 00:06:59,620 a spinodal 186 00:07:00,000 --> 00:07:00,500 transition 187 00:07:00,959 --> 00:07:03,540 because it's associated with a vanishing 188 00:07:03,919 --> 00:07:04,419 eigenvalue 189 00:07:04,879 --> 00:07:08,019 of a stability matrix a stability matrix, 190 00:07:08,959 --> 00:07:12,079 which provide information on the emergent criticality, the 191 00:07:12,079 --> 00:07:12,979 emergent transition. 192 00:07:13,415 --> 00:07:16,935 So following intuition from statistical physics, we can 193 00:07:16,935 --> 00:07:17,435 try 194 00:07:17,814 --> 00:07:20,954 to estimate and characterize these tipping points. 195 00:07:21,495 --> 00:07:21,995 Empirically 196 00:07:22,295 --> 00:07:23,754 speaking, in real ecosystem, 197 00:07:24,375 --> 00:07:25,834 one could measure fluctuation 198 00:07:26,454 --> 00:07:27,754 of the average abundance 199 00:07:28,279 --> 00:07:32,199 with respect to environmental noise, which affect the 200 00:07:32,199 --> 00:07:35,720 current capacity, so the maximum attainable value of 201 00:07:35,720 --> 00:07:36,779 a species abundance, 202 00:07:37,240 --> 00:07:38,300 or also individual 203 00:07:38,759 --> 00:07:39,579 growth rates. 204 00:07:39,975 --> 00:07:41,274 So this is perfectly 205 00:07:41,735 --> 00:07:44,074 doable in in a real ecosystem 206 00:07:44,694 --> 00:07:45,595 and provides 207 00:07:46,214 --> 00:07:47,595 early word signal 208 00:07:48,455 --> 00:07:49,274 of the proximity 209 00:07:49,814 --> 00:07:51,035 to a deep endpoint. 210 00:07:52,375 --> 00:07:56,259 How applicable is your work to actually colleges 211 00:07:56,259 --> 00:07:57,860 out in the field? Is this one of 212 00:07:57,860 --> 00:07:59,699 your goals, or is it all very far 213 00:07:59,699 --> 00:08:01,639 removed several steps back from that? 214 00:08:02,180 --> 00:08:04,420 So I suppose that in the field, there 215 00:08:04,420 --> 00:08:06,040 are people who, 216 00:08:06,900 --> 00:08:07,560 do this. 217 00:08:07,939 --> 00:08:08,439 So 218 00:08:09,194 --> 00:08:09,855 I personally 219 00:08:10,475 --> 00:08:12,654 don't, go that far because, 220 00:08:13,355 --> 00:08:13,855 my 221 00:08:14,394 --> 00:08:14,894 focus 222 00:08:15,194 --> 00:08:16,415 and that of my collaborators 223 00:08:16,954 --> 00:08:17,694 is mostly 224 00:08:18,235 --> 00:08:20,095 to try and understand 225 00:08:20,555 --> 00:08:21,055 how 226 00:08:21,580 --> 00:08:22,400 the mechanisms, 227 00:08:23,020 --> 00:08:24,960 that are involved in ecology 228 00:08:25,500 --> 00:08:26,000 shape 229 00:08:26,300 --> 00:08:28,620 patterns that can be observed at the level 230 00:08:28,620 --> 00:08:29,439 of a community. 231 00:08:30,540 --> 00:08:33,019 And the reason we focus on this is 232 00:08:33,019 --> 00:08:35,440 that it is still pretty poorly understood 233 00:08:36,105 --> 00:08:36,605 how 234 00:08:37,065 --> 00:08:37,565 processes 235 00:08:38,105 --> 00:08:39,565 turn into patterns. 236 00:08:40,264 --> 00:08:42,445 So what we can observe typically 237 00:08:42,904 --> 00:08:43,565 are patterns. 238 00:08:44,264 --> 00:08:46,105 Like, I don't know, we we go out 239 00:08:46,105 --> 00:08:46,924 in the field 240 00:08:47,384 --> 00:08:50,100 and we measure how many individuals are there 241 00:08:50,100 --> 00:08:51,480 of every species. 242 00:08:52,019 --> 00:08:54,179 So if it's a microbial community, there are 243 00:08:54,179 --> 00:08:57,299 many, many different species, hundreds or thousands of 244 00:08:57,299 --> 00:08:59,700 different species. And then there are ways to 245 00:08:59,700 --> 00:09:00,200 estimate 246 00:09:00,835 --> 00:09:01,414 how many 247 00:09:01,794 --> 00:09:04,534 of each of these species is there. 248 00:09:05,075 --> 00:09:07,174 Once you have this kind of information, 249 00:09:07,554 --> 00:09:08,294 you can 250 00:09:09,634 --> 00:09:10,934 write down histograms, 251 00:09:11,475 --> 00:09:12,134 do some 252 00:09:12,514 --> 00:09:13,014 statistics 253 00:09:13,394 --> 00:09:14,294 on this data. 254 00:09:14,860 --> 00:09:17,600 And these statistics, they turn out to be 255 00:09:18,139 --> 00:09:18,639 sometimes, 256 00:09:19,179 --> 00:09:22,460 different from one ecosystem to another, but sometimes 257 00:09:22,460 --> 00:09:24,160 they are incredibly similar. 258 00:09:24,779 --> 00:09:25,759 And we still 259 00:09:26,139 --> 00:09:27,600 don't understand completely 260 00:09:27,980 --> 00:09:29,440 why certain patterns 261 00:09:29,820 --> 00:09:30,399 are so 262 00:09:31,154 --> 00:09:35,014 conserved or universal if you want, to use, 263 00:09:35,315 --> 00:09:36,695 a physics term. 264 00:09:37,394 --> 00:09:40,834 And sometimes instead, we would like actually to 265 00:09:40,834 --> 00:09:44,274 focus on differences rather than on what is 266 00:09:44,274 --> 00:09:45,254 the same everywhere. 267 00:09:46,079 --> 00:09:46,579 So 268 00:09:46,879 --> 00:09:47,360 our, 269 00:09:48,000 --> 00:09:49,779 goal is mainly to 270 00:09:50,240 --> 00:09:50,740 understand 271 00:09:52,159 --> 00:09:53,059 what determines 272 00:09:53,440 --> 00:09:53,940 this, 273 00:09:54,480 --> 00:09:54,980 regularities, 274 00:09:55,759 --> 00:09:57,539 and what determines the differences. 275 00:09:58,125 --> 00:10:01,004 And then, of course, the the ultimate goal 276 00:10:01,004 --> 00:10:03,245 would be to be able to go out 277 00:10:03,245 --> 00:10:05,644 in nature and to be able to say, 278 00:10:05,644 --> 00:10:08,705 okay, this ecosystem that has these statistical features 279 00:10:09,245 --> 00:10:11,665 is particularly endangered, for instance, 280 00:10:12,125 --> 00:10:14,830 rather than this other one is instead, 281 00:10:15,470 --> 00:10:16,929 you know, a stable ecosystem. 282 00:10:17,789 --> 00:10:20,289 We observe, for instance, this kind of differences 283 00:10:20,509 --> 00:10:21,730 between, ecosystems 284 00:10:22,269 --> 00:10:22,929 that are, 285 00:10:23,549 --> 00:10:24,049 like, 286 00:10:24,909 --> 00:10:25,409 associated, 287 00:10:26,029 --> 00:10:28,845 to hosts. So they have evolved for a 288 00:10:28,845 --> 00:10:29,504 long time 289 00:10:29,884 --> 00:10:32,845 within a host. So they are sort of 290 00:10:32,845 --> 00:10:33,345 more, 291 00:10:34,365 --> 00:10:36,304 stable in in their functioning 292 00:10:37,004 --> 00:10:38,065 rather than ecosystems, 293 00:10:38,524 --> 00:10:40,304 like, I don't know, plankton ecosystems, 294 00:10:41,004 --> 00:10:42,865 that have a very, very high turnover, 295 00:10:43,669 --> 00:10:46,389 and, they tend to have many species that 296 00:10:46,389 --> 00:10:49,610 are rare and plausibly to an extinction threshold. 297 00:10:50,389 --> 00:10:52,549 I like the distinction you made between patterns 298 00:10:52,549 --> 00:10:54,230 and processes. You want to be able to 299 00:10:54,230 --> 00:10:56,169 go out and see a pattern in nature 300 00:10:56,230 --> 00:10:58,329 or in the gut or in the laboratory 301 00:10:58,804 --> 00:11:01,284 and say, ah, because I see this pattern, 302 00:11:01,284 --> 00:11:02,964 these are the processes that are going on, 303 00:11:02,964 --> 00:11:03,625 and therefore, 304 00:11:04,084 --> 00:11:06,804 this is what might happen next. Yep. That's 305 00:11:06,804 --> 00:11:09,284 exactly what people have been trying to do 306 00:11:09,284 --> 00:11:10,264 for a long time. 307 00:11:10,899 --> 00:11:13,220 The fact is that, so as we review 308 00:11:13,220 --> 00:11:14,039 in the article, 309 00:11:14,500 --> 00:11:17,940 there had been many different approaches where people 310 00:11:17,940 --> 00:11:20,679 started from hypothesis that seem to be ecologically 311 00:11:20,820 --> 00:11:21,320 reasonable, 312 00:11:21,860 --> 00:11:24,175 and then they'd use the pattern. And then 313 00:11:24,175 --> 00:11:26,095 they went out in nature, looked at the 314 00:11:26,095 --> 00:11:28,654 pattern that looked like that and said, ah, 315 00:11:28,654 --> 00:11:31,134 okay. I I believe then then my model 316 00:11:31,134 --> 00:11:31,955 is right. 317 00:11:32,654 --> 00:11:33,154 But 318 00:11:33,535 --> 00:11:34,754 one of the problems 319 00:11:35,055 --> 00:11:38,129 that has emerged is that different models tend 320 00:11:38,129 --> 00:11:40,149 to provide very similar patterns 321 00:11:40,529 --> 00:11:42,149 that are very hard to distinguish 322 00:11:42,769 --> 00:11:44,230 from empirical data. 323 00:11:44,690 --> 00:11:45,350 And then 324 00:11:45,730 --> 00:11:46,529 it is still, 325 00:11:47,330 --> 00:11:49,590 one of the open questions that we raised, 326 00:11:50,529 --> 00:11:53,424 in the more perspective part. So what is 327 00:11:53,424 --> 00:11:53,924 exactly 328 00:11:55,184 --> 00:11:56,404 that is peculiar 329 00:11:56,784 --> 00:11:58,084 of a given model 330 00:11:58,464 --> 00:12:01,825 and, how can we distinguish models that start 331 00:12:01,825 --> 00:12:03,365 from very different assumptions 332 00:12:03,985 --> 00:12:05,125 Mhmm. Based on data? 333 00:12:05,929 --> 00:12:07,929 Okay. So as you've already alluded, you've written 334 00:12:07,929 --> 00:12:10,250 the paper recently for the journal EPL in 335 00:12:10,250 --> 00:12:13,549 which you discussed some basic classes of statistical 336 00:12:13,690 --> 00:12:17,949 physics models that you as theoretical ecologists use 337 00:12:18,409 --> 00:12:20,409 and the challenges that future models need to 338 00:12:20,409 --> 00:12:20,909 address. 339 00:12:21,235 --> 00:12:22,754 I wonder if we could discuss a few 340 00:12:22,754 --> 00:12:23,735 of these challenges 341 00:12:24,274 --> 00:12:26,355 in a bit more depth. We've already talked 342 00:12:26,355 --> 00:12:30,195 about species interactions, including higher order interactions. What 343 00:12:30,195 --> 00:12:30,934 does it mean? 344 00:12:31,475 --> 00:12:34,434 So it basically means that the interaction between 345 00:12:34,434 --> 00:12:35,159 two species 346 00:12:35,559 --> 00:12:38,120 is modulated by the presence of a third 347 00:12:38,120 --> 00:12:40,139 one or a fourth one. 348 00:12:40,440 --> 00:12:42,059 So suppose that species 349 00:12:42,519 --> 00:12:43,339 a competes 350 00:12:43,879 --> 00:12:45,819 with species b for nutrients, 351 00:12:46,360 --> 00:12:48,600 then suppose now that the strength of this 352 00:12:48,600 --> 00:12:49,100 competitive 353 00:12:49,480 --> 00:12:50,860 effect is reduced 354 00:12:51,445 --> 00:12:54,264 if species c is present because 355 00:12:55,284 --> 00:12:57,784 c improves soil condition via 356 00:12:58,164 --> 00:12:59,384 nitrogen fixation 357 00:12:59,845 --> 00:13:01,065 or other effect. 358 00:13:01,445 --> 00:13:04,345 So overall, a, b, and c work 359 00:13:04,804 --> 00:13:05,464 out altogether 360 00:13:06,129 --> 00:13:09,029 and affect both the stability and the dynamics 361 00:13:09,409 --> 00:13:10,309 of the community. 362 00:13:10,850 --> 00:13:12,789 So on one hand, I higher interaction 363 00:13:13,250 --> 00:13:16,769 have the potential to add another layer of 364 00:13:16,769 --> 00:13:17,269 complexity, 365 00:13:17,809 --> 00:13:18,949 to add more biological 366 00:13:19,409 --> 00:13:22,695 complexity and realism to the community. But on 367 00:13:22,695 --> 00:13:25,195 the other hand, they further complicate the analysis. 368 00:13:25,894 --> 00:13:29,735 So rigorously understanding how relevant they are in 369 00:13:29,735 --> 00:13:33,014 maintaining diversity in the community is still an 370 00:13:33,014 --> 00:13:33,835 open question. 371 00:13:34,375 --> 00:13:35,835 I would just perhaps 372 00:13:36,440 --> 00:13:37,820 step back a second 373 00:13:38,360 --> 00:13:40,059 and just say that, 374 00:13:40,679 --> 00:13:41,179 many 375 00:13:41,879 --> 00:13:43,500 simplified models of ecosystems 376 00:13:44,200 --> 00:13:46,700 usually only consider pairwise interactions. 377 00:13:47,559 --> 00:13:50,215 It is certainly more realistic to take into 378 00:13:50,215 --> 00:13:52,075 account higher order interactions 379 00:13:52,855 --> 00:13:55,975 even though most results have been obtained so 380 00:13:55,975 --> 00:13:59,595 far for this simple setting where basically 381 00:14:00,134 --> 00:14:01,355 interactions among 382 00:14:01,839 --> 00:14:03,299 a very complex community 383 00:14:03,679 --> 00:14:06,559 just boil down to interaction between pairs of 384 00:14:06,559 --> 00:14:07,059 species. 385 00:14:08,000 --> 00:14:08,980 Another simplification 386 00:14:09,600 --> 00:14:13,139 is this idea that obviously any real ecosystem, 387 00:14:13,360 --> 00:14:16,019 even a bacterial ecosystem with millions of individuals 388 00:14:16,080 --> 00:14:17,625 will have a finite number of species. 389 00:14:18,745 --> 00:14:20,924 Why is this hard to model mathematically? 390 00:14:21,705 --> 00:14:24,985 Indeed, ecological communities have a varying number of 391 00:14:24,985 --> 00:14:25,485 species. 392 00:14:26,184 --> 00:14:27,404 So macroorganisms 393 00:14:28,345 --> 00:14:28,825 like, 394 00:14:29,225 --> 00:14:30,445 animals and plants 395 00:14:31,470 --> 00:14:34,690 have communities that are composed of, you know, 396 00:14:35,230 --> 00:14:38,129 at maximum a few 100 of different species. 397 00:14:38,830 --> 00:14:42,049 And this is the number is much higher 398 00:14:42,110 --> 00:14:42,610 for 399 00:14:43,245 --> 00:14:44,304 microbial communities. 400 00:14:45,085 --> 00:14:47,424 But, indeed it is not infinite. 401 00:14:48,205 --> 00:14:50,705 So the problem is to find 402 00:14:51,164 --> 00:14:52,945 the right spot between, 403 00:14:53,725 --> 00:14:56,764 very low dimensional systems. So that can be 404 00:14:56,764 --> 00:14:57,264 analyzed 405 00:14:57,830 --> 00:14:58,490 with methods, 406 00:14:58,950 --> 00:15:01,290 from, I don't know, bifurcation analysis, 407 00:15:01,830 --> 00:15:03,290 dynamic system theory, 408 00:15:03,830 --> 00:15:06,410 and these other models that come from statistical 409 00:15:06,470 --> 00:15:08,730 physics, where you have in principle, 410 00:15:09,029 --> 00:15:10,649 an infinite number of species. 411 00:15:11,654 --> 00:15:14,715 What turns out is that in many cases, 412 00:15:15,254 --> 00:15:18,154 even though, results are obtained in the thermodynamic 413 00:15:18,455 --> 00:15:18,955 limit, 414 00:15:19,335 --> 00:15:21,995 a lot of conclusions still hold for 415 00:15:22,535 --> 00:15:24,394 a reasonable number of species. 416 00:15:24,860 --> 00:15:27,339 So, at a certain point, of course, this 417 00:15:27,339 --> 00:15:28,159 breaks down, 418 00:15:28,620 --> 00:15:31,500 but there are even some features that are 419 00:15:31,500 --> 00:15:32,000 actually 420 00:15:32,620 --> 00:15:33,120 strictly 421 00:15:33,820 --> 00:15:34,639 low dimensional 422 00:15:35,019 --> 00:15:37,519 or, low co dimensional, and therefore 423 00:15:37,820 --> 00:15:38,720 they can be, 424 00:15:39,384 --> 00:15:39,884 described 425 00:15:40,264 --> 00:15:42,345 even, with, tools from, 426 00:15:42,904 --> 00:15:44,365 dynamical system theory 427 00:15:44,825 --> 00:15:47,404 despite, being emergent from 428 00:15:47,705 --> 00:15:49,625 a system with a very, very high number 429 00:15:49,625 --> 00:15:50,764 of degrees of freedom. 430 00:15:51,705 --> 00:15:54,360 I will also like to say that the 431 00:15:54,360 --> 00:15:56,759 kind of techniques that we are describing in 432 00:15:56,759 --> 00:15:59,559 our review are typically referred to as mean 433 00:15:59,559 --> 00:16:02,460 field approach. So one of these is dynamical 434 00:16:02,519 --> 00:16:05,659 mean field theory. And so it's very helpful. 435 00:16:05,960 --> 00:16:08,554 It's very useful to characterize 436 00:16:09,095 --> 00:16:11,195 emergent mechanisms, collective behaviors, 437 00:16:11,735 --> 00:16:14,934 but it has been also shown that it 438 00:16:14,934 --> 00:16:17,914 can fail in the presence of sparse networks. 439 00:16:18,470 --> 00:16:21,429 So when the network is not completely connect 440 00:16:21,429 --> 00:16:23,290 and each node, each species 441 00:16:23,830 --> 00:16:27,429 is just connected, is just related to a 442 00:16:27,429 --> 00:16:27,929 few 443 00:16:28,230 --> 00:16:28,889 of them. 444 00:16:29,269 --> 00:16:31,769 And so in this case, we should improve 445 00:16:32,195 --> 00:16:32,855 and refine 446 00:16:33,154 --> 00:16:36,195 our techniques to take into account also finite 447 00:16:36,195 --> 00:16:36,934 size fluctuations. 448 00:16:37,554 --> 00:16:39,394 And so combine it at the same time 449 00:16:39,394 --> 00:16:40,534 numerical simulation 450 00:16:40,835 --> 00:16:43,254 with rigorous analytical method. 451 00:16:44,309 --> 00:16:47,029 What does failure look like when you're modeling 452 00:16:47,029 --> 00:16:48,629 a system like this? How do you know 453 00:16:48,629 --> 00:16:50,070 when your model has failed? Do you have 454 00:16:50,070 --> 00:16:52,149 to compare it with experimental data or you're 455 00:16:52,149 --> 00:16:54,230 just like, oh gosh. That's pretty something really 456 00:16:54,230 --> 00:16:54,730 unphysical? 457 00:16:55,830 --> 00:16:57,125 Yeah. So experimental 458 00:16:57,504 --> 00:17:00,245 data that are available or rather, 459 00:17:00,545 --> 00:17:02,165 let's say, empirical data 460 00:17:02,625 --> 00:17:03,125 for, 461 00:17:04,065 --> 00:17:05,125 natural communities 462 00:17:05,664 --> 00:17:07,025 are typically this, 463 00:17:07,424 --> 00:17:09,605 measures of species abundances. 464 00:17:10,470 --> 00:17:12,890 So this can be either like a snapshot 465 00:17:13,269 --> 00:17:13,769 measures. 466 00:17:14,309 --> 00:17:15,929 Like, I go out in the ocean, 467 00:17:16,390 --> 00:17:17,130 I sample, 468 00:17:17,669 --> 00:17:19,690 I come back, and then I sequence, 469 00:17:20,230 --> 00:17:21,929 whatever I find in the water. 470 00:17:22,789 --> 00:17:25,190 Or in some cases and actually more and 471 00:17:25,190 --> 00:17:25,929 more luckily, 472 00:17:26,284 --> 00:17:29,244 there are time series. So there are, you 473 00:17:29,244 --> 00:17:31,984 know, one can access the variation over time 474 00:17:32,204 --> 00:17:33,984 of the abundance of different species. 475 00:17:34,605 --> 00:17:37,904 And so this time series are particularly valuable 476 00:17:38,284 --> 00:17:38,784 because, 477 00:17:39,164 --> 00:17:41,099 they provide a different perspective 478 00:17:55,099 --> 00:17:56,079 abandoned again. 479 00:17:56,865 --> 00:17:59,924 So if we don't access this temporal variation, 480 00:18:00,384 --> 00:18:01,845 we will never know exactly 481 00:18:02,384 --> 00:18:03,204 which species 482 00:18:03,825 --> 00:18:04,565 are abundant, 483 00:18:05,505 --> 00:18:06,565 and whether 484 00:18:07,105 --> 00:18:08,884 there is something that is interesting 485 00:18:09,559 --> 00:18:12,059 also in the dynamics and not only 486 00:18:12,440 --> 00:18:14,460 on, you know, our snapshot 487 00:18:14,759 --> 00:18:16,299 vision of the ecosystem. 488 00:18:17,240 --> 00:18:20,359 So this goes back to the idea by 489 00:18:20,359 --> 00:18:23,615 Lotka and Volterra that, you know, ecosystems need 490 00:18:23,615 --> 00:18:24,755 not be at equilibrium. 491 00:18:25,294 --> 00:18:27,474 They can also live, in states, 492 00:18:27,775 --> 00:18:28,994 that are variable. 493 00:18:29,454 --> 00:18:32,255 And we know now, especially thanks to the 494 00:18:32,255 --> 00:18:33,394 latest developments, 495 00:18:34,190 --> 00:18:37,549 where statistical physics has contributed a lot, that 496 00:18:37,549 --> 00:18:39,329 there are many different, 497 00:18:40,029 --> 00:18:41,329 out of equilibrium regimes, 498 00:18:41,710 --> 00:18:42,849 that can be partially 499 00:18:43,470 --> 00:18:45,730 described by statistical physics methods. 500 00:18:46,625 --> 00:18:47,525 Do you have an example 501 00:18:47,825 --> 00:18:50,305 of something that statistical physics has been very 502 00:18:50,305 --> 00:18:52,325 successful in describing and out of the equilibrium 503 00:18:52,384 --> 00:18:52,884 system? 504 00:18:53,825 --> 00:18:55,765 So something we have been studying 505 00:18:56,305 --> 00:19:00,005 are the distribution of abundances of planktonic 506 00:19:00,384 --> 00:19:01,365 protest communities. 507 00:19:01,940 --> 00:19:02,819 So protests are, 508 00:19:03,619 --> 00:19:04,759 unicellular eukaryotes. 509 00:19:05,700 --> 00:19:07,319 And, there have been surveys, 510 00:19:07,700 --> 00:19:09,160 in the last years, 511 00:19:09,859 --> 00:19:10,339 where, 512 00:19:10,980 --> 00:19:12,359 basically the whole 513 00:19:13,059 --> 00:19:14,119 the total biodiversity 514 00:19:14,740 --> 00:19:17,365 of the sunlit ocean has been revealed 515 00:19:17,825 --> 00:19:18,644 by sampling 516 00:19:19,345 --> 00:19:21,845 in many different locations in the world ocean. 517 00:19:22,384 --> 00:19:24,964 And one striking feature there was, 518 00:19:25,345 --> 00:19:25,845 that 519 00:19:26,625 --> 00:19:27,524 most species 520 00:19:27,984 --> 00:19:29,365 appear to be rare, 521 00:19:30,289 --> 00:19:32,069 and one could assess 522 00:19:32,450 --> 00:19:35,509 what was the law of decay of abundances. 523 00:19:36,690 --> 00:19:39,329 So if you rank species from the most 524 00:19:39,329 --> 00:19:40,869 abundant to the least abundant. 525 00:19:42,369 --> 00:19:43,190 And methods 526 00:19:45,065 --> 00:19:47,725 derived by statistical physics and 527 00:19:48,505 --> 00:19:50,525 using this, lot of models, 528 00:19:51,144 --> 00:19:53,625 this time a generalized lot of models. So 529 00:19:53,625 --> 00:19:54,125 with 530 00:19:54,585 --> 00:19:56,125 many, many different species 531 00:19:56,700 --> 00:19:57,440 allowed to 532 00:19:57,980 --> 00:19:59,359 reproduce, the observation, 533 00:20:00,619 --> 00:20:01,919 that this decay, 534 00:20:02,539 --> 00:20:04,880 of the so called species abundance distribution 535 00:20:05,819 --> 00:20:08,460 is actually a power law. And that this 536 00:20:08,460 --> 00:20:11,015 power law has an exponent that is different 537 00:20:11,015 --> 00:20:13,654 from one that is, the typical exponent of 538 00:20:13,654 --> 00:20:14,315 the Ziff's 539 00:20:14,775 --> 00:20:15,994 law. Models also 540 00:20:16,615 --> 00:20:17,414 sort of, 541 00:20:17,734 --> 00:20:21,015 retrieve exponents that are in the same order 542 00:20:21,015 --> 00:20:22,394 of magnitude. So, 543 00:20:22,934 --> 00:20:25,380 that is between, let's say, one and two 544 00:20:25,700 --> 00:20:27,720 as those that are observed empirically. 545 00:20:28,579 --> 00:20:30,900 And this, and this kind of observation is 546 00:20:30,900 --> 00:20:31,400 actually 547 00:20:31,940 --> 00:20:33,640 linked in, with 548 00:20:34,019 --> 00:20:35,799 different models. So some of them, 549 00:20:36,180 --> 00:20:36,680 so 550 00:20:37,140 --> 00:20:40,599 this lots of Volterra, some others, neutral models. 551 00:20:41,434 --> 00:20:43,454 But lots of Volterra models are particularly 552 00:20:43,835 --> 00:20:46,734 useful because they also provide a dynamical 553 00:20:47,275 --> 00:20:50,234 picture of, what is the underpinning of this 554 00:20:50,234 --> 00:20:50,734 distribution. 555 00:20:51,755 --> 00:20:54,634 That is, the idea that there are at 556 00:20:54,634 --> 00:20:55,694 a given time, 557 00:20:56,130 --> 00:20:58,450 a few species that are highly dominant and 558 00:20:58,450 --> 00:20:58,950 successful, 559 00:20:59,490 --> 00:21:02,450 but this highly successful species do not stay 560 00:21:02,450 --> 00:21:05,109 there forever, and there is a permanent turnover 561 00:21:05,890 --> 00:21:08,150 of successful and rare species. 562 00:21:08,764 --> 00:21:10,764 So all the diversity that is, 563 00:21:11,244 --> 00:21:14,384 observed is actually necessary to maintain 564 00:21:15,404 --> 00:21:15,904 overall 565 00:21:16,444 --> 00:21:17,904 this very high diversity 566 00:21:18,605 --> 00:21:19,984 of controlling communities. 567 00:21:21,019 --> 00:21:23,419 Which presumably has some implications for conservation. You 568 00:21:23,419 --> 00:21:25,339 can't just go out and save one species 569 00:21:25,339 --> 00:21:26,779 because you have to save them all if 570 00:21:26,779 --> 00:21:29,419 you want to preserve the Absolutely. Absolutely. Exactly. 571 00:21:29,419 --> 00:21:32,000 Because if you throw away the rare species, 572 00:21:32,220 --> 00:21:34,399 actually, your system stops oscillating 573 00:21:34,940 --> 00:21:35,440 and 574 00:21:36,214 --> 00:21:37,674 stops this dynamics 575 00:21:38,695 --> 00:21:39,674 of turnover. 576 00:21:41,015 --> 00:21:42,855 I want to get into this idea of 577 00:21:42,855 --> 00:21:45,414 the importance of time in in the modeling 578 00:21:45,414 --> 00:21:47,174 because one of the things you say in 579 00:21:47,174 --> 00:21:49,255 your paper is is actually quite hard to 580 00:21:49,255 --> 00:21:53,230 model is evolution. Now all organisms evolve, and 581 00:21:53,230 --> 00:21:55,950 some organisms evolve fairly slowly because they only 582 00:21:55,950 --> 00:21:56,450 have 583 00:21:56,909 --> 00:21:59,630 descendants every few years. But bacteria, of course, 584 00:21:59,630 --> 00:22:02,029 which you're talking about modeling a lot, they 585 00:22:02,029 --> 00:22:04,215 evolve quite quickly. Why is this hard to 586 00:22:04,215 --> 00:22:05,974 model, and why is it so important that 587 00:22:05,974 --> 00:22:07,835 future models should capture it? 588 00:22:09,174 --> 00:22:12,875 Yeah. Evolution is is really the central feature 589 00:22:13,335 --> 00:22:13,815 of, 590 00:22:14,295 --> 00:22:15,035 of life. 591 00:22:15,414 --> 00:22:17,674 And, indeed, in biology, 592 00:22:18,720 --> 00:22:19,940 we we are faced 593 00:22:20,880 --> 00:22:23,299 all over the place, with the fact that 594 00:22:23,599 --> 00:22:25,279 when we give a name to an entity 595 00:22:25,279 --> 00:22:28,319 like a species, actually, this species is changing 596 00:22:28,319 --> 00:22:29,059 over time. 597 00:22:29,599 --> 00:22:32,355 And it is changing in terms of, the 598 00:22:32,355 --> 00:22:34,595 the composition of the gene pool, in the 599 00:22:34,595 --> 00:22:35,095 species, 600 00:22:35,714 --> 00:22:37,575 and then species can branch 601 00:22:37,954 --> 00:22:39,575 and produce new species. 602 00:22:40,515 --> 00:22:42,535 So this is particularly 603 00:22:43,555 --> 00:22:45,474 it's hard to model, but at the same 604 00:22:45,474 --> 00:22:45,974 time, 605 00:22:46,309 --> 00:22:49,190 it can, there are lots of models that 606 00:22:49,190 --> 00:22:50,410 take this into account. 607 00:22:50,950 --> 00:22:53,750 What is, difficult to model is that we 608 00:22:53,750 --> 00:22:55,049 cannot really predict, 609 00:22:55,429 --> 00:22:56,970 what will be the effect 610 00:22:57,269 --> 00:22:58,009 of mutations. 611 00:22:58,625 --> 00:23:01,684 So this is again is our stochastic processes 612 00:23:02,384 --> 00:23:03,505 that allow us, 613 00:23:03,984 --> 00:23:05,525 or nature to explore 614 00:23:06,305 --> 00:23:07,845 a number of different phenotypes 615 00:23:08,625 --> 00:23:11,525 and a number potentially of different communities 616 00:23:12,065 --> 00:23:12,805 or species, 617 00:23:13,570 --> 00:23:16,549 but we cannot really say which will be 618 00:23:17,170 --> 00:23:19,350 the new species that will appear. 619 00:23:19,890 --> 00:23:21,509 And this is somehow 620 00:23:22,450 --> 00:23:22,950 problematic 621 00:23:23,490 --> 00:23:26,450 if you want to be sure that the 622 00:23:26,450 --> 00:23:26,950 novelty 623 00:23:27,330 --> 00:23:27,830 obeys 624 00:23:28,605 --> 00:23:29,585 some given, 625 00:23:30,285 --> 00:23:30,785 law. 626 00:23:31,884 --> 00:23:32,865 At the same time, 627 00:23:33,325 --> 00:23:34,065 if one 628 00:23:34,765 --> 00:23:35,265 considers, 629 00:23:35,804 --> 00:23:36,865 that, mutations 630 00:23:37,244 --> 00:23:37,744 happen 631 00:23:38,605 --> 00:23:39,105 gradually 632 00:23:39,404 --> 00:23:42,065 so that, you hardly ever have 633 00:23:42,859 --> 00:23:43,359 immense, 634 00:23:43,740 --> 00:23:45,840 jumps, in the phenotypic space, 635 00:23:46,380 --> 00:23:47,680 then you can still 636 00:23:48,380 --> 00:23:51,600 try and describe an evolutionary process in terms 637 00:23:51,660 --> 00:23:53,759 of what is the expected distribution 638 00:23:54,460 --> 00:23:55,964 of the phenotypic effects 639 00:23:56,444 --> 00:23:57,105 of mutations. 640 00:23:57,724 --> 00:23:59,585 And then in this way, you can 641 00:24:00,204 --> 00:24:00,704 introduce, 642 00:24:01,644 --> 00:24:02,464 new species. 643 00:24:03,085 --> 00:24:05,244 Then you you need the tools that are 644 00:24:05,244 --> 00:24:07,984 a bit different. So, they they are available, 645 00:24:08,125 --> 00:24:10,544 like, I don't know, adaptive dynamics, for instance. 646 00:24:11,059 --> 00:24:13,880 It is a method that takes into account, 647 00:24:14,819 --> 00:24:17,640 whether a new species that appears will invade 648 00:24:17,859 --> 00:24:19,640 a system or will not. 649 00:24:20,579 --> 00:24:23,000 There are methods from population genetics, 650 00:24:23,744 --> 00:24:24,884 quantitative genetics. 651 00:24:25,424 --> 00:24:25,924 So 652 00:24:26,945 --> 00:24:28,704 the the I I would say that these 653 00:24:28,704 --> 00:24:29,204 methods 654 00:24:29,825 --> 00:24:31,765 have not as yet been 655 00:24:32,144 --> 00:24:33,924 systematically applied to communities. 656 00:24:34,305 --> 00:24:35,605 So they are more often 657 00:24:36,130 --> 00:24:38,150 applied to organisms, 658 00:24:39,409 --> 00:24:40,150 which have, 659 00:24:40,529 --> 00:24:42,950 if you want to also within an organism, 660 00:24:43,009 --> 00:24:44,769 there is a sort of an ecosystem of 661 00:24:44,769 --> 00:24:45,269 genes. 662 00:24:46,289 --> 00:24:48,210 So there is a hope that they can 663 00:24:48,210 --> 00:24:50,470 be actually, they are starting to be 664 00:24:50,954 --> 00:24:51,674 now generalized, 665 00:24:52,154 --> 00:24:54,414 to account also for the evolution of communities. 666 00:24:55,595 --> 00:24:58,095 Recently, there have been studies in which, 667 00:24:58,794 --> 00:25:00,174 people try to, 668 00:25:00,714 --> 00:25:03,534 model couplings and interaction not as sequential 669 00:25:04,075 --> 00:25:07,890 variables, but also as annealed variables, so varying 670 00:25:07,890 --> 00:25:08,549 in time. 671 00:25:08,849 --> 00:25:10,529 And so this can be of interest in 672 00:25:10,529 --> 00:25:13,910 many different systems, so in aquatic systems, plantonic 673 00:25:14,049 --> 00:25:14,549 community. 674 00:25:15,490 --> 00:25:18,309 Sylvia has already discussed this kind of subject. 675 00:25:18,369 --> 00:25:21,195 But, since I have a background in spin 676 00:25:21,195 --> 00:25:23,134 glass theory and this other system, 677 00:25:23,515 --> 00:25:25,055 I would like also to 678 00:25:25,674 --> 00:25:27,875 add that this kind of techniques turn out 679 00:25:27,875 --> 00:25:28,734 to be particularly 680 00:25:29,035 --> 00:25:29,535 powerful 681 00:25:29,994 --> 00:25:30,734 for modeling 682 00:25:31,275 --> 00:25:31,775 gut, 683 00:25:32,779 --> 00:25:36,059 microbial communities. And so very recently, we have 684 00:25:36,059 --> 00:25:37,839 tried to to better understand 685 00:25:38,140 --> 00:25:39,519 the the emergent patterns 686 00:25:39,900 --> 00:25:41,039 in healthy versus 687 00:25:41,500 --> 00:25:42,000 disease 688 00:25:42,380 --> 00:25:46,079 individuals, disease communities. So for instance, community characterized 689 00:25:46,220 --> 00:25:46,694 by, 690 00:25:47,255 --> 00:25:50,394 affected by Crohn disease or ulcerative colitis. 691 00:25:50,774 --> 00:25:52,154 And so the gut microbiota 692 00:25:52,694 --> 00:25:55,734 is a typical instance, a typical example of 693 00:25:55,734 --> 00:25:57,755 system in which you can distinguish 694 00:25:58,855 --> 00:26:00,634 two different time scales. 695 00:26:01,130 --> 00:26:04,090 One, a fast time scale which is driven 696 00:26:04,090 --> 00:26:04,590 by, 697 00:26:05,529 --> 00:26:09,150 daily feeding with and circadian plots 698 00:26:09,610 --> 00:26:11,549 that set the scale over 699 00:26:11,850 --> 00:26:12,825 twenty four hours 700 00:26:14,585 --> 00:26:17,784 and slower time scales that are modulated by 701 00:26:17,784 --> 00:26:18,924 seasonal changes, 702 00:26:19,704 --> 00:26:20,845 diet variation, 703 00:26:21,384 --> 00:26:22,444 and overall 704 00:26:22,744 --> 00:26:23,964 long term stability. 705 00:26:24,585 --> 00:26:26,044 So, again, this is 706 00:26:26,359 --> 00:26:29,000 an open question in theoretical ecology. It's a 707 00:26:29,000 --> 00:26:30,539 very challenging topic, 708 00:26:31,080 --> 00:26:33,740 which we are trying to address from 709 00:26:34,119 --> 00:26:35,340 a theoretical perspective. 710 00:26:36,119 --> 00:26:37,559 And one of the most exciting areas in 711 00:26:37,559 --> 00:26:39,240 medicine as well. Sylvia, do you have some 712 00:26:39,240 --> 00:26:40,299 of that? Yeah. I agree. 713 00:26:41,204 --> 00:26:42,644 Yes. Just thinking, 714 00:26:43,684 --> 00:26:44,184 so 715 00:26:44,484 --> 00:26:45,924 that I would like to point out that 716 00:26:45,924 --> 00:26:46,585 there is 717 00:26:47,125 --> 00:26:48,585 one particularly interesting 718 00:26:48,884 --> 00:26:52,085 topic in a community evolution. That is what 719 00:26:52,085 --> 00:26:53,704 happens when you apply 720 00:26:54,369 --> 00:26:56,289 selection at multiple levels, 721 00:26:56,609 --> 00:26:58,849 at the same time. So you can consider 722 00:26:58,849 --> 00:27:01,429 for instance, in the gut, of course, bacteria 723 00:27:01,730 --> 00:27:03,669 compete against one another. 724 00:27:04,130 --> 00:27:06,390 And, there is a short timescale, 725 00:27:06,929 --> 00:27:07,829 that is associated 726 00:27:08,210 --> 00:27:11,615 with ecological and also evolutionary changes that are 727 00:27:11,615 --> 00:27:12,115 driven 728 00:27:12,575 --> 00:27:13,394 by competition. 729 00:27:14,095 --> 00:27:16,095 But then at the same time, they get 730 00:27:16,095 --> 00:27:18,974 selected because they are in our gut, so 731 00:27:18,974 --> 00:27:19,715 they provide 732 00:27:20,095 --> 00:27:22,109 a function to the host. 733 00:27:23,069 --> 00:27:24,450 So this can be reproduced 734 00:27:24,750 --> 00:27:26,849 also in the lab if you have, 735 00:27:27,630 --> 00:27:28,130 microcosps 736 00:27:28,589 --> 00:27:29,490 that contain, 737 00:27:29,950 --> 00:27:31,009 different species, 738 00:27:31,390 --> 00:27:32,529 and then you can 739 00:27:33,230 --> 00:27:34,690 score these microcosps 740 00:27:35,309 --> 00:27:36,769 according to a given 741 00:27:37,424 --> 00:27:39,845 collective feature. I don't know, total biomass. 742 00:27:41,025 --> 00:27:42,164 And this is something, 743 00:27:42,545 --> 00:27:43,285 that is, 744 00:27:43,985 --> 00:27:45,904 now that there are people who are developing 745 00:27:45,904 --> 00:27:46,644 these methods, 746 00:27:47,105 --> 00:27:49,200 in the hope of, being able 747 00:27:50,079 --> 00:27:52,899 to select communities that have this, 748 00:27:53,519 --> 00:27:54,899 some particular property 749 00:27:55,519 --> 00:27:57,700 like, they are useful for bioremediation 750 00:27:58,639 --> 00:28:00,980 or they produce a particular product. 751 00:28:02,144 --> 00:28:04,224 So what is challenging in this case is 752 00:28:04,224 --> 00:28:07,105 that, you are selecting also at the level 753 00:28:07,105 --> 00:28:08,244 of the communities. 754 00:28:08,785 --> 00:28:09,585 So there is, 755 00:28:10,464 --> 00:28:12,085 competition between communities 756 00:28:13,585 --> 00:28:16,565 for a feature that is a collective feature. 757 00:28:17,130 --> 00:28:17,789 So this, 758 00:28:18,570 --> 00:28:20,750 kind of multilevel selection 759 00:28:22,170 --> 00:28:23,070 is, really, 760 00:28:24,570 --> 00:28:27,769 very actively researched upon, in this moment, and 761 00:28:27,769 --> 00:28:28,910 mathematical models, 762 00:28:29,505 --> 00:28:31,845 including models from statistical physics 763 00:28:32,305 --> 00:28:34,244 are being used to try and understand 764 00:28:34,625 --> 00:28:35,684 under what conditions 765 00:28:36,144 --> 00:28:36,644 can 766 00:28:37,105 --> 00:28:37,924 this evolution 767 00:28:38,305 --> 00:28:39,525 be most efficient? 768 00:28:39,825 --> 00:28:40,884 When does it fail 769 00:28:41,184 --> 00:28:41,585 and, 770 00:28:42,065 --> 00:28:43,285 how to actually 771 00:28:43,759 --> 00:28:44,899 obtain communities, 772 00:28:45,679 --> 00:28:46,419 that provide, 773 00:28:46,799 --> 00:28:47,940 a specific function. 774 00:28:48,559 --> 00:28:49,679 And this would presumably 775 00:28:50,079 --> 00:28:52,740 potentially in the future help someone say, okay. 776 00:28:52,799 --> 00:28:54,659 So this person has symptoms 777 00:28:54,960 --> 00:28:58,319 of irritable bowel syndrome, IBS, Crohn's disease, or 778 00:28:58,319 --> 00:28:59,379 something like that. 779 00:28:59,734 --> 00:29:00,875 And we've seen 780 00:29:01,575 --> 00:29:04,215 these conditions in their guts, and we think 781 00:29:04,215 --> 00:29:06,535 that changing the system in some way would 782 00:29:06,535 --> 00:29:08,934 help the system the ecosystem in their gut 783 00:29:08,934 --> 00:29:11,015 evolve into something that was not the disease 784 00:29:11,015 --> 00:29:14,480 state. Yeah. Yes. Or you might be able 785 00:29:14,480 --> 00:29:17,200 at a certain point to swallow a pill 786 00:29:17,200 --> 00:29:20,000 that has been that contains a community that 787 00:29:20,000 --> 00:29:20,740 was evolved 788 00:29:21,359 --> 00:29:22,480 in order to, 789 00:29:22,960 --> 00:29:23,440 help, 790 00:29:23,759 --> 00:29:25,059 the gut reestablish 791 00:29:26,005 --> 00:29:27,224 its original state. 792 00:29:28,005 --> 00:29:30,724 Yeah. For instance, you can think of performing 793 00:29:30,724 --> 00:29:34,005 a fecal microbiota transplant from a healthy donor 794 00:29:34,005 --> 00:29:35,304 to an healthy recipient 795 00:29:35,765 --> 00:29:37,304 and to study the evolution 796 00:29:37,765 --> 00:29:38,664 of the community 797 00:29:39,849 --> 00:29:41,630 over several months. 798 00:29:42,009 --> 00:29:43,769 In that new environment that has been placed 799 00:29:43,769 --> 00:29:45,369 in as opposed to where it came from. 800 00:29:45,369 --> 00:29:47,929 Yeah. And I will also add that our 801 00:29:47,929 --> 00:29:49,149 gut is a very 802 00:29:49,529 --> 00:29:50,750 structured ecosystem. 803 00:29:51,369 --> 00:29:52,829 So it's it's important 804 00:29:53,450 --> 00:29:54,029 to take 805 00:29:54,490 --> 00:29:54,990 structure 806 00:29:55,505 --> 00:29:58,384 into account in this kind of model. For 807 00:29:58,384 --> 00:30:01,105 instance, by using a meta community scenario in 808 00:30:01,105 --> 00:30:03,285 which different patches are connected 809 00:30:03,985 --> 00:30:05,205 with with the others. 810 00:30:06,305 --> 00:30:08,945 So was it strange for you, Ada, to 811 00:30:08,945 --> 00:30:11,559 go from thinking about spin glasses and the 812 00:30:11,559 --> 00:30:13,980 physics of that to thinking about the statistical 813 00:30:14,119 --> 00:30:15,659 physics of gut microbiomes? 814 00:30:16,599 --> 00:30:17,980 Yeah. It might appear 815 00:30:18,359 --> 00:30:21,480 a quite exotic topic. I have a background 816 00:30:21,480 --> 00:30:25,159 in statistical physics of disorders system, field theory, 817 00:30:25,159 --> 00:30:28,134 and so on. So very recently, in collaboration 818 00:30:28,275 --> 00:30:29,575 with the team of gastroenterologists 819 00:30:30,755 --> 00:30:32,054 in Padua in Italy, 820 00:30:32,515 --> 00:30:34,934 we try to connect disorder systems 821 00:30:35,394 --> 00:30:37,015 akin to spin glasses 822 00:30:37,394 --> 00:30:39,654 that are still in their infancy 823 00:30:40,470 --> 00:30:41,450 with metagenomic 824 00:30:41,829 --> 00:30:45,210 data from healthy versus unhealthy dataset 825 00:30:45,589 --> 00:30:48,950 where for unhealthy immune patients that are affected 826 00:30:48,950 --> 00:30:51,769 by Crohn disease and ulcerative colitis. 827 00:30:52,304 --> 00:30:54,784 So the the kind of strategies that we 828 00:30:54,784 --> 00:30:57,284 perform, that we apply, is basically 829 00:30:57,585 --> 00:30:59,204 based on the following approach. 830 00:30:59,585 --> 00:31:02,164 So we propose some order parameters, 831 00:31:02,944 --> 00:31:05,125 mean abundance and high order correlations 832 00:31:05,585 --> 00:31:08,210 between species abundance that are typically 833 00:31:08,589 --> 00:31:11,890 referred to as overlap, self overlap, and intrastate 834 00:31:12,029 --> 00:31:13,569 overlap in spin glass theory. 835 00:31:13,869 --> 00:31:16,589 We measure this quantity from data for the 836 00:31:16,589 --> 00:31:17,490 two cohorts, 837 00:31:18,029 --> 00:31:20,450 for the two data set, healthy versus unhealthy. 838 00:31:20,924 --> 00:31:23,485 And then in a high dimensional setting, so 839 00:31:23,485 --> 00:31:24,384 based on 840 00:31:24,765 --> 00:31:27,505 the disorder generalized loca paltura model, 841 00:31:27,805 --> 00:31:30,545 we infer a limited set of parameters. 842 00:31:31,005 --> 00:31:33,825 So mean and variance of the interaction matrix, 843 00:31:34,440 --> 00:31:37,579 demographic noise amplitude, and an immigration parameter, 844 00:31:38,200 --> 00:31:39,179 which is crucial 845 00:31:39,720 --> 00:31:43,000 to prevent one species from getting extinct in 846 00:31:43,000 --> 00:31:45,159 in this kind of setting. And so by 847 00:31:45,159 --> 00:31:48,039 inferring this kind of quantity, we could actually 848 00:31:48,039 --> 00:31:48,539 decode 849 00:31:49,184 --> 00:31:50,404 microbiome data, 850 00:31:50,784 --> 00:31:52,884 which offer insight into the ecological 851 00:31:53,264 --> 00:31:54,644 forces that shapes 852 00:31:55,024 --> 00:31:55,524 macroecological 853 00:31:56,065 --> 00:31:58,964 states. And indeed, we managed to classify, 854 00:32:00,144 --> 00:32:02,304 the two data set, the two cohorts for 855 00:32:02,304 --> 00:32:05,920 healthy versus unhealthy, and we could actually have 856 00:32:06,299 --> 00:32:07,359 visible clusterization 857 00:32:07,980 --> 00:32:08,880 of the two. 858 00:32:09,259 --> 00:32:10,160 One characterized 859 00:32:11,019 --> 00:32:11,599 by higher 860 00:32:12,220 --> 00:32:12,720 heterogeneity 861 00:32:13,660 --> 00:32:15,599 and higher demographic fluctuations 862 00:32:16,255 --> 00:32:18,734 in such a way to to interpret this 863 00:32:18,734 --> 00:32:20,515 kind of fundings as, 864 00:32:21,214 --> 00:32:23,714 being more stable with respect to external 865 00:32:24,015 --> 00:32:24,515 perturbation, 866 00:32:25,454 --> 00:32:25,954 antibiotic 867 00:32:26,255 --> 00:32:26,755 treatment, 868 00:32:27,534 --> 00:32:28,034 changes 869 00:32:28,335 --> 00:32:29,234 in the diet, 870 00:32:29,619 --> 00:32:30,919 and another characterized 871 00:32:31,220 --> 00:32:32,200 by smaller 872 00:32:32,579 --> 00:32:33,079 heterogeneity 873 00:32:33,460 --> 00:32:36,679 and smaller demographic noise. And so this could 874 00:32:37,059 --> 00:32:38,039 open the room 875 00:32:38,819 --> 00:32:40,200 for targeted strategies 876 00:32:40,819 --> 00:32:42,279 in microbial dynamics, 877 00:32:42,755 --> 00:32:44,855 also for for better investigating 878 00:32:45,235 --> 00:32:48,434 the appearance of multiple alternative stable states in 879 00:32:48,434 --> 00:32:51,174 this kind of community. Because indeed, an healthy 880 00:32:51,394 --> 00:32:53,495 dataset seems to be more 881 00:32:53,955 --> 00:32:54,455 captured 882 00:32:55,099 --> 00:32:57,340 and seems to get closer and closer to 883 00:32:57,340 --> 00:33:00,160 the stability line of the single equilibrium regime. 884 00:33:00,299 --> 00:33:02,940 So there is still room for discussion, and 885 00:33:02,940 --> 00:33:05,180 we would like to work on this kind 886 00:33:05,180 --> 00:33:08,299 of topics in the next few months and 887 00:33:08,299 --> 00:33:09,194 and few years. 888 00:33:10,474 --> 00:33:12,794 At the end of your EPL paper, you 889 00:33:12,794 --> 00:33:15,274 expressed some hope that statistical physics can bring 890 00:33:15,274 --> 00:33:18,654 ideas not only for officially describing currently available 891 00:33:18,714 --> 00:33:19,214 observations, 892 00:33:19,914 --> 00:33:22,255 but also for planning future ones. 893 00:33:22,769 --> 00:33:25,490 How might your ecologist or biologist or even 894 00:33:25,490 --> 00:33:26,390 medical colleagues 895 00:33:26,930 --> 00:33:28,869 use the finding of statistical physics 896 00:33:29,170 --> 00:33:30,950 to plan future observations? 897 00:33:32,609 --> 00:33:35,670 Yeah. Our hope is that statistical physics approach 898 00:33:36,205 --> 00:33:37,744 and intuition from 899 00:33:38,125 --> 00:33:40,144 disorder system cannot prioritize 900 00:33:40,845 --> 00:33:44,545 observation that are most informative about underlying 901 00:33:44,845 --> 00:33:46,144 ecological mechanisms. 902 00:33:46,605 --> 00:33:49,965 We mentioned Crohn disease and ulcerative colitis. So 903 00:33:49,965 --> 00:33:50,465 can 904 00:33:50,940 --> 00:33:53,759 statistical physics can definitely help in 905 00:33:54,380 --> 00:33:54,880 providing 906 00:33:55,500 --> 00:33:59,740 quantitative fingerprints, quantitative order parameters for distinguished different 907 00:33:59,740 --> 00:34:02,079 regimes. So I will say, first of all, 908 00:34:02,299 --> 00:34:03,839 can be very efficient 909 00:34:04,214 --> 00:34:06,714 in detecting phase transition and criticality, 910 00:34:07,255 --> 00:34:08,155 and ecologists 911 00:34:08,534 --> 00:34:11,835 could plan experiments along environmental 912 00:34:12,135 --> 00:34:12,635 gradients 913 00:34:13,014 --> 00:34:15,275 or under control perturbations, 914 00:34:16,295 --> 00:34:19,355 like increasing variability in species abundance, 915 00:34:20,190 --> 00:34:21,409 tuning, pH 916 00:34:21,789 --> 00:34:22,289 level, 917 00:34:22,670 --> 00:34:25,650 or nutrient amount in a well controlled 918 00:34:26,190 --> 00:34:29,409 chemical condition, for instance, in a chemostat reactor. 919 00:34:30,109 --> 00:34:31,730 Second, probing multistability 920 00:34:33,309 --> 00:34:34,369 with replicated 921 00:34:34,670 --> 00:34:35,170 samples. 922 00:34:35,815 --> 00:34:39,275 So we mentioned several times single temporal snapshot 923 00:34:39,574 --> 00:34:40,074 versus 924 00:34:40,454 --> 00:34:40,954 longitudinal 925 00:34:41,335 --> 00:34:42,954 data, long time series, 926 00:34:43,414 --> 00:34:44,235 and also 927 00:34:45,094 --> 00:34:47,275 trying to reduce the dimensionality 928 00:34:47,815 --> 00:34:48,635 of the model 929 00:34:48,940 --> 00:34:50,239 and to have information 930 00:34:50,699 --> 00:34:51,440 on the community 931 00:34:52,140 --> 00:34:53,280 just based on 932 00:34:53,579 --> 00:34:54,800 a few parameters. 933 00:34:55,739 --> 00:34:56,239 So 934 00:34:57,019 --> 00:34:59,519 I will say that these are three main 935 00:34:59,579 --> 00:35:00,079 directions. 936 00:35:00,539 --> 00:35:02,855 So this can open the road to have 937 00:35:02,855 --> 00:35:05,275 controlled strategies in many different 938 00:35:05,574 --> 00:35:07,034 ecological and biological 939 00:35:07,335 --> 00:35:07,835 systems. 940 00:35:09,014 --> 00:35:10,954 I think that, yeah, the main 941 00:35:11,574 --> 00:35:12,074 goal 942 00:35:12,454 --> 00:35:14,954 of our work is to be able to 943 00:35:15,494 --> 00:35:16,635 propose to ecologists 944 00:35:17,174 --> 00:35:17,674 some 945 00:35:18,710 --> 00:35:20,809 order parameters or some statistical, 946 00:35:22,309 --> 00:35:22,789 means, 947 00:35:23,269 --> 00:35:24,089 of distinguishing, 948 00:35:24,789 --> 00:35:25,690 different processes. 949 00:35:26,630 --> 00:35:29,449 So I wouldn't say that so far, 950 00:35:30,385 --> 00:35:30,885 ecologists 951 00:35:31,505 --> 00:35:34,385 would come to us and ask, okay. What 952 00:35:34,385 --> 00:35:34,885 is 953 00:35:35,184 --> 00:35:37,204 the other parameter I should look at? 954 00:35:37,985 --> 00:35:40,305 But I think that there have been, so 955 00:35:40,305 --> 00:35:42,885 many progresses in the last few years, 956 00:35:43,265 --> 00:35:46,719 that the news are now starting to percolate 957 00:35:46,940 --> 00:35:47,440 also 958 00:35:48,139 --> 00:35:49,039 in the ecological 959 00:35:49,340 --> 00:35:49,840 community. 960 00:35:50,539 --> 00:35:51,920 Though it is challenging, 961 00:35:52,619 --> 00:35:54,639 to communicate across disciplines 962 00:35:55,340 --> 00:35:57,420 and to be able to reach out to 963 00:35:57,420 --> 00:35:59,119 the people who actually plan 964 00:35:59,715 --> 00:36:01,095 the observational campaigns, 965 00:36:01,875 --> 00:36:04,135 which are often planned on considerations 966 00:36:04,434 --> 00:36:06,454 that are different, from, those, 967 00:36:07,315 --> 00:36:07,815 theorists 968 00:36:08,275 --> 00:36:10,914 for good reasons. I am not saying they 969 00:36:10,914 --> 00:36:13,335 shouldn't, but we we will, anyway, 970 00:36:14,090 --> 00:36:15,929 get more and more data because this is 971 00:36:15,929 --> 00:36:19,130 the trend. There's more and more observational data 972 00:36:19,130 --> 00:36:22,190 that will be available and that are 973 00:36:22,890 --> 00:36:23,690 more and more, 974 00:36:24,570 --> 00:36:26,269 available in public repositories. 975 00:36:27,210 --> 00:36:27,855 So this, 976 00:36:29,054 --> 00:36:30,994 will make our work easier. 977 00:36:31,534 --> 00:36:32,914 What are some of the challenges 978 00:36:33,215 --> 00:36:36,094 of connecting with the data that's gathered by 979 00:36:36,094 --> 00:36:38,914 observational scientists for you as a theoretical ecologist? 980 00:36:40,094 --> 00:36:41,394 Yeah. So there are several 981 00:36:41,775 --> 00:36:42,275 challenges. 982 00:36:43,380 --> 00:36:45,319 First of all, there are differences, 983 00:36:46,019 --> 00:36:47,000 in perspective, 984 00:36:47,539 --> 00:36:49,400 even over the same data, 985 00:36:50,019 --> 00:36:51,880 from people coming from physics 986 00:36:52,179 --> 00:36:53,779 and people coming more from, 987 00:36:54,659 --> 00:36:55,880 experimental ecology. 988 00:36:57,174 --> 00:36:58,074 So there is 989 00:36:58,454 --> 00:36:59,194 a trend, 990 00:36:59,734 --> 00:37:00,234 in 991 00:37:00,614 --> 00:37:03,414 the recent decades that has been spurred by 992 00:37:03,414 --> 00:37:03,994 the availability 993 00:37:04,295 --> 00:37:05,034 of methods 994 00:37:05,815 --> 00:37:08,554 of sequencing that are more and more sophisticated 995 00:37:08,934 --> 00:37:10,554 and increasingly cheaper 996 00:37:11,690 --> 00:37:12,829 to actually collect 997 00:37:13,130 --> 00:37:15,529 a lot a lot of data and then 998 00:37:15,529 --> 00:37:16,029 proceed 999 00:37:16,329 --> 00:37:18,109 with statistical approaches, 1000 00:37:18,889 --> 00:37:21,389 mining this data and trying to find out 1001 00:37:21,690 --> 00:37:22,429 what are 1002 00:37:22,969 --> 00:37:23,630 the signals 1003 00:37:24,335 --> 00:37:26,355 of, I don't know, variation, 1004 00:37:26,894 --> 00:37:27,394 from 1005 00:37:28,014 --> 00:37:29,315 one place to another 1006 00:37:29,694 --> 00:37:30,514 of ecosystems. 1007 00:37:31,614 --> 00:37:34,734 Our point of view is typically to start 1008 00:37:34,734 --> 00:37:35,875 from a hypothesis, 1009 00:37:36,734 --> 00:37:39,099 and this is also true for a lot 1010 00:37:39,099 --> 00:37:40,559 of theoretical biology. 1011 00:37:41,420 --> 00:37:44,300 You start from hypothesis and then you would 1012 00:37:44,300 --> 00:37:46,860 like data to be gathered to support or 1013 00:37:46,860 --> 00:37:47,760 not this hypothesis. 1014 00:37:48,860 --> 00:37:50,880 And, the two things are complimentary, 1015 00:37:51,659 --> 00:37:54,934 but, sometimes there is like a language barrier 1016 00:37:55,234 --> 00:37:58,694 and the the perspective barrier that makes it 1017 00:37:59,234 --> 00:38:00,054 not completely 1018 00:38:01,394 --> 00:38:02,755 easy for us, 1019 00:38:03,074 --> 00:38:03,574 to 1020 00:38:04,114 --> 00:38:06,054 speak to an ecological 1021 00:38:06,594 --> 00:38:07,094 audience. 1022 00:38:07,949 --> 00:38:10,530 And this part of this, is that 1023 00:38:11,230 --> 00:38:11,730 ecology 1024 00:38:12,030 --> 00:38:13,329 has a long history, 1025 00:38:14,190 --> 00:38:16,989 and there are, schools of thought that are 1026 00:38:16,989 --> 00:38:18,289 rooted in this history. 1027 00:38:18,829 --> 00:38:20,449 And we do not necessarily 1028 00:38:21,309 --> 00:38:21,809 know 1029 00:38:22,164 --> 00:38:24,264 all of this, coming from physics, 1030 00:38:24,804 --> 00:38:27,125 even though I personally have been, in a 1031 00:38:27,125 --> 00:38:29,864 college and evolution of almost forever now. 1032 00:38:30,244 --> 00:38:32,484 But still, I feel like I am a 1033 00:38:32,484 --> 00:38:32,984 physicist, 1034 00:38:33,364 --> 00:38:33,864 and 1035 00:38:34,565 --> 00:38:37,519 the perspective I bring on the data is 1036 00:38:37,519 --> 00:38:38,739 more that of a physicist 1037 00:38:39,119 --> 00:38:41,139 than, of an ecologist. 1038 00:38:42,000 --> 00:38:44,239 I suppose it takes all perspective to see 1039 00:38:44,239 --> 00:38:47,119 the entire problem and to solve it. Hopefully 1040 00:38:47,119 --> 00:38:49,119 so. That's the effort that we are trying 1041 00:38:49,119 --> 00:38:51,460 to do with, ADA also engaging, 1042 00:38:52,000 --> 00:38:53,175 with medical doctors. 1043 00:38:54,295 --> 00:38:55,755 Doctors. Yeah. That's the hope. 1044 00:38:56,855 --> 00:38:59,015 Well, Sylvia and Ada, thank you very much 1045 00:38:59,015 --> 00:39:00,855 for coming on the podcast. It's been wonderful 1046 00:39:00,855 --> 00:39:01,835 speaking with you. 1047 00:39:02,135 --> 00:39:04,795 Thank you very much for having us, and, 1048 00:39:05,414 --> 00:39:06,315 all the best. 1049 00:39:14,680 --> 00:39:17,180 That was Ada Altieri and Silvia de Monte 1050 00:39:17,400 --> 00:39:20,619 talking about statistical physics approaches to ecological communities. 1051 00:39:20,945 --> 00:39:22,304 If you'd like to know more about this 1052 00:39:22,304 --> 00:39:24,625 work, check out their paper in EPL, which 1053 00:39:24,625 --> 00:39:27,025 is open access and freely available via the 1054 00:39:27,025 --> 00:39:27,525 IOPscience 1055 00:39:27,824 --> 00:39:28,324 website. 1056 00:39:29,824 --> 00:39:31,824 My thanks to Ade and Sylvia, to our 1057 00:39:31,824 --> 00:39:34,699 producer Fred Iles, and to the journal EPL, 1058 00:39:34,760 --> 00:39:37,079 which sponsored this episode of the Physics World 1059 00:39:37,079 --> 00:39:37,900 weekly podcast. 1060 00:39:38,840 --> 00:39:41,019 Join us again next week for more fascinating 1061 00:39:41,159 --> 00:39:42,699 stories from the world of physics.